Search, recommendations and shopping agents that know your catalog, inventory and rules, to sell more with measured results. For e-commerce managers.
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AI in production:
DIAL and Mirador, for The Hackett Group (NASDAQ: HCKT)
Contas Casal (own product):
AI assistant on real data (RAG), with per-model cost tracking
E-commerce:
Mahout ERP, with orders, inventory and Magento 2 integration
Open source:
Official PagSeguro and Mercado Pago Magento 2 modules, 214K installs
AI in production: DIAL and Mirador, for The Hackett Group (NASDAQ: HCKT)
Contas Casal (own product): AI assistant on real data (RAG), with per-model cost tracking
E-commerce: Mahout ERP, with orders, inventory and Magento 2 integration
Open source: official PagSeguro and Mercado Pago Magento 2 modules, 214K installs
Shoppers search in their own words, the store returns "no results" and they buy from a competitor.
Thousands of products are live with the description copied from the supplier, incomplete attributes and the wrong category.
The recommendation app suggests out-of-stock or unrelated items, because it cannot see your orders or your margins.
Inventory is planned in a spreadsheet from last year's average, and best sellers run out while slow movers pile up.
Hybrid search (semantic and keyword) with reranking that understands synonyms, typos and intent and respects filters and inventory, price and margin rules.
Recommendations on the storefront, the product page and the cart, from models trained on order history plus commercial rules, always respecting available inventory.
Descriptions, titles and attributes generated in bulk from supplier data and categories assigned by supervised classification, with human review by sampling before publishing.
An assistant that answers questions, compares options and builds the cart from live catalog, inventory and price, remembers the shopper's preferences and hands off to a person when needed.
Structured product data exposed through an API or an MCP server, the protocol that connects agents to systems, so external shopping assistants can find your products.
Forecasting models trained on sales, seasonality and promotions show how much of each item to buy and, before they feed planning, are validated on periods the model has not seen.
Supervised models sort tickets and customer reviews by reason and urgency, so the team can prioritize and fix in the catalog whatever generates complaints.
Prompt caching reuses the fixed part of prompts and a semantic cache returns answers already given to similar questions: lower latency and cost per search and conversation.
Evals on real searches and questions, A/B tests against the current version and a dashboard of conversion, quality and cost per feature, to show whether each one pays off.
See also: AI agent development, e-commerce content writing, and e-commerce development.
Related experience, in data: DIAL, an AI-native data-strategy platform with data discovery, profiling and cleansing, the same work a large catalog needs.
Related experience, in AI: Mirador, an engine that turns natural-language requirements into live React dashboards and apps, on Claude and OpenAI behind a provider abstraction.
Related experience, in e-commerce: multi-tenant SaaS ERP for SMBs, e-commerce retailers and franchise networks, with orders, multi-warehouse inventory and Magento 2 integration.
Related experience, in e-commerce: merged contributions to the official payment modules for Magento 2.
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AI for e-commerce is the use of language models, semantic search and machine learning to sell more and run an online store better: search that understands intent, recommendations, demand forecasting, catalog content generated at scale and shopping agents. Hire custom work when ready-made apps cannot see your catalog, your inventory or your rules.
We analyze the catalog, zero-result searches, orders and the platform. You receive the use cases ranked by impact and effort, the target architecture, the success metrics and the estimate.
We explore the catalog, order and search data (data science), set aside the test data and build the evaluation dataset of real searches and edge cases. You approve the business metric of the test.
One use case, usually search, the catalog or demand forecasting, on your real data and measured by the evaluation dataset. You receive a working PoC, the results and a recommendation to proceed, adjust or stop.
Store integration, guardrails, observability and A/B testing against the current version, in short releases. You receive the feature in production, with a dashboard of conversion, quality and cost.
We monitor quality, cost and the test result, adjust what operations reveal and prioritize the next use case. Evolution can continue under a monthly retainer.
| Scope | Typical timeline | Engagement model |
|---|---|---|
| AI for e-commerce discoveryPrioritized use cases, data, target architecture, metrics and estimate | 1–2 weeks | Fixed-scope project |
| Proof of conceptOne use case (search, recommendations, catalog or demand forecasting) on real data, with evals | 2–4 weeks | Fixed-scope project |
| ProductionFeature integrated into the store, with guardrails, observability and A/B testing against the current version | 6–12 weeks | Fixed scope or time & materials with a cap |
| Ongoing evolutionNew use cases and quality and cost tuning | Ongoing | Monthly fractional retainer |
Timelines are typical ranges. Discovery defines the scope and the estimate of each project; the contract can be in USD or BRL.
