01
Guided product configuration
An agent walks customers through complex product or bundle decisions in real time, checking compatibility and stock instead of pointing to a static filter.
Agentic AI in retail
Agents that work inside retail and DTC operations .
We build agents that guide complex product decisions, resolve order exceptions, and coordinate inventory across your existing systems. Every action checks live state and logs back to the order record, so operations teams can see what the agent did and why.
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
Most retail AI answers the question and leaves the order unfinished
Bundles, sizing, substitutions during a stockout, an order that arrived damaged: retail and DTC decisions run several steps deep and turn on configuration. A single-turn chatbot can describe a policy; it cannot check live inventory, cross-reference order history, and complete or escalate the task. An operations team will trust order-state access, catalog logic and a defined escalation path, and that is the layer most retail deployments leave out.
every completed step logged against the order
Use cases
Where agents earn trust in retail operations
Workflows where the right action depends on checking live state, not reciting a policy.
02
Order exception & returns triage
Flags damaged, delayed, or mismatched orders, pulls the relevant order and shipment data, and drafts a resolution for review or completes routine cases within set rules.
03
Inventory & reorder coordination
Monitors stock across SKUs and locations, cross-checks lead times with suppliers, and drafts reorder recommendations before a stockout hits the storefront.
04
Post-purchase support, multi-channel
Handles order-status, shipping, and account questions over chat, email, or SMS, reasoning across the order system instead of a scripted decision tree.
The cost of static self-service
Where commerce workflows lose orders today
These numbers come from real shipped agentic AI engagements. Mixam's are direct print-on-demand commerce; the third figure is not retail — it shows the same before/after coverage mechanism in media. Every figure below links to the case study behind it.
Our directly-published retail proof is from print-on-demand commerce. The multi-step orchestration mechanism (live state checks, multi-step completion, human escalation) is the same one general retail and DTC operations need; the third figure below is not retail at all, but shows the same before/after coverage mechanism.
Product combinations navigated alone
1B+
Mixam's customers faced paper stock, print specification and delivery decisions across a billion-plus combination catalog — with static filters and support tickets.
+11.76%
Increase in orders on day 1 of the Australian launch
The published figure is scoped to one market on the first day the assistant was live. We state the scope because a single-market day-one number is not a sitewide annual result.
1% → 100%
Knowledge-inquiry response rate before and after
Not a retail deployment — A bilingual English/Arabic autonomous agent embedded in ARIJ's Moodle environment, answering only from ARIJ's own knowledge base — across 22 countries.
Client results
Proof from production commerce deployments
Our directly-published retail proof comes from print-on-demand commerce. The third case is not retail — it shows the same before/after coverage mechanism in media.
Delivery path
From workflow audit to a production order agent
TriStorm keeps engineering and operations aligned — integration risk surfaced before full build commitment.
Scope the order and inventory workflow
We audit the target workflow, catalog complexity, and existing systems (OMS, PIM, CRM) to see where decisions get made today and where an agent can act versus where it must hand off.
- Workflow audit
- System & data map
- Prioritised use case
Build, test and validate
We build against real catalog and order data, with an evaluation suite scored against your actual product logic and edge cases (stockouts, substitutions, damaged orders) before it reaches a customer.
- Working prototype
- Evaluation suite
- Escalation rules
Deploy with monitoring and support
Production rollout integrated with your OMS and CRM, with monitoring and an order-level audit trail your operations team can review, plus a runbook for independent operation.
- Production deployment
- Order-level audit log
- 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 merchandising and store operations 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 retail, answered
Where does agentic AI actually fit into a retail operation?
In workflows where the right answer depends on checking live state (stock, order history, product compatibility) rather than reciting a policy. Guided product configuration, order exception handling, and reorder coordination are good starting points. We do not deploy agents to set final prices or approve refunds unsupervised; the agent proposes, a system or a person confirms.
How is this different from the chatbot our platform already ships with?
A platform chatbot answers from a script or a knowledge base, one turn at a time. An agent plans across steps — it checks stock, cross-references order history, and completes or escalates a task instead of deflecting. Our order-recommendation build for Mixam guided customers through more than a billion possible product combinations, which a scripted flow cannot do.
What does guided product configuration actually look like in production?
The agent reasons over your catalog and current stock to walk a customer through a complex or configurable purchase — bundles, sizing, substitutions during a stockout, instead of a static filter UI. That is the mechanism behind the Mixam engagement: a multi-agent product advisor with live inventory checked at every step.
Can an agent handle returns and order exceptions without creating new risk?
It pulls the order, shipment, and product data, drafts a resolution, and either completes routine cases within rules you set or routes anything ambiguous to a human rep with the reasoning attached. Nothing ships outside the rule boundary you define upfront.
How do you handle customer and payment data?
Agents read through your existing access layer — they do not get a separate copy of customer or payment data. Integration respects your existing PCI-DSS scope and access controls. We design the data path before we design the agent.
Can this integrate with our existing OMS, PIM, or CRM?
Yes, through your existing APIs and data infrastructure, not a replacement. The Mixam system integrates with the existing catalog and order pipeline using PydanticAI and retrieval over the product data already in place.
What is the realistic timeline to a working system?
A scoped Proof of Value (one workflow, real catalog and order data, a working agent) typically lands in about three weeks. Full production rollout with monitoring follows the same TriStorm phases as any other engagement.
What happens when the agent is not confident, or a case falls outside the rules?
It escalates instead of guessing. Confidence thresholds and rule boundaries route the case to a human rep with the reasoning attached — the same pattern we build for order exceptions and returns triage.
Start with one workflow
Map one order or catalog workflow worth automating
A 30-minute call identifies integration points, catalog complexity, and a realistic path to a working agent your operations team will trust.