AI Integration

Put Your Product Inside the AI Tools Your Customers Already Use

The question is no longer whether to add a chatbot. It's whether Claude, ChatGPT and your customers' agents can work with your product directly. We build the MCP servers, typed tools and guardrails that make that safe, plus the assistants, RAG pipelines and automations that run on top. We build on leading models through their APIs, so there's no model training and no data-science team to hire.

Book a Discovery CallDescribe your use case
ClaudeConnected: your product (MCP)
Book the client who messaged this morning for a balayage tomorrow at 14:00 and send her a deposit link.
find_client recent_messages: todaycheck_availability service: balayage, date: tomorrowcreate_appointment awaiting approval

Ready to book: balayage, tomorrow 14:00 to 15:30, 95 € with a 30 € deposit. The slot is free and the client has no other booking that day.

Approve and I'll create the appointment as pending confirmation and text the deposit link. It confirms itself when the deposit is paid.

Booking and payment tools are available because this session is a shop admin. An artist's assistant would not see them.

What we build

Six ways AI ends up inside a real product

MCP servers for your product

Expose your product's data and actions as typed, permission-aware tools over the Model Context Protocol, so any MCP client (Claude, ChatGPT, Cursor, internal agents) can use it. One integration, every assistant.

"Show me tomorrow's bookings and which deposits are still unpaid."

Agents that act in your systems

Agents built on tool calling and orchestrated with LangGraph: create tickets, update records, trigger workflows, with allowlisted actions, approval steps and full audit trails.

"Open a ticket for the failed payout, attach the Stripe event, and notify finance."

RAG assistants over your documents

Support and sales assistants grounded in your documentation, contracts and policies through retrieval, with sources shown and a handoff to a person when confidence is low.

"What does the enterprise contract say about data residency?"

AI features inside existing products

Summaries, smart search, drafting, classification and recommendations added to the app you already have, with usage and cost controls built in.

"Draft a listing description from these five photos and the spec sheet."

Workflow automation

n8n and custom automations that connect your tools and use models to classify, extract, summarise and route, from inbound email to CRM updates.

"Route billing emails to a Stripe lookup and summarise the rest for support."

AI readiness for your API

An audit of how an agent would experience your product: tool boundaries, permissions, rate limits, idempotency and what to expose first. Delivered as a plan you can hand to any team.

"Which of our endpoints should become tools first, and what's missing from them?"
How it works

One server, every assistant

We expose your product as typed tools over the Model Context Protocol. Claude, ChatGPT, agents and your own chat UI all call the same tools, under the same permissions, and get the same numbers your dashboards show.

  • A working MCP server or AI feature, deployed in your infrastructure
  • Tool-level permissions matching your product's roles and plans
  • An evaluation set of real questions, run before launch and on every change
  • Rate limits, cost caps and usage dashboards per tenant
  • Documentation of tools, prompts, data flow and model choices
  1. 01Your product
    • Database and read models
    • Existing APIs
    • Permissions and plans
    • Documents
  2. 02Typed tools
    • MCP server
    • Permission-aware tool list
    • Validated parameters
    • Aggregate outputs
  3. 03AI clients
    • Claude
    • ChatGPT
    • Cursor and IDEs
    • Your own agents and chat UI
  4. 04Guardrails
    • Role gating
    • Allowlisted actions
    • Rate and cost limits
    • Evaluation set
    • Audit log
  5. 05Where it shows up
    • Customer's assistant
    • Internal agents
    • In-product chat
    • Automations
The shape of every MCP server, agent and assistant we ship.
Guardrails

Safety is tool design, not prompt engineering

  1. 01

    Typed tools, not raw queries

    The model calls functions you wrote. It never writes SQL or touches a table.

  2. 02

    Permissions in the tool list

    Each user sees only the tools their role and plan allow; sensitive fields are withheld, not hidden by a prompt.

  3. 03

    One definition per metric

    Numbers an assistant quotes are computed the same way your product computes them.

  4. 04

    Allowlisted actions with approval

    Agents can only do what you've enabled, and risky actions wait for a human.

  5. 05

    Evaluation before launch

    A set of real questions with expected answers, run on every change.

  6. 06

    Limits and audit trail

    Rate limits, cost caps and a log of every tool call, per tenant.

Proof

AI Integration work we've shipped

All case studies
MarketplaceFrance

An in-app AI assistant for every role in a beauty marketplace: shop admins, artists, clients and the operations team

A role-aware assistant built on Claude with typed tools over the marketplace API. It books and confirms, collects deposits, drafts stories and daily summaries, suggests rebookings and helps clients reserve, with the tool list derived from the active space and approval before every write.

  • 5roles, each with its own tool list
  • 57V2 to V4 stories that specify agent behaviour per role
  • Claude
  • Python
  • AWS Lambda
  • API Gateway
FAQ

AI Integration questions, answered

What is an MCP server, and do we need one?

The Model Context Protocol is the open standard AI clients use to discover and call tools. An MCP server lets your customers use your product from inside Claude, ChatGPT or any agent, under their own permissions, without you building and maintaining a chatbot. If your customers are already asking an assistant about data that lives in your product, you need one.

Isn't this just a chatbot that writes database queries?

No, and we'd steer you away from that. Letting a model generate SQL means sanitizers, prompt injection risk and numbers that don't match your dashboards. With typed tools the model calls code you wrote, every metric is computed the same way your product computes it, and permissions are enforced in the tool, not the prompt.

Do you train custom AI models?

No, and most products don't need to. We build on leading models like Claude and GPT through their APIs and make them useful with your data through tools, retrieval and good design. It's faster, cheaper and far easier to maintain.

Is our data safe?

Tools return only what the task needs and only what the user may see; sensitive fields can be withheld by role. We use the providers' business APIs, which don't train on your data by default, and keep an audit trail of every tool call.

What does it cost to run?

Model usage is billed by the provider per request; MCP servers add almost nothing because the heavy lifting is ordinary API calls. We estimate running costs in the proposal and add caching, limits and model selection to keep them predictable.

Have a project in mind?

Tell us what you're building. You'll get a straight answer on scope, timeline and cost, usually within one business day.

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