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AI Catalog Enrichment Services

Build catalog records that every destination can trace back to the source.

We'll reply with a written proposal: the work, what we need from you and an estimated timeline.

The hidden problems

The costly AI catalog enrichment problems hide below the work you can see.

Before we suggest filling in data for more products, we check your source systems, how products are identified, who owns each field, each destination's rules, problem cases and how much review your team can handle.

  • Thousands of products are missing details

    Large catalogs often have missing details along with the same product listed more than once, broken links between products and their versions, out-of-date source data and gaps that only matter for certain destinations.

  • Filters don't help shoppers

    Filters only help when product details are complete, consistent and match what shoppers are actually looking for.

  • Feed data is thin

    Shopping, search, recommendation and AI systems each need records made for them. Even then, data you send can still be changed, delayed or rejected before it becomes a live product listing.

  • Nobody sets the rules

    New products and changes to your sources keep repeating the same mistakes unless it is written down who owns how products are identified, which source wins for each field, checks on incoming data, problem cases, go-live, monitoring and undoing changes.

  • Nobody controls what each product detail means or is for

    Before changing or adding to any records, define each master field's meaning, type, category, language and country version, which sales channels it applies to, which source comes first and where it will be used.

  • The proof behind each value, and how sure we are, is an afterthought

    Keep a record of which source, change step, AI model, reviewer and batch produced each value, so conflicts, things that break again and destination errors can be traced and undone.

Scope

What our AI catalog enrichment service includes.

Catalog enrichment means filling in and fixing the details in your product catalog so every product is complete and accurate. Softscotch helps online stores combine approved product data from named systems into reliable catalog records for your store, site search, product feeds and the AI shopping tools that support them. We define how each product, product group, version (variant) and bundle is identified; check incoming records; fix duplicates and conflicts; apply approved rules for each field; check what each destination actually shows; and release changes in stages that can be undone. We never make up missing product codes or product claims, we don't replace all your product systems by default and we can't promise that any destination will approve your products or that sales will rise.

The first eight cards are part of this service, ending with training and handover to your team. The last two are related services, shown here so it is clear who does what and nothing overlaps without anyone noticing.

Checking your catalog

We review your source systems, how products and versions are identified, how fields are set up, categories, links between products, language and country versions, sales channels, site search, feeds, data quality and who runs what day to day.

Rules for product details

We define the official product types, which fields are required, optional or not allowed, units, allowed values, which source wins, how fresh data must be, and rules for each language, country and sales channel, by category and use.

How data gets added and checked

We create a step-by-step process, saved in numbered versions, to bring data in, check it, match it up, add details, review, prepare, go live and undo if needed, with ways to set aside problem records, try again and re-run safely.

Matching data to feeds and filters

We map your master fields separately into filters, site search, product cards, product feeds, marketplaces, recommendations and AI shopping tools you are eligible for, then check what each one actually shows.

Quality checks

We check how products are identified, duplicates, how complete, correct, consistent and up to date the data is, category structure, links between products, whether each destination is ready and problems with live product listings.

Defining each product detail and its purpose

This work includes defining each product detail and its purpose. We set each field's meaning, type, whether it belongs to the whole product or each version, which categories it applies to, language and country version, sales channels, which source comes first and where it is used later.

Proof and confidence for each value

This work includes keeping proof and confidence for each value. For every important value we add, we keep the main source, how it was worked out or changed, the reviewer, the batch, the time and how sure we are or whether there is a conflict.

Training and handover to your catalog team

We show your team how to add new products, review flagged records, approve changes and undo a release, with simple written steps. That way your catalog stays accurate after our work ends, without relying on us for every update.

Why Softscotch

We connect your source records to data every destination can use.

The work links how products are identified, rules for each field, imports, problem cases, checks before go-live and later reporting, instead of stopping at a one-off spreadsheet that has been filled in.

We agree the starting point before doing more

We write down where your AI catalog enrichment stands today, its limits and the meaning of each term, so the plan starts from evidence.

Expert inputs stay connected to the actual work

Before adding any details, we define how products are identified, who owns each field and each destination's rules. The same plan carries each record through checks, preparation, matching what was sent with what went live, monitoring and undoing changes if needed.

Safeguards and failure plans are designed early

Permissions, platform rules, bad data, duplicate actions and undoing changes are dealt with before they turn into problems on your live store.

Platform automation stays visible

Any automatic expansion, AI generation or optimization comes with written inputs, exclusions, quality checks and a clear point where a person must review.

Quality is measured beyond the surface number

Reports include contribution margin (profit after direct costs), how easily shoppers find products and the effect further down the line on your business, where the data allows.

The system keeps working after we hand it over

Owners, definitions, source records, notes on each change, re-checks and triggers for updating everything stay usable after launch.

The full working scope

The decisions behind a product catalog you can depend on.

These 36 factors cover your data sources, how products and their versions are identified, what each field means, imports, conflicts, review, how destinations process the data, monitoring and upkeep.

