AI agents that ship. Not demos.
Production AI agents that automate document processing, internal Q&A, research, content creation, and customer-facing workflows. Well-scoped, using the tools you like.
What an AI agent actually is
An AI agent is a piece of software that uses an LLM to take actions inside a defined workflow. It reads inputs, decides what to do next, calls tools (a search API, your database, an email service), and produces a structured output.
Agents work in production when they're scoped to a single, well-understood job: classify this document, summarise this meeting, generate this campaign brief, route this support ticket. They fail when asked to be general-purpose assistants without bounded context.
We build the first kind. Our agents handle one job, do it consistently, and get measured against deterministic baselines so we know they're actually adding value.
What we build
Document processing agents
Invoices, contracts, intake forms, RFPs. Agents extract structured data, classify, route, and escalate exceptions. Replaces manual data entry without making the data wrong.
Research & content agents
Brief generation, market research, competitive analysis, content drafts. Agents that read the right sources, cite them, and produce structured outputs your team can edit.
Customer-facing agents
Support triage, lead qualification, RFP response drafts. Agents that handle the first pass and hand off cleanly when they hit boundaries — never silently bullshitting.
Internal ops agents
Meeting note → CRM updates, calendar coordination, knowledge base maintenance. Agents that take the boring, predictable work off your team.
Multi-step workflow agents
Agents that orchestrate across systems: read from email, query a database, call an API, write to a doc. Built on n8n, LangChain, or custom code depending on what fits.
How we build agents
Discovery: scope the job
What exactly should the agent do? On what inputs? With what tools? Most agent projects fail because this scope is fuzzy. We pin it down on paper before building.
Build: deterministic scaffolding first
We build the workflow as much as possible without the LLM, then plug LLM calls in only where they add unique value. Cheaper, more predictable, easier to debug.
Evaluate: golden test sets and structured eval
Every agent gets a test set we can run on every model change. Quality is measured, not vibes-checked. Drift gets caught.
Deploy: monitored, with human-in-the-loop where needed
Production with logging, alerting, and a clear handoff path when the agent is unsure. EU AI Act compliant by design.
Pricing
A single-purpose production agent typically costs €15–80K depending on the complexity of inputs, the number of tools it needs to call, and the integration surface. Includes evaluation framework, monitoring, and post-launch tuning window. We can usually start with a 1-week proof in your stack for €5K.
Built and shipped
Marketing Automation Agent
10× campaign brief output. Reads brand guidelines, target audience research, and past campaigns; produces structured briefs the marketing team edits and ships.
Lease Document Analyzer
Extracts key terms, financial obligations, and risk flags from lease documents. Cuts review time from hours to minutes; humans validate edge cases.
Document Classifier
Auto-tags and routes incoming project documents to the right folder and team member. 50,000+ documents processed.
Common questions
Pair with
Have a workflow that screams 'AI agent'?
Tell us what you're trying to automate. We'll tell you within one business day whether an agent is the right answer — and if not, what is.