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Product data enrichment
Fills missing attributes against your own schema and source documents, and holds a record back when the source does not support a value — rather than inventing one that survives until a customer returns the item.
Agentic AI in e-commerce & retail
One product record, and every place it has to be correct .
We build agents that enrich product data against your own schema, resolve a customer's configuration out of a catalog too large for rules, and hold anything incomplete back instead of publishing a gap.
Trusted by operators
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+11.76% orders from day 1 of the Australian launchThree-agent product advisor guiding customers through print-order configuration.
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Supply chain intelligence agents reducing manual coordination overhead.
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37 kernels ported from CUDA to Intel GaudiProduction AI workloads moved to new hardware for on-prem LLM deployment.
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AI agent implementation for a global automotive enterprise.
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2 hrs → 3 min to generate a workflowText-to-workflow agents building validated node graphs inside the platform.
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0 hallucinations across 329+ validation scenariosHIPAA-compliant agentic RAG over proprietary clinical triage guidelines.
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Why agentic AI
Your catalog answers the customer who already knows the SKU
Baymard Institute's on-site search benchmark (170+ sites and apps, more than 10,000 individual performance assessments, updated April 2026) found that 56% of sites do not adequately support how users actually search. The breakdown is the useful part: only 12% of sites have trouble with an exact product name, while 43% fail on queries that describe a use case and 39% on queries that describe a feature. The customer who knows the model number is served. The one describing a need is not, and roughly half of sites turn that into a no-results dead end rather than a route forward.
a gap costs less before publication than after a return
Use cases
Where agents earn trust in commerce operations
Workflows with a defined source of truth, a countable outcome, and a reviewer already in the loop.
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On-site search that resolves intent
Handles the queries that describe a use case or a feature rather than a model number (the categories where Baymard found 43% and 39% of sites failing) and returns a route forward instead of a no-results page.
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Guided order configuration
Narrows a catalog too large for rules down to one valid, priced specification — the mechanism behind our three-agent advisor for Mixam, working across more than a billion product combinations.
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Order verification and exception routing
Checks an incoming order against stock, fulfillment constraints and production rules, clears what is clean, and routes genuine exceptions to a person with the reasoning attached.
The cost of an incomplete catalog
Where commerce workflows lose orders today
Mixam is a direct e-commerce deployment — guided order configuration in production. ARIJ Network and Schmitt-Thompson are adjacent, and they are here because they demonstrate the two mechanisms a catalog agent depends on: answering only from a named source, and declining to answer when the source does not cover the case. Every figure below links to the case study behind it.
Agents do not reprice your catalog or publish to a channel unsupervised. They fill what your own data supports, resolve what the customer is actually asking for, and stop at the point where a guess would become a return.
The success rate Mixam originally asked us to reach
80%
The target their Digital Product Manager set for workflow results before the engagement started.
1% → 100%
Knowledge-inquiry response rate, before and after
Adjacent, not e-commerce — A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base — across 22 countries.
0 hallucinations
Hallucination events across 329+ validated scenarios
Adjacent, not e-commerce — Nurse-triage guidance where a wrong recommendation is a patient-safety event — staged retrieval and validation rather than one model answering in a single pass.
Client results
Proof from production commerce deployments
Mixam is a direct e-commerce engagement. The other two are the closest available proof for the mechanism underneath — grounded output tied to a named source, and a refusal path that was designed rather than discovered in production.
Delivery path
From catalog audit to a production commerce agent
TriStorm keeps merchandising and engineering aligned — the attributes an agent must never invent are named before full build commitment.
Audit the catalog and the queries
We audit your attribute schema, the completeness of the records behind your best-selling lines, and your real search and order logs — then rank candidates by revenue exposure rather than by how easy they are to automate.
- Catalogue & schema audit
- Query and gap analysis
- Prioritised use case
Build and validate against real records
We implement against your actual product data, with an evaluation suite scored for correct enrichment and for correct refusal — a value the source does not support has to fail the suite, not pass it quietly.