Strategy, demand and starting point
Defining each product detail and its purposeDecide which product details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.
Rules for product detailsDefine the official product types, required and optional fields, units, which source comes first, how fresh data must be, and language, country and sales channel rules, by category and use.
Filters don't help shoppersFilters only help when product details are complete, consistent and match what shoppers are actually looking for.
How shoppers find productsReview search terms, the paths shoppers take to find products and what your merchandising team needs, then keep a saved, numbered list of searches to re-test after every change.
Connecting details to filtersConnect only complete, valid fields that matter to customers to filters and category pages, while keeping "unknown" and "not relevant" values clearly marked.
Search checkTest your most important searches, the products they should show, any limits and what happens when a search finds nothing, against a named version of your catalog.
Data, signals and inputs
Proof and confidence for each valueKeep the main source, how the value was changed, the reviewer, batch, time and how sure we are or whether there is a conflict, so every important value can be traced and undone.
Matching data to feeds and filtersMap master fields for each destination, then compare what was sent with the final live product records, any issues and whether products are eligible, where the platform shows this.
Thousands of products are missing detailsCheck missing details together with duplicate products, versions with no parent product, out-of-date time stamps, missing language or country versions and what each destination requires.
Feed data is thinShopping, search, recommendation and AI destinations each need their own approved fields, grouping, update methods and rule checks, rather than one general feed for everything.
Structure and setup
Rules for product versionsSet out how product groups, products, versions, bundles and replacement products are identified, so shared facts pass down safely and facts that set versions apart don't spill over onto the wrong SKU (the code for each exact item).
Catalog and page structureOrganize products, versions, bundles, accessories, substitutes, items that work together, documents and backup options for each destination, without creating duplicate records.
How the whole system fits togetherConnect agreements with each data source, checks on data structure, matching of product records, adding details, waiting lists for problems, destinations and monitoring into one system that is easy to maintain.
Map of connected systemsShow every system the work depends on: your ERP (business system for stock, orders and finance), suppliers, PIM (product information system), DAM (image and file library), store platform, feeds, search and marketplaces, including logins, usage limits, deletions and who looks after each one.
Making the changes
Checking your catalogReview source systems, how products are identified, how fields are set up, which source comes first, categories, links between products, language and country versions, sales channels, data quality, destinations and who runs what day to day.
How data gets added and checkedBring in, check, set aside, match up, add details to, review, prepare, go live with and monitor numbered batches of data, with safe ways to try again, re-run and undo that never create the same change twice.
Product groups to start withChoose typical groups of products and versions based on business value, data risk, what each destination needs, how often they change and how much your reviewers can handle.
Rounds of adding detailsCompare the changes for a test group and approve them, save them through the master and destination mappings, check what each destination shows, then expand or undo according to agreed rules.
Safety checks
Checking against approved word listsLimit data types, values, units, how many values a field can have, category, language and country version and sales channels, with clear "unknown", "not relevant" and "conflict" options.
Quality checksFor each group of products, check how products are identified and whether data is complete, correct, consistent, specific and up to date, plus links between products, whether each destination is ready and problems with live listings.
Feed, rules and app checksBefore launch, check where each field's data comes from, rights to source material, that data matches in every destination, permissions, platform rules, details that change often like price and stock, deletions, what happens when something fails and how to recover.
Pre-launch checklistTest record counts from each source, data structure errors, setting problem records aside, safe retries, usage limits, review waiting lists, what destinations show, undoing changes and who gets alerts.
Testing and improving
Testing against a checked sampleMeasure the quality of added details and anything in the catalog that breaks again, using saved, numbered samples, as your source data structure, products, rules, AI models and destinations change.
Merchandising testsTest groups of products with improved data against similar products, and look at how shoppers find products, profit margin, returns and customer quality, rather than total sales alone.
List of fair testsRank ideas that can be tested and change one clear thing at a time where the evidence allows.
Comparing groups of productsCompare meaningful groups and time periods instead of relying on one blended average.
Measuring results and value
Profit and customer group reportingLook at contribution margin (profit after direct costs), stock levels, the value of new customers and repeat buying alongside total revenue.
Early signs and final resultsKeep the amount of data brought in, its quality and freshness, review workload and the health of live product listings separate from how shoppers find products, sales and money results.
Sales adjusted for qualityTake into account whether orders were valid, the right kind of customer, profitable, returned or accepted further down the line.
True cost of the workInclude the effort spent bringing data in, changing it, running AI models, reviewing, sending to destinations, monitoring and handling problems when we report on costs and value.
Limits of what we can proveState clearly what the data can actually see, what is only assumed and what would need proper testing to show cause and effect.
Upkeep, rules and growth
When a person must reviewSend conflicts over which product is which, uncertain values, high-risk facts and new kinds of data structure to qualified reviewers, and keep their corrections and the source evidence.
Clear rules and ownersNew products keep repeating mistakes unless there are clear rules and owners for agreements with each source, field ownership, versions, response times, how long data is kept, problem cases, alerts and undoing changes.
Managing catalog changesGive an owner and re-checks to every change in source formats, how products are identified, categories, rules for adding details, destinations, promotions, policies and AI models.
Named owners for each jobMake named people responsible for inputs, approvals, changes going live, monitoring and handling unusual cases.
History of versions and decisionsKeep the versions of what came in and what went out, batch counts, changes made, approvals, results in each destination, any undo steps and the evidence behind each decision.
Research and implementation tools

Catalog tools help us decide. They don't decide for us.

The tools we use change with your platform and business, but every tool must give us a useful input, a way to check the setup or a real result, not just another score nobody can explain.

Look under the hood

Seven checks show what must work before catalog data is rolled out widely.

Open each tab to see the proof, safeguards and sign-off checks behind each deliverable. Every finding becomes a task with an owner, or a clear decision not to act.

