Retail runs on fast decisions made from slow, scattered data

Range, price, promotion, stock and service decisions depend on store, digital, supply-chain and customer data that rarely sits in one place. We fix the data underneath and put AI where the commercial value can be measured.

Start a conversation The problems we solve

The problems

What gets in the way of good trading decisions

  • Forecasts nobody fully trusts

    Demand forecasts are overridden by hand because the inputs are late, partial or disputed.

  • Range and product decisions on partial data

    Buying and range decisions are made without a single view of sales, returns, margin and availability.

  • No single view of the customer

    Store, online, app and service each see a different customer.

  • Promotions that cost margin

    Promotions are judged on volume, while the margin and the stock consequences arrive later.

  • Stock you cannot see

    Inventory across stores, warehouses and suppliers does not reconcile, so losses and availability gaps are found late.

  • Store and digital data that do not join up

    Footfall, transactions, web and app behaviour live in separate systems with separate definitions.

  • Supply-chain visibility

    Lead times, supplier performance and inbound stock arrive by email and spreadsheet.

  • Customer service knowledge

    Agents search policies, product information and past cases to give an answer that should be instant and consistent.

  • Product content at scale

    Descriptions, attributes and imagery for thousands of lines take time and drift out of date.

  • Systems that grew by acquisition

    Growth and acquisitions left several ERPs, tills and data stores that each tell part of the story.

Forcing events

When it usually comes to a head

  • A trading season that exposed the gaps in stock or forecast data
  • An acquisition or new channel adding another set of systems
  • Margin pressure that makes promotion and pricing decisions matter more
  • A board asking what AI agents will actually return, in pounds
  • Replatforming the ERP, e-commerce or point of sale

What you already hold

The knowledge and data involved

  • Point-of-sale and e-commerce transactions
  • Stock, warehouse and supply-chain data
  • Product master data, attributes and content
  • Customer, loyalty and service records
  • Promotional calendars, pricing and margin
  • Policies, returns rules and service knowledge

Workflows

Workflows we change

Specific pieces of recurring work, each with an owner, a baseline and a measure of what changed.

  1. Demand forecasting

    Forecasts built on joined-up sales, stock and promotional data, with the overrides recorded and learned from.

  2. Range and product decisions

    Range reviews with sales, margin, returns and availability in one view.

  3. Promotion and margin analysis

    Promotions judged on margin and stock as well as volume, before and after they run.

  4. Stock and inventory visibility

    Stock reconciled across locations, with losses traced to their causes.

  5. Customer service answers

    Agents given the approved answer from policies, product information and resolved cases, cited.

  6. Product content

    Product descriptions and attributes drafted from the product data for a person to approve.

  7. Trading insight

    Weekly trading questions answered from the data, with the figures traceable to source.

Where AI earns its place

AI opportunities

  • Agents with a measured commercial case

    AI agents for trading, service or content, each with a baseline and a measure in pounds before it scales.

  • Service answers that are consistent

    Customer service grounded in approved policy and product information, cited, with the agent in control.

  • Product content at speed

    Descriptions and attributes drafted from product data, checked by a person before publishing.

  • Trading questions answered from the data

    Natural-language questions over curated trading data, with the figures traceable.

What usually sits underneath

When AI disappoints, the cause is usually the data, systems and ownership beneath it, so we look there too.

  • Product, customer and location data held differently in each system
  • Several ERPs or tills after growth and acquisitions
  • Store and digital data with different definitions of a sale or a customer
  • Batch reporting that arrives after the decision has been made
  • Data platforms that cost more each year without answering more questions

Broader technology and data work

  • Data platform design and modernisation on Azure and Databricks
  • Integration after acquisitions or replatforming
  • Independent review of a forecasting, CDP or AI vendor
  • Technology due diligence on a retail acquisition

What we could build or change

Examples of the work

Illustrations of what an engagement could produce, scoped to your own information and measured against a baseline.

  • A trading data model that reconciles store, digital and stock data
  • A customer service assistant grounded in policy and product information
  • A product content workflow that drafts from product data for approval
  • A promotion analysis that shows the margin and stock effect, not only volume

Answerable

Answerable Assist for customer service

Answerable Assist is set up for customer service: knowledge base, support tickets, product reviews, sizing guides and return policies. The screen shows it answering how to handle an exchange for the wrong size, with the guide behind the answer.

See every configuration

Answerable Assist: CX & Customer Support

Set up for customer service: knowledge base, support tickets, product reviews, sizing guides and return policies.

Answerable Assist screen with an answer on exchanging an incorrectly sized item, an exchange table and its cited guide (opens the screen full size in a new tab)
Answerable Assist answering how to handle an exchange for an incorrect size, with the steps and the guide behind them. Real product screens from demonstration environments. Organisation and author names in them are fictional, and the figures show how the product answers, not findings.

Proof

What we have built and done

Each item says where it comes from: TechGuidr’s own products, the founder’s earlier roles or demonstration configurations.

  • Founder’s earlier role

    Global Data & AI Lead, Microsoft Retail & Consumer Goods

    From 2022 to 2025 Dan was Global Data & AI Lead in the solutions team for Microsoft’s Retail & Consumer Goods business. This is experience from an earlier role, not TechGuidr client work.

  • Founder’s earlier role

    Finding where the stock was going

    For a leading UK retailer, a data warehouse built around the events where losses clustered, so unexplained stock losses could be traced to causes. Delivered in an earlier role.

    See earlier work
  • Founder’s earlier role

    Donated goods tracked from bag to till

    For a national charity’s retail operation, a greenfield Azure data platform connecting legacy point-of-sale terminals and IoT tracking across vehicles, textile banks and bags. Delivered in an earlier role.

    See earlier work
  • Demonstration configuration

    Answerable Assist: CX & Customer Support

    A configuration for customer service answers from policies, product information and resolved cases, shown on illustrative content.

    See the configurations

How we engage

Where to start

Every engagement starts from the decision or outcome you need. Prices are starting points and exclude VAT.

Industries

Related industries

All industries

Bring the trading question nobody can answer quickly

We will tell you what sits underneath it and whether AI, better data or a process change is the right first move.

Start a conversation See the AI Work Review