AI Agent Integration

A concrete, narrowly scoped AI agent instead of a vague “AI initiative” workshop. We build the agent, integrate it into your existing tool landscape, and show your team how to maintain it.

What’s included

• Use-case definition with a clear success criterion — what exactly the agent should do, what it shouldn’t, and how you’ll know it’s working
• Model selection: we prefer locally-hosted or EU-hosted models (Ollama, Mistral, EU-hosted providers) where the data sensitivity allows. When cloud models like Claude or OpenAI fit the task better, we discuss the trade-offs upfront — including data processing agreements, region, and data flows
• Integration with existing tools: Slack, email, internal wikis, ticket systems
• Optional: retrieval-augmented access (RAG) over your internal documents — vector store, embedding pipeline, source citations
• Eval suite with real examples so you can measure the impact of model or prompt changes
• Written documentation: architecture, prompts, escalation paths

Use cases we see often

• Support triage: incoming requests classified, draft response with sources
• Internal Q&A: searchable answers across Confluence, Notion, or a file share
• Document classification: route incoming mail by type, urgency, or customer

Who it’s for

SMBs with a concrete, bounded use case where an agent measurably saves time or errors. Not suitable as a generic “we want to do something with AI” initiative.

What’s not included

No model training (fine-tuning, RLHF) — we work with existing models. No ongoing operation; after onboarding the agent is yours, with an optional retainer available separately.