Check 01 · Checking your catalog

Checking your catalog

We review your product fields, category structure, search data, filters, feeds, merchandising rules and missing values.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Catalog check scopeReview product fields, category structure, search data, filters, feeds, merchandising rules and missing values.
How shoppers find products startReview search terms, the paths shoppers take to find products and what your merchandising team needs.
Connecting details to filters inputConnect product details to filters and category pages that are actually useful.
Search check structureReview improvements to site search and to how shoppers find products.
Proof and confidence go liveKeep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.
Feeds and filters controlMap improved fields into filters, site search, product cards, shopping feeds, recommendations and AI shopping tools.
Check 02 · Rules for product details

Rules for product details

We define the required and optional product details for each category, what buyers want, the type of product and how shoppers find it.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Detail rules scopeDefine the required and optional product details for each category, what buyers want, the type of product and how shoppers find it.
Missing details inputLarge catalogs often lack uses, product details, materials, compatibility or the words buyers actually search with.
Thin feed data structureShopping, search, recommendation and AI systems need better data than basic SKU (product code) fields.
Rules for product versions go liveKeep facts about the whole product separate from facts about each version, so color, size, material and compatibility don't spill over onto the wrong SKU (the code for each exact item).
Catalog and page structure controlOrganize collections, products, versions, bundles, menus and reusable content without creating duplicate paths to the same thing.
How the system fits together testConnect the inputs, parts, decisions and outputs into one system that is easy to maintain.
Check 03 · How data gets added and checked

How data gets added and checked

We create a process for adding product details, with source fields, rules for checking data and review steps.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Process for adding data scopeCreate a process for adding product details, with source fields, rules for checking data and review steps.
Map of connected systems structureShow the systems that feed into this work, or rely on it, that could block it or skew the results.
Catalog check go liveReview product fields, category structure, search data, filters, feeds, merchandising rules and missing values.
Checks and review steps controlCreate a process for adding product details, with source fields, rules for checking data and review steps.
Groups to start with testChoose the product groups for the first round of adding details.
First round of details measureFill in product details for the products that matter most.
Check 04 · Matching data to feeds and filters

Matching data to feeds and filters

We map improved fields into filters, site search, product cards, shopping feeds, recommendations and AI shopping tools.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Feed and filter map scopeMap improved fields into filters, site search, product cards, shopping feeds, recommendations and AI shopping tools.
Approved word lists go liveLimit results to approved values, units, formats and category rules, with a clear "unknown" option.
Quality checks controlCheck for duplicates, missing values, labels that don't match, poor category structure and weak product groupings.
Feed, rules and app checks testBefore launch, check that data matches everywhere, app permissions, tax, shipping, returns, accessibility and what happens when something fails.
Pre-launch checklist measureTest inputs, permissions, platform rules, content, tracking and what happens when something fails, before any change goes live.
Testing against a checked sample ownerMeasure how many values are right, how many are missed, how often reviewers disagree and how useful the details are, on a reviewed sample, as your catalog changes.
Check 05 · Quality checks

Quality checks

We check for duplicates, missing values, labels that don't match, poor category structure and weak product groupings.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Data quality scopeCheck for duplicates, missing values, labels that don't match, poor category structure and weak product groupings.
Merchandising tests controlTest changes to how products are found, proof, offers and checkout against profit margin, returns and customer quality, not just sales numbers.
List of fair tests testRank ideas that can be tested and change one clear thing at a time where the evidence allows.
Comparing groups of products measureCompare meaningful groups and time periods instead of relying on one blended average.
Profit and customer group reporting ownerLook at contribution margin (profit after direct costs), stock levels, the value of new customers and repeat buying alongside total revenue.
Early signs and final results startKeep early signs that the work is on track separate from real, confirmed business results.
Check 06 · Defining each product detail

Defining each product detail and its purpose

This work includes defining each product detail and its purpose. We decide which details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Each detail's meaning and purpose scopeThis work includes defining each product detail and its purpose. Decide which details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.
Sales adjusted for quality testTake into account whether orders were valid, the right kind of customer, profitable, returned or accepted further down the line.
True cost of the work measureInclude content production, platform, tool and day-to-day running effort when we report on costs and value.
Limits of what we can prove ownerState clearly what the data can actually see, what is only assumed and what would need proper testing to show cause and effect.
When a person must review startSend uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them.
Clear rules and owners inputNew products keep creating the same data problems unless the rules, and who owns them, are written down.
Check 07 · Proof and confidence

Proof and confidence for each value

This work includes keeping proof and confidence for each value. We keep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.

The result records the evidence, how important it is, the owner, how we'll check it works and the next decision, instead of ending with just an observation.

Proof for every value scopeThis work includes keeping proof and confidence for each value. Keep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.
Managing catalog changes measureGive an owner and re-checks to every change in your store theme, apps, products, feeds, promotions and policies.
Named owners for each job ownerMake named people responsible for inputs, approvals, changes going live, monitoring and handling unusual cases.
History of versions and decisions startKeep a record of what changed, why, when, who changed it and what evidence was used.
Each detail's purpose inputDecide which product details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.
Rules for product details structureDefine the required and optional product details for each category, what buyers want, the type of product and how shoppers find it.
What changes

From a scattered catalog to a well-run system with clear rules.

Before

Work on your catalog is happening, but its inputs, safeguards and business decisions are scattered and disconnected.

  • Thousands of products are missing detailsLarge catalogs often lack uses, product details, materials, compatibility or the words buyers actually search with.
  • Filters don't help shoppersFilters only help when product details are complete, consistent and match what shoppers are actually looking for.
  • Feed data is thinShopping, search, recommendation and AI systems need better data than basic SKU (product code) fields.
After the first round of work

The most important changes are live, the key safeguards have been checked and reports use definitions everyone agreed on.

  • Checking your catalogReview product fields, category structure, search data, filters, feeds, merchandising rules and missing values.
  • Rules for product detailsDefine the required and optional product details for each category, what buyers want, the type of product and how shoppers find it.
  • How data gets added and checkedCreate a process for adding product details, with source fields, rules for checking data and review steps.
Ongoing upkeep

Named owners, regular re-checks and clear triggers for updates keep the work useful as platforms and your business change.

  • When a person must reviewSend uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them.
  • Clear rules and ownersNew products keep creating the same data problems unless the rules, and who owns them, are written down.
  • Managing catalog changesGive an owner and re-checks to every change in your store theme, apps, products, feeds, promotions and policies.
Your type of business

AI catalog enrichment changes with your customers, risks and costs.

Our method stays the same, but the data, proof, safeguards, buying cycle and what counts as a good result change from market to market.

Local

Local and multi-location

For local and multi-location businesses, AI catalog enrichment has to take into account location details, what local shoppers search for, how many inquiries each location can handle and results for each branch.

FocusDefining each product detail, rules for product details, filters that don't help shoppers
MeasureProfit after direct costs, how shoppers find products, finished checkouts
Online sales

Ecommerce and retail

For online stores and retail, AI catalog enrichment has to take into account the size of your catalog, accurate product facts, stock levels, profit margin, returns and repeat purchases.

FocusFilters that don't help shoppers, how shoppers find products, connecting details to filters
MeasureHow shoppers find products, finished checkouts, return rate
Software

Software companies (SaaS)

For SaaS (software sold by subscription) and software companies, AI catalog enrichment has to take into account self-serve product journeys, integrations, ongoing value, getting new users active and keeping customers.

FocusConnecting details to filters, search check, proof and confidence for each value
MeasureFinished checkouts, return rate, value of new customers
B2B

Business-to-business (B2B) and professional services

For B2B (business-to-business) and professional services firms, AI catalog enrichment has to take into account groups of buyers who decide together, long sales cycles, expert proof and real sales opportunities.

FocusProof and confidence for each value, matching data to feeds and filters, products missing details
MeasureReturn rate, value of new customers, accuracy of the catalog
Health

Healthcare and wellness

For healthcare and wellness businesses, AI catalog enrichment has to take into account accuracy, who reviews what, privacy, safety limits and the quality of appointments.

FocusProducts missing details, thin feed data, rules for product versions (variants)
MeasureValue of new customers, accuracy of the catalog, profit after direct costs
Regulated

Finance, insurance and legal

For finance, insurance and legal firms, AI catalog enrichment has to take into account the location and laws that apply, approvals, evidence, sensitive data and terms that change.

FocusRules for product versions, catalog and page structure, how the whole system fits together
MeasureAccuracy of the catalog, profit after direct costs, how shoppers find products
Education

Education and training

For education and training providers, AI catalog enrichment has to take into account course details, deadlines, enrollment decisions, student outcomes and accessibility.

FocusHow the whole system fits together, map of connected systems, checking your catalog
MeasureProfit after direct costs, how shoppers find products, finished checkouts
Media

Publishers and media

For publishers and media companies, AI catalog enrichment has to take into account original evidence, who wrote what, subscriptions, content rights and fast publishing cycles.

FocusChecking your catalog, how data gets added and checked, product groups to start with
MeasureHow shoppers find products, finished checkouts, return rate
Marketplaces

Marketplaces and directories

For marketplaces and directories, AI catalog enrichment has to take into account very large numbers of listings, matching duplicate records, data quality and making sales work for both buyers and sellers.

FocusProduct groups to start with, rounds of adding details, checking against approved word lists
MeasureFinished checkouts, return rate, value of new customers
Travel

Travel and hospitality

For travel and hospitality businesses, AI catalog enrichment has to take into account busy and quiet seasons, locations, availability, policies and booking value.

FocusChecking against approved word lists, quality checks, feed, rules and app checks
MeasureReturn rate, value of new customers, accuracy of the catalog
Industrial

Industrial and manufacturing

For industrial and manufacturing companies, AI catalog enrichment has to take into account product specifications, compatibility, distributors, long buying decisions and serious inquiries.

FocusFeed, rules and app checks, pre-launch checklist, testing against a checked sample over time
MeasureValue of new customers, accuracy of the catalog, profit after direct costs
Membership

Memberships and communities

For membership and community businesses, AI catalog enrichment has to take into account access levels, ongoing engagement, moderation, renewals and the value members get.

FocusTesting against a checked sample, merchandising tests, list of fair tests to run
MeasureAccuracy of the catalog, profit after direct costs, how shoppers find products
Clear ownership

We do the agreed catalog work. Your team decides what is true about your products.

These columns show what Softscotch delivers, which outside pieces we coordinate and which facts, permissions and business decisions stay with you.

We do the work

The AI catalog enrichment system we agreed in your scope.

  • Set up and check the review of your catalog
  • Set up and check the rules for product details
  • Set up and check the process for adding and checking data
  • Set up and check how data matches feeds and filters
  • Set up and check the quality checks
  • Set up and check what each detail means and is for
We coordinate

The outside pieces that decide whether it all works.