- Working prototype
- Evaluation suite
- Hold-and-review rules
Deploy with monitoring
Production rollout integrated with your commerce platform and PIM, with audit logging, reporting on hold rate alongside publication rate, and a runbook so your merchandising team runs the system independently.
- Production deployment
- Audit trail & monitoring
- Operator runbook
Not sure where to start?
A 30-minute call is usually enough to find your highest-value use case
Talk directly to our founders and PhD AI engineers. We will show you real results from 30+ agentic projects and walk through how to apply them to your own catalog, search and order workflows. Every example is something already running in production.
Independence
How we help you stay independent
Your team owns what we build. We work on open-source foundations, and the agent logic, the integrations and the evaluation harness transfer to you at the end of the engagement.
Technological sovereignty
We have delivered systems that run with no connection to a big-tech platform: sovereign AI, engineered in Europe.
Small language models
Smaller models keep token costs predictable in day-to-day operations and let the system run on your own internal or on-premise infrastructure.
Open source
We build on open-source software as contributors and as an official Pydantic implementation partner, so the stack stays inspectable and your team keeps the source.
FAQ
Agentic AI in e-commerce and retail, answered
What is agentic AI in e-commerce and retail?
Agentic AI in e-commerce and retail is software that plans and carries out multi-step commerce operations across systems — enriching product records, resolving a configuration, verifying and routing orders, reordering inventory, and handling inbound support — acting on its own where the data supports it and escalating to a person when a decision genuinely needs one. Unlike a static recommendation engine or a rules-based automation, an agent reasons across live catalog, browsing and purchase signals and adapts as those signals change.
Our on-site search already works. Why would we change it?
It probably works for the queries you test it with. Baymard Institute's search benchmark (170+ sites and apps, more than 10,000 individual performance assessments, updated April 2026) found that 56% of sites do not adequately support how users actually search. The pattern is the tell: only 12% of sites struggle with an exact product name or model number, but 43% fail on queries that describe a use case and 39% on queries that describe a feature. Search handles the customer who already knows the SKU and fails the one who is describing a need.
How is this different from the recommendation engine we already run?
A static algorithm applies the same rules regardless of context. An agentic system reads individual behavior, purchase history, and real-time browsing signals, refines its outputs as those signals evolve, and coordinates across inventory, pricing and fulfillment rather than scoring one event in isolation.
Which e-commerce workflows can agents actually handle?
Product data enrichment against your own attribute schema, on-site search that resolves intent rather than matching strings, guided order configuration where the valid combinations are too numerous for rules, order verification and exception routing, demand forecasting for replenishment, and inbound support grounded in your catalog.
Can an agent really handle a catalog with millions of combinations?
That is precisely the case where rules stop working and an agent starts paying for itself. For Mixam we built a three-agent product advisor operating across more than a billion product combinations with 15 tools, reaching a 95.4% success rate in workflow results — a figure their Digital Product Manager confirmed on record against an original target of 80%.
Do we have to replatform to deploy these agents?
No. Retailers and e-commerce companies usually have strong technical foundations and established data infrastructure, and we design agents to integrate with the commerce platforms and tools already in place, without forcing teams to replatform or abandon existing workflows.
Where should we start if agentic AI is new to us?
With a readiness assessment. We map current processes, identify the highest-value automation opportunities, and produce a deployment roadmap grounded in what the technology can realistically deliver today — then scope the first workflow into production before expanding.
What happens when the agent does not have enough product data to answer?
It holds the record instead of publishing a gap. A missing attribute caught before publication costs a review; the same gap caught after publication costs a return, and the customer's trust in the listing. We design the incomplete-data path explicitly, and report on how often it fires, because a system that never declines to publish is not being careful, it is guessing.
Start with one workflow
Start with one catalog, search, or order workflow
A 30-minute call maps your attribute schema, the queries you are losing today, and a realistic path to an agent your merchandising team will actually trust.