Not another chatbot. An agent that acts inside your tools.
We design and deploy AI agents that read your data, perform actions in your business tools and deliver measurable outcomes — not a demo, but an operational building block integrated into your stack.
Plugged into your APIs and databases
Guardrails and human approval
First prototype in 4 to 6 weeks
A chatbot answers. An agent acts.
The difference isn't conversational — it's operational.
Provides answers, redirects to documentation, asks you to do the work yourself. Stuck outside your systems.
Queries your databases, calls your APIs, triggers your workflows, and reports back. You get the outcome, not just a reply.
Why an agent?
The limits of today's solutions, and what a real agent actually solves.
Chatbots stay outside your stack
Classic chatbots reply in text but can't reach your CRM, ERP or internal database. Your teams still have to do all the work behind the scenes.
RAG alone isn't enough
Searching through documents is useful, but it doesn't trigger anything. An agent combines retrieval, reasoning and tool calls to go all the way to action.
Vertical platforms are rigid
Off-the-shelf agent SaaS forces their integrations and their model. Your use cases rarely fit the grid — and you pay per seat.
AI without guardrails is unmanageable
An agent that mutates your data without audit or approval is a liability. We define from day one what it can do, with what approval and what traceability.
Observability is missing
Many agent PoCs run blind. We install logs, replays and metrics from the start so you can actually measure ROI.
You stay locked into one model
If you're tied to a single provider, you're exposed the day pricing changes or the model regresses. Our architecture lets you switch.
What you gain with a custom agent
Concrete operational benefits, measurable sprint after sprint.
Automation of high-value tasks
Quotes, lead qualification, level-1 customer responses, record updates: the agent handles the routine and frees your teams for expertise.
Native integration with your stack
Email, calendar, CRM, ERP, product catalog, internal APIs: the agent plugs into the tools you already have, not a vendor's ecosystem.
Control and guardrails
Per-action permissions, human approval on sensitive operations, dry-run before execution, rollback: you stay in control.
Closed or open-source models, your call
Proprietary models (Claude, GPT) for the best of the state of the art, or open models (Mistral, Llama, Hermes) hosted on your infrastructure or in Europe for sovereignty. We combine based on your case.
Full traceability
Every tool call, every response, every decision: all logged, replayable, auditable. Essential for compliance and continuous improvement.
Measurable ROI
We define KPIs during scoping (time saved, auto-resolution rate, conversions) and track them continuously to steer the project.
Our method
Five steps to go from idea to an agent running in production.
Use case scoping
We start from a concrete workflow, identify automatable tasks, required data and tools, and success metrics. No AI for AI's sake.
Architecture and guardrails
Model choice, tool graph design, permission boundaries, human-in-the-loop checkpoints and traceability. This is the layer that makes the agent trustworthy.
Fast prototype
A working agent on a narrow scope in 4 to 6 weeks, plugged into your real data (dry-run by default for sensitive actions).
Real-world iteration
Your users put it through its paces. We analyze logs, tune prompts, add tools, sharpen guardrails, build the eval suite.
Production and monitoring
Progressive rollout, quality and cost monitoring, drift alerts, and ongoing support to evolve the agent with your needs.
Typical example
A representative case from projects we deliver.
60-person SMB with an 8-person support team. 200 to 300 customer requests per day, 60% of which are recurring (order status, returns, product FAQ, docs).
Support spends most of its time copy-pasting replies and digging information from 4 different tools. Average response time exceeds 24h. Sales can't follow up on warm leads anymore.
An AI agent connected to the CRM, the order back-office and the documentation base. It handles simple requests end-to-end (reply + action) and triages the complex ones for a human.
70% of requests handled without human intervention, average response time down to 8 minutes, support team refocused on high-value cases. First ROI hit at month 3.
The stack we use
A balanced mix of closed models (Claude, GPT) and open-source ones (Mistral, Hermes), orchestrated with open building blocks like n8n and OpenClaw.
Proprietary or open-source models · n8n + OpenClaw orchestration · European hosting available
Claude (Anthropic)
GPT (OpenAI)
Mistral AI
Hermes · Hugging Face
n8n
OpenClaw
Frequently asked questions
Expect 4 to 6 weeks for a first prototype plugged into your data, and 2 to 4 months for a stable production deployment with guardrails and observability. Duration mostly depends on how many tools need to be integrated.
We combine based on your case. Closed models (Claude from Anthropic, GPT from OpenAI) are often state of the art for reasoning and tool-use. Open-source models (Mistral for Europe, Llama, Hermes from Nous Research, Qwen) enable on-premise or European hosting, full privacy and controlled inference costs. Many of our projects use a closed model for the main reasoning loop and an open one for local tasks.
We can host the agent and its data in Europe, anonymize or pseudonymize what goes out to model APIs, and log everything to meet audit requirements. This is scoped into the architecture from day one.
No. The agent sits behind an abstraction layer that lets you switch model or provider without rewriting the logic. You keep the code, the prompts and the data.
Fine-tuning adapts a model to your style. RAG enriches it with your documents. An agent adds the ability to execute actions in your tools. The three combine depending on your need.
Costs split between build (initial development, typically €20k to €60k depending on scope) and run (model calls, hosting, monitoring). We provide a clear cost model from the scoping phase.
We rely on battle-tested open-source building blocks. n8n for declarative workflows and integration with your business tools (CRM, ERP, email, databases). OpenClaw when we want an autonomous self-hosted agent, model-agnostic and privacy-first. MCP (Model Context Protocol) and LangChain/LangGraph for custom development when logic goes beyond a workflow. The choice depends on the level of autonomy and integration required.
Let's discuss your use case
Describe your need and we'll get back to you within 24 to 48h with a first recommendation and a costed scope.
Talk to an expert