ChatGPT only answers — but does not act
You have a ChatGPT license, staff use it. But they still have to do the tasks themselves.
We build multi-agent systems that don't just answer — they act. Research, enter, forward, escalate. With real tools, real memory, real responsibility. GDPR-compliant, productive in 8–16 weeks.
You have a ChatGPT license, staff use it. But they still have to do the tasks themselves.
Research → analysis → entry → forwarding — daily, multiple times, hours per process.
n8n workflows are rule-based. Tasks requiring judgment need more — they need reasoning.
"AI does what on its own?" — without safeguards, that would be dangerous. With the right architectures, it's controlled.
Not "ChatGPT with internet" — but an agent system that operates your specific tools, works with defined memory, and lets critical actions be approved.
We build with frameworks like Claude Agent SDK, LangGraph, n8n, and custom orchestrations. Tool use for API calls, database access, email, CRM. Memory architecture distinguishes short- and long-term. Human-in-the-loop for critical decisions.
Implementation in 4 phases: discovery, architecture (clear safeguards), building in iterative sprints, handover with training.
Which tasks? Which tools? Which safeguards? Risk analysis.
Week 1–3Agent topology, tool inventory, memory strategy, permission model, escalation paths.
Week 4–6Iteratively in 2-week sprints. Testable with real data after each sprint.
Week 7–14Training, playbooks, escalation procedures, 90-day hyper-care.
Week 15–16Task: "How do competitors X, Y, Z position themselves?" — agent researches the web, analyzes, creates a structured report. Automated weekly.
Classifies incoming emails, suggests answers, forwards, escalates complex cases. With approval step.
Optimizes daily appointments, suggests routