AI & agentic commerce development
Make your commerce platform usable by AI systems — through APIs, MCP servers and controlled tool interfaces that let assistants and agents act inside your business rules, not around them.
What we build
MCP servers for commerce
Model Context Protocol interfaces that expose products, availability, pricing, carts, quotes and order status to AI assistants as explicit, permissioned tools.
Commerce APIs for agents
Clean, well-documented, rate-limited APIs designed to be consumed by machines — with stable schemas, deterministic errors and clear authorisation scopes.
AI-assisted B2B ordering
Let a buyer ask for a part by description, vehicle, specification or previous order and get a validated, priced, in-stock line item back — the slowest step in B2B ordering, removed.
Guardrails and business rules
Customer-specific pricing, credit limits, assortment visibility and approval rules enforced server-side, so an agent can never do more than the buyer it acts for.
Back-office automation
AI applied to product data enrichment, translation, categorisation, support triage and order exception handling, with human review where it matters.
Discoverability for AI
Structured data, entity-rich content and machine-readable catalogues so AI systems can describe your products and services accurately.
Have a different AI use case in mind? Talk to us about your project
Agentic commerce is an interface problem before it is an AI problem
An agent is only as safe and useful as the surface it is given. The work is defining which operations exist, who may call them, what they validate, and what they refuse — the same discipline as any good API, with much higher stakes when the caller is autonomous.
We approach this as engineers with 30+ years of enterprise delivery behind us and direct experience of EU compliance, GDPR and emerging AI legislation. That means auditability, logging and human oversight are designed in from the first interface.
- Scoped tools
- Explicit operations, least privilege, no open database access.
- Server-side rules
- Pricing and entitlement enforced where the agent cannot reach.
- Auditability
- Every agent action logged and attributable.
- Human in the loop
- Approval steps for value, risk or exception thresholds.
- Evaluation
- Test suites for agent behaviour, not just unit tests.
- EU compliance
- GDPR, data residency and AI Act readiness considered upfront.
Built on three decades of enterprise integration, not on hype
Agentic commerce works when the underlying commerce APIs, product data and business rules are already sound. That is the part we have been doing since 1992 — across energy, manufacturing, retail, finance and research organisations — and it is why we treat AI as a new consumer of a well-built system rather than a replacement for one.
Common questions
What is an MCP server, in commerce terms?
It is a standard way to hand an AI assistant a defined set of tools — search products, check stock, create a cart, request a quote — instead of letting it guess at your APIs. You decide exactly which operations exist and who may call them.
Is this safe for B2B pricing?
It is, when pricing and entitlements are resolved server-side against the authenticated account. The agent asks; your system decides. Nothing customer-specific is ever exposed to the model as raw data.
Do we need this now?
The honest answer is that it depends on your buyers. For B2B distributors with complex catalogues the productivity case is already real. For others, the sensible first step is making the commerce API clean enough that adding an agent interface later is a small project.
Let’s talk about your commerce platform
Tell us what you are running today and where it is holding the business back. We will come back with an honest read on the architecture, the integration work involved, and what a realistic first phase looks like.