  • Coordinate catalog and merchandising inputs, owners and go-live decisions
  • Coordinate website development inputs, owners and go-live decisions
  • Coordinate payments and delivery inputs, owners and go-live decisions
  • Coordinate product feed and marketplace inputs, owners and go-live decisions
  • Coordinate email and customer marketing inputs, owners and go-live decisions
  • Coordinate analytics and finance inputs, owners and go-live decisions
You review and sign off

Your business keeps control of the facts and the business decisions.

  • Approve the priorities, limits and the written scope
  • Confirm facts about customers, products and how you operate
  • Review sensitive claims, permissions and policy decisions
  • Approve changes customers will see and any major automation
  • Confirm what counts as a good result and how money is measured
  • Keep final control over access, spending and legal compliance
How it fits together

How AI catalog enrichment works: its inputs, safeguards and follow-up.

The first row shows this service in detail. The wider system around it connects winning new customers, the shopping experience, data, operations and reporting.

AI Catalog Enrichment

01
Checking your catalog

Review product fields, category structure, search data, filters, feeds, merchandising rules and missing values.

02
Rules for product details

Define the required and optional product details for each category, what buyers want, the type of product and how shoppers find it.

03
How data gets added and checked

Create a process for adding product details, with source fields, rules for checking data and review steps.

04
Matching data to feeds and filters

Map improved fields into filters, site search, product cards, shopping feeds, recommendations and AI shopping tools.

05
Quality checks

Check for duplicates, missing values, labels that don't match, poor category structure and weak product groupings.

06
Defining each product detail and its purpose

This work includes defining each product detail and its purpose. Decide which details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values.

07
Proof and confidence for each value

This work includes keeping proof and confidence for each value. Keep the text, image, supplier or human evidence behind each added product detail, and how sure we are of it.

08
When a person must review

Send uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them.

09
Clear rules and owners

New products keep creating the same data problems unless the rules, and who owns them, are written down.

The connected growth engine

Catalog and merchandising

Your catalog and merchandising team provides inputs and decisions that AI catalog enrichment can't take on by itself.

Website development

Your website development team provides inputs and decisions that AI catalog enrichment can't take on by itself.

Payments and delivery

Your payments and delivery teams provide inputs and decisions that AI catalog enrichment can't take on by itself.

Product feeds and marketplaces

Your product feed and marketplace owners provide inputs and decisions that AI catalog enrichment can't take on by itself.

Email and customer marketing

Your email and customer marketing team provides inputs and decisions that AI catalog enrichment can't take on by itself.

Analytics and finance

Your analytics and finance teams provide inputs and decisions that AI catalog enrichment can't take on by itself.

One connected scope. AI Catalog Enrichment is coordinated with the strategy, creative, experience, data and reporting it depends on, so responsibilities and handoffs stay visible in one written plan.

What to expect

Your product catalog timeline, and the full growth plan.

AI catalog enrichment moves from decisions about sources and fields to a quality-checked test group of products before it is rolled out more widely. How soon changes go live depends on the size of your catalog, how complex your categories are, the quality of your sources, connections to your platform and how much human review is needed. This work sits inside the six-month Softscotch growth plan shown below.

AI catalog enrichment, from data check to adding details at scale

Days 1–5

Data sources and catalog brief

Agree the source systems, destinations, owners, test group of products, rules for identifying products, which fields matter most, how much review your team can handle and how success is measured.

KPI Brief approved
Weeks 1–2

Product and field rules

Define products, groups, versions, bundles, which source comes first, field types, categories, language and country versions, sales channels and how deletions work.

KPI Rules agreed
Weeks 2–4

Test import

Check and match up a fixed test group of products, set conflicts aside for review and prepare approved new details without overwriting your live data.

KPI Test group prepared
Weeks 3–6

Destination and recovery tests

Check record counts, how products are identified, that fields match everywhere, grouping, rejections, partial updates, deletions, re-running, undoing and what each destination actually shows.

KPI Tests passed
After approval

Careful go-live

Release the accepted test group with named people monitoring it, owners for problem cases and a batch record that lets us undo it.

KPI Go-live checked
Often weeks 6–10

First useful report

Review data quality, processing issues, reviewer workload, how search or feeds behave and any available sales safeguards before rolling out more widely.

KPI Next group decided

Our full growth plan (all services together), month by month

Month 1

Setup and first launch

Agree priorities, fix the biggest problems and launch the first approved work once we have access, content, budget and your sign-off.

KPI First work live
Month 2

Build and improve

Use the first report to improve the live work and build the next agreed materials, campaigns, pages or follow-up steps.

KPI Improvements live
Month 3

Do more of what works

Do more of the messages, audiences, pages or channels that the results show are working, while keeping budgets and quality checks within the limits we agreed.

KPI What works, expanded
Month 4

Strengthen results

Fix weak handovers between teams, gaps in tracking and anything slowing delivery, then test the next priorities against measures that matter to your business, such as lead quality and sales.

KPI Blockers removed
Month 5

Turn more visitors into customers

Improve the offer, message, page and follow-up steps wherever the results show people getting stuck or leads that aren't a good fit.

KPI Improvements to win more customers live
Month 6

Plan the next round

Review what changed, what the results really show, what should stop and which work deserves budget and attention in the next period.

KPI Next round agreed
How we compare

AI catalog enrichment is careful data work, not filling in fields in bulk.

The difference is a clear, written link between expert inputs, how the work is set up, the safeguards, the results we actually see and the next business decision.