Want to know which format fits your project?
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Here AI is the product, so governance is part of the delivery: evaluation before launch, guardrails in production and a person accountable for every decision that affects price, inventory or the customer.
Source of truthPrice, inventory and delivery time always come from your systems, by direct query; the model neither invents nor calculates them.
Evals and validationReal searches and questions run in CI, and a quality regression blocks the release; forecasting and classification models are validated on data they did not see in training.
Machine learningSupervised learning (classification, regression, forecasting) when the problem calls for a model of its own; fine-tuning and reinforcement learning come in only when an eval shows a gain over prompt and context.
Guardrails and reviewInput and output validation, prompt-injection defenses, action limits (no discount outside the rules) and human review of a sample of generated content.
Memory and costThe assistant keeps the session context and, for signed-in customers, the history in your systems; prompt caching, a semantic cache and per-model routing keep the cost per conversation under control.
Your dataCustomer and order data never trains models, and we work under NDA.
| Criterion | Ready-made AI app or plugin | Custom AI integrated into the store |
|---|---|---|
| When it makes sense | Store on a SaaS platform, small or mid-size catalog and standard rules | Large catalog, rules of your own or a store on Magento, custom or headless |
| Data the AI can see | Whatever the platform exposes to the app | Catalog, inventory, orders, margins and commercial rules from your systems |
| Rules and customization | The settings the vendor provides | Ranking, business rules and tone of voice defined by your operation |
| Costs | Subscription, usually by volume of sessions or orders | Initial development; then tokens and infrastructure monitored per feature |
| Time to start | Days: installation and configuration | Weeks: discovery, proof of concept and production |
AI for e-commerce is the application of language models and semantic search to the catalog, the orders and the customer service of an online store, to raise conversion and cut manual work. The most common use cases are: search that understands shopper intent, product recommendations, generation and correction of descriptions and attributes, agents that help shoppers choose and buy, and automated support. Each can be a ready-made app or a custom solution integrated with your data.
Start with the use case that already has data available and an outcome that is easy to measure; in most stores, that is search or catalog enrichment. Search has traffic and clear metrics: zero-result searches and the conversion of shoppers who search. A better catalog helps search, SEO and recommendations at once. Shopping agents come later, when catalog and search already perform well. Discovery ranks the use cases by impact and effort, with your numbers.
No. AI features run as services next to the store, connected through APIs to the catalog, inventory and orders, and show up on the storefront as a search, recommendation or chat component. That holds for Magento, custom platforms and headless. On closed SaaS platforms, the limit is what the API exposes: if it gives no access to catalog and orders, the app of the platform itself tends to be the better path.
Yes, when there is enough order history to train and validate the model; discovery measures that before any estimate. We use data science and supervised learning (classification, regression and forecasting), validated on data the model has not seen. Without history, the minimum is a catalog with title, attributes, price and inventory through an API, and the store starts with search or the catalog. Reinforcement learning, which tunes ranking from clicks and purchases, comes in only with enough traffic and a gain proven in an A/B test.
It depends on the use case, the size of the catalog, the quality of the data and how many integrations the store requires. That is why we do not publish a price list. A 1–2 week discovery defines scope and estimate, and the proposal can be fixed scope or time & materials with a cap, in USD or BRL. Beyond development there is the recurring cost of tokens and infrastructure, which we forecast per search and per conversation and monitor per model.
With A/B testing: some visitors get the AI version, others get the current one, and we compare conversion, average order value and revenue per session. Each use case has its own metrics, such as zero-result searches, clicks on recommendations and time to publish a product. We define the metric and the baseline before building. We do not promise percentages: the test shows the effect on your store, with your data.
Only what each task needs, and never to train models. Search and catalog work on product data; recommendations use pseudonymized history where the use case allows; the agent receives customer data only within the session of that customer. Access is logged, and the provider and the processing region are chosen with your team during discovery, according to your compliance requirements. We work under NDA.
Agentic commerce is shopping carried out by AI shopping agents, which research, compare and, with authorization, place the order on behalf of the customer. To be found by them, the catalog needs complete, structured data (attributes, price, inventory, policies), up-to-date feeds and APIs or MCP servers that agents can query. The standards are still taking shape, so we start with the foundation that serves any channel: clean product data and a well-documented API.
AI-focused IT consultancy
Senior engineering from discovery to delivery: whoever designs the architecture also writes and reviews the code.
Master Timm · Updated on
In the first conversation we look at the catalog, the search and the orders, and tell you plainly where AI pays off in your store and where a ready-made app is enough.
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