Search drives conversions, but precision drops on long-tail queries
Multilingual or intent-driven searches keep missing what keyword matching alone can't see.
AI & intelligent automation
Make search, content and decisions smarter — without another static rule.
We build AI integrations that make your commerce systems smarter — semantic product search that surfaces what keyword queries miss, LLM pipelines that scale content operations, and ML signals that improve pricing, fraud, and routing decisions.
SIGNS THE PROBLEM IS DATA, NOT RULES
Four signs a rulebook has run out of room to help.
Product content needs to scale faster than the team that writes it
Descriptions, attributes and SEO tags can't keep up without linear headcount growth.
Fraud, pricing or routing still run on static rules and manual review
Every edge case needs a human, because the rulebook can't weigh signals the way a model can.
Demand planning and inventory still run on spreadsheets and gut feel
Stockouts and overstock get discovered after they've already cost you, not before.
OUR APPROACH
Search, generation or decisioning — the right approach depends on which problem you actually have.
These are three different technical problems wearing the same label of "AI". Mixing them up is how a project ends up building the wrong thing.
Search & retrieval
Semantic and vector search re-ranks results by meaning, not just keyword overlap — for catalogues where precision drops on long-tail or multilingual queries.
Needs a representative query log and product data good enough to embed meaningfully.Generation
LLM pipelines draft and enrich product content, support replies, or documentation at a scale no team can hand-write.
Every generated output needs a validation step — nothing ships unreviewed into a customer-facing field by default.Decisioning
ML models replace static if/else rules for fraud scoring, dynamic pricing, or order routing — using signals a rulebook can't weigh.
Needs enough historical data and a fallback path for when the model is wrong.WHAT WE CAN OWN
Three AI problems, with the boundary visible on each.
Search that understands intent, not just keywords
Hybrid ranking (BM25 + dense retrieval), multilingual support and personalization signals sit on top of your existing business rules, not instead of them.
Which model or vector database to standardize on is a joint technical call, not something we lock you into unilaterally.Content and support that scale without headcount
OpenAI, Anthropic, Vertex AI or open-source models connect to your commerce backend for product descriptions, attributes, SEO tags and support replies — with a validation step before anything publishes.
Choosing the model provider and approving what tone it writes in stays your call; we build the pipeline and the guardrails.Decisions that use signals, not just rules
Fraud scoring, dynamic pricing and order routing run on ML signals built for production, not a proof-of-concept — plus demand forecasting and anomaly detection on top of your existing data warehouse.
No vendor lock-in, no black box — every model decision is built to be auditable, not just accurate.HOW WE WORK
Assess → Prototype → Integrate → Operate.
Assess
In
Your data, existing search/rules, and the actual decision or content bottleneck.
We do
We check whether the data is good enough to support a real model, not just a demo.
Out
A scoped recommendation, including when AI isn't the right fix yet.
Prototype
In
The scoped problem and representative data.
We do
We build a working prototype against real queries or historical decisions, not synthetic examples.
Out
Measured accuracy and a go/no-go case for production.
Integrate
In
The validated prototype and your production systems.
We do
We wire the model into your commerce backend with monitoring, fallbacks, and human review where it matters.
Out
A production feature, not a detached notebook.
Operate
In
Live traffic and real outcomes.
We do
We track drift, retrain on schedule, and keep a human escalation path for edge cases.
Out
A system that stays accurate as your catalogue and customers change.
FREQUENTLY ASKED
Questions that decide whether AI is the right fix here.
Do you build us a custom model, or use an existing one?
Usually neither extreme — we connect proven foundation models (OpenAI, Anthropic, Vertex AI, or open-source) to your data, rather than training something from scratch unless the problem genuinely needs it.
How do you stop an LLM from generating something wrong on our product pages?
Every generated output goes through a validation step before it's allowed to ship — nothing customer-facing gets published unreviewed by default.
Is this explainable, or a black box?
We engineer for auditability — you can see what signals drove a pricing, fraud, or ranking decision, not just the output.
What if we don't have much historical data yet?
That's usually the first finding, not a blocker — we're honest about what a model can and can't support before committing to a build.
Let's find your actual AI problem.
Tell us where search, content or decisions are the bottleneck — we'll tell you whether it's a search problem, a generation problem, or a decisioning problem.
Discuss an AI integration →Already know you need a specific commerce capability, not AI specifically? See custom commerce platform strategy →
7+years on the market
42+projects delivered
20+specialists on the team
8+countries served