The risk of using a different agency for everything
  • Fills in fields before sorting out which product is which
  • Lets several systems quietly control the same field
  • Mixes up product groups, products and versions
  • Overwrites conflicts instead of setting them aside for review
  • Treats "filled in" as the same as "correct"
  • Sends the same record format everywhere
  • Only checks the data that was sent
  • Updates every record at once with no way to recover
  • Hides out-of-date data and rejected records
  • Scales beyond what reviewers can handle
vs
Softscotch
  • Starts with clear, stable IDs for products and versions
  • Names the owner and the winning source for each field
  • Sets up product groups, products, versions and bundles properly
  • Sets conflicts aside for a named reviewer
  • Reports correctness, freshness and readiness
  • Maps each destination separately
  • Compares what was sent with what went live
  • Uses step-by-step batches you can undo
  • Monitors counts, data age, matching and issues
  • Scales up only after a typical test group is accepted
The numbers

Results from Softscotch client work.

These totals come from work for Softscotch clients across search, paid ads, websites and lead generation, as published on softscotch.com. Past results don't guarantee future results.

$12M+Ad spend managed for clients
87,200+Leads generated for clients
2,140+Search terms ranked on Google's first page
$28M+Revenue generated for clients
Client case studies

All six Softscotch client stories.

Every Quick Story currently published on Softscotch.com, covering education, ecommerce, healthcare and SaaS. Stock photography represents each client industry; the figures are reported historical results and do not guarantee future performance.

Education consultant speaking with a prospective student Education Ads + local SEO
+183%leads

183% more leads for an education consultancy

WordPressQuora adsLocal SEO
The challenge

An education consulting business in India needed to build its digital presence and lead flow from scratch.

What changed
  • Built a new WordPress website
  • Used Quora ads to grow daily lead volume
  • Added Local SEO and community engagement
+183% leads25+ leads/day-38% bounce rate+273% new users
Bright modern home interior representing the home-solutions client Home solutions Shopify + SEO
+82%Shopify sales

Home-solutions ecommerce growth

ShopifyOrganic searchAmazon ads
The challenge

A home-solutions business was dependent on business-to-business (B2B) marketplaces, losing sales and recovering from a Google penalty.

What changed
  • Resolved the Google penalty
  • Revamped the website and online store
  • Improved organic visibility and Amazon campaign performance
+82% Shopify sales+147% organic+23% Amazon ROAS
Mother and baby representing the lactation-consultant client Health consultant $20/day
5/daybooked calls

Five booked calls a day for a health consultant

Facebook adsGoogle AdsLocal SEO
The challenge

A Houston lactation consultant needed to recover a malware-damaged website and rebuild local patient acquisition.

What changed
  • Rebuilt the hacked site and migrated it to secure hosting
  • Ran targeted Facebook and Google campaigns
  • Reached top-three local Google positions
5 calls/day$20 daily spend+87% local traffic+34% new users
Ecommerce owner preparing customer orders beside a laptop Ecommerce 10 months
+315%organic traffic

315% organic growth for an ecommerce site

Content strategyOn-page SEO
The result

Softscotch reports a 315% increase in ecommerce organic traffic over 10 months through focused on-page SEO.

What changed
  • Optimized product and collection pages
  • Improved the links between pages and the page addresses (URLs)
  • Made the site easier for Google to explore and better matched to what people search for
+315% organic10 monthsProductsCollections
SaaS team reviewing campaign data around a laptop SaaS platform Cold outreach
16%reply rate

9% close rate for a transcription SaaS

ProspectingSMTP
The challenge

A transcription platform needed a qualified prospect list and a reliable email-sending setup for outreach we managed.

What changed
  • Built a comprehensive prospecting list
  • Managed the cold outreach campaigns
  • Set up reliable email-sending servers (SMTP)
9% close rate16% reply rateSaaSManaged outreach
Healthcare professional consulting with a patient Healthcare 6 months
+182%organic traffic

182% organic growth for a healthcare clinic

HealthcareOn-page SEOOff-page SEO
The challenge

A clinic in a competitive location needed to build organic search visibility from the ground up.

What changed
  • Built the clinic's foundation for unpaid (organic) Google search
  • Combined on-page and off-page SEO
  • Reached top-five positions in Google Search
+182% organic6 monthsTop 5 Google
01 / 06
In their words

Teams that stopped leaving clicks on the table.

★★★★★

"The Softscotch team has been a solid partner as our business evolved over the last five years. Unlike most agencies, they have never charged us for work that falls outside the agreement. That is what sets them apart."

IM
Imtiyaz MominHairareus
★★★★★

"Without their knowledge, we would be in a very different place than we are now. Whenever they find something that can be improved, they implement it quickly. I could not be happier with their performance and response time."

AP
Aaron PaulsonBaby Pavilion
★★★★★

"Softscotch goes above and beyond the scope. The time, effort and dedication they put in is like having your own in-house marketing team. They are not just another marketing company."

TM
Tabassum MithaniTRS Care
★★★★★

"We are extremely happy with Softscotch, such an attentive team. No project is too small, and they give their 100%. We are extremely satisfied with their work."

SH
Safiya HafiziSimply Lactation
★★★★★

"Softscotch is a great team to work with: transparent, reliable and genuinely invested in the growth of your company. They are good partners to have by your side."

MJ
Malvika JainCyclop
★★★★★

"The Softscotch team has been a solid partner as our business evolved over the last five years. Unlike most agencies, they have never charged us for work that falls outside the agreement. That is what sets them apart."

IM
Imtiyaz MominHairareus
★★★★★

"Without their knowledge, we would be in a very different place than we are now. Whenever they find something that can be improved, they implement it quickly. I could not be happier with their performance and response time."

AP
Aaron PaulsonBaby Pavilion
★★★★★

"Softscotch goes above and beyond the scope. The time, effort and dedication they put in is like having your own in-house marketing team. They are not just another marketing company."

TM
Tabassum MithaniTRS Care
★★★★★

"We are extremely happy with Softscotch, such an attentive team. No project is too small, and they give their 100%. We are extremely satisfied with their work."

SH
Safiya HafiziSimply Lactation
No black box

You'll always know exactly what we changed.

Plenty of agencies go quiet after the audit and you never quite know what you're paying for. We do the opposite. You get a live view of every change and what it moved, in plain language you can act on.

Your delivery, at a glanceSample
Changes shipped12
Pages reviewed28
StartMidpointNow
Delivery activity✓ Up to date
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Live client dashboard

Every change, score and ranking in one place you can open any time, not a monthly PDF you wait around for.

This week, explainedSample
✓Titles updated
Ready for your review
SSSoftscotch weekly update
0:00
2:00
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Weekly video summary

A short screen-recorded video (Loom) each week walking you through exactly what shipped and what it moved, so nothing is a mystery.

The change is clearSample

Before

Titles30 / 100
Structure42 / 100
Content28 / 100

After

Titles84 / 100
Structure79 / 100
Content90 / 100
Same page. Reviewed changes.
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Before/after score per page

See the exact lift on every page we touch, not a vague "it's improving" you have to take on faith.

Your weekly reportSample
01
What shipped

Approved page updates are live.

02
What we learned

Product details need a review.

03
What happens next

Confirm the next priority pages.

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Plain-English reporting

Clicks, rankings and revenue, explained in words you can act on, instead of a wall of charts about how often you were seen (impression charts.

Your data. Your view.Sample
Organic search
StartMidpointNow
Sample report preview↗
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Reports on demand

Ask for any cut of the data whenever you need it and get it, no waiting for the end of the month.

Every step, visibleSample
  1. ✓
    Proposed

    Softscotch prepares the change.

  2. ✓
    Approved

    Your team gives the go-ahead.

  3. ✓
    Published

    Checked live and recorded.

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Ethical, transparent, always on

One accountable team, strictly white-hat (only methods search engines and platforms allow), no shortcuts and no surprises. Support whenever you need it.

One plan, full-stack access

One Softscotch plan gives you access to the full service stack, coordinated around your priorities.

You do not need a separate agency or retainer for every channel. Your plan can draw from search, paid media, web, ecommerce, outreach, automation, lead generation, messaging, content, creative and operations. The agreed scope sets what is active, in what order and at what capacity.

Pricing approach

One price for everything, not a stack of separate retainers.

You get one price for your whole marketing, not a separate retainer for every service. SEO, paid ads, your website, automations, cold email and newsletters, we run all of it as a single scope, so you get the most result for what you pay. Agencies sell each of these on its own, so the invoice climbs whether or not that line drives a single lead.

Bought separately, service by service

A different agency or retainer for each service. The bill stacks up fast, and keeps climbing whether or not each one moves a single lead.

SEO (technical, on-page, content)$2,000–3,500/mo
Local SEO+$750–1,500/mo
Programmatic SEO+$1,000–2,500/mo
CRO & landing pages+$2,000–3,500/mo
Paid ads (Google & Meta)+$2,500–4,000/mo
Marketing automations+$1,500–3,000/mo
Cold email outreach+$1,500–2,500/mo
Email marketing & newsletters+$750–1,500/mo
Website management & care+$750–1,500/mo
WordPress build & setup+$2,500–6,000 once
10 separate services$12,750–23,500/mo

Plus the one-off build, and a fresh contract, kickoff and onboarding delay for every new service you add.

All of it, one scope

We run your whole marketing as one engagement, so you get the most result for what you pay, not a pile of disconnected invoices.

  • SEO: technical, on-page, content, local and AI search
  • Template-built pages (programmatic SEO), links from other websites (backlinks) and authority
  • CRO and landing pages
  • Paid ads across Google and Meta
  • Automations, cold email and newsletters
  • WordPress build, setup and ongoing care
  • One team, one roadmap and one point of contact
  • Reporting tied to qualified leads and revenue
Everything above. One flat monthly fee.A fraction of the cost of the stack on the left
Plus free access to our own platforms
Post InstantlyVerified WidgetsTrending FunnelsBrands for CreatorsDarnItSEOGoHighLevel

Priced to your site after the audit. No sticker price, and no per-service creep.

What shapes your quote
Site sizePage count, templates and how much of the site actually needs work.
Market competitionHow hard the terms you want to win are, and who you are up against.
Content needsHow much new writing, rewriting and production the plan calls for.
Build complexityYour website platform (CMS, the system you edit your site in), your other tools and how involved the changes are to make.
Final pricing comes after the audit, once we have seen the four things above on your actual site. You get a written scope and a fixed quote before any work begins: no lock-in, no surprise line items, and no upfront number that ignores your market.
Request a custom scope
Questions, answered

AI catalog enrichment FAQs, including the questions other agencies often skip.

These answers cover expert safeguards, setup, measurement, what we can and can't guarantee, timing, pricing and who owns what.

Filter
What is included in AI catalog enrichment?

It can include checking your source systems and how products are identified, agreed field definitions, setting up categories and links between products, checking imports, fixing duplicates and conflicts, adding details by people and with AI help, mapping data to each destination, waiting lists for problem records, releasing changes in stages, checking what destinations show, monitoring, re-running and undoing changes, plus training and handover to your team. The written scope names the systems, groups of products and destinations included.

Can you add details to thousands of products (SKUs)?

Yes, when your source data, destination limits and review capacity allow it. First we sort out which product is which and how the data is structured, test typical groups of products and versions, record how many were accepted, rejected or set aside, and confirm we can undo changes before scaling up. A large catalog is never a reason to risk an unsafe update of everything at once.

Does catalog enrichment help paid ads?

It can support product feeds, labels, product grouping and how campaigns are organized, but running paid ads is a separate service. We only map fields each destination supports, keep the data you send separate from the final live product listings, and report feed or platform results separately from free search results and how shoppers find products on your site.

How do you measure catalog enrichment?

We track conflicts over which product is which, data structure errors, and how complete, correct, consistent and up to date the data is, plus review workload, processing issues, whether data matches in each destination and how fast we recover from problems, by group of products, language and country version and sales channel. Then, where the evidence allows, we connect those measures to search, filters, feeds, how shoppers find products, sales, returns and money results.

Do you include defining each product detail and its purpose?

Yes, where it fits the agreed AI catalog enrichment scope. We decide which product details actually help shoppers find products, help merchandising, improve recommendations or feeds before creating any values. Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you keep proof and confidence for every value?

Yes. For important fields, we can keep the main source, the original value, how it was changed or worked out, the AI model or rule version, the reviewer, the batch and what each destination received. This lets us explain any live value, settle conflicts and undo a change that turned out to be unsafe.

Do you include rules for product versions (variants)?

Yes, where it fits the agreed AI catalog enrichment scope. We keep facts about the whole product separate from facts about each version, so color, size, material and compatibility don't spill over onto the wrong SKU (the code for each exact item). Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you include checking values against approved word lists?

Yes, where it fits the agreed AI catalog enrichment scope. We limit results to approved values, units, formats and category rules, with a clear "unknown" option. Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

Do you include testing against a checked sample over time?

Yes. Added details can be checked against a "golden set" (a sample of products whose correct details a person has confirmed). We also test the catalog for changes in data structure, unusual record counts, conflicts over which product is which, out-of-date data, rejections by destinations and search results getting worse after any change to sources, rules or AI models.

Do you set rules for when a person must review?

Yes, where it fits the agreed AI catalog enrichment scope. We send uncertain, high-risk and new kinds of values to category experts, and keep their corrections so the system learns from them. Our first review confirms the access, proof, person responsible for the work and the check that it works before it is added to the plan.

What do you change first?

We start with how products are identified, agreements with each data source and problems with how data is structured, because adding details to the wrong product, or to fields that keep changing, multiplies mistakes. We then pick the first group of products by business value, data risk, what each destination needs, how much your reviewers can handle and how easily changes can be undone.

Do you carry out the recommendations?

Yes, within the written scope. We build, set up or rewrite the approved AI catalog enrichment work, coordinate the outside pieces and check the live result. Any task that needs a separate specialist or someone on your team is named before work starts.

Does this replace our PIM, ERP, DAM or feed manager?

Not by default. We can improve how your data is organized, your workflows, system connections and destination mappings around the systems you already use, such as your PIM (product information system), ERP (business system for stock, orders and finance) or DAM (image and file library). Replacing or setting up a core platform is only included when the written scope clearly names that system, the move to it, testing, ownership and ongoing upkeep.

Can you guarantee a specific result?

No. Platforms, markets, customer behavior and outside conditions are beyond any agency's control. We are responsible for the agreed setup, quality checks and reporting, and we will show you what changed and what the evidence supports.

How long does the first round of work take?

A data sources and catalog brief usually takes 3 to 5 business days once owners, typical sample records, destinations and access are ready. A test import with a release checked in each destination is often planned over 6 to 10 weeks. Unclear product IDs, updating large numbers of old records, many systems or language and country versions, custom system connections, specialist review and slow approvals can make it take longer. These are planning estimates, not a promised go-live or results date.

How is AI catalog enrichment priced?

Pricing depends on how many sources and destinations there are, the number of products and versions, how complex the links between products are, field and system connection work, how much adding details and review is needed, how often data updates, monitoring and upkeep. Platform licenses, data clean-up, paid connectors, moving to a new system and specialist review are separate unless listed in the proposal. Before work begins, the proposal states what you get, our assumptions, who does what, timing, our fee, the contract length and how either side can end it.

Which catalog source systems can you work with?

Depending on access and scope, data can come from an ERP (business system for stock, orders and finance), a PIM (product information system), a DAM (image and file library), supplier files, your store platform, a database, an API (a secure link between software systems) or your current feed manager. We define which system owns each field, how full uploads, change-only updates and deletions work, and where badly formed or conflicting records are set aside for review instead of quietly overwriting your catalog.

How do you check a product feed or AI shopping destination after sending data?

We don't stop at the source record. Where the platform shows it, we compare the data we sent with the live product listing, its status, issues, grouping, eligibility and details that change often, such as price or stock. Work on Shopify Catalog, Google Merchant Center's conversational attributes (optional fields for AI shopping) and OpenAI's product feeds for ChatGPT shopping depends on each destination and whether you are eligible, and being ready can't guarantee your products will be seen or sold.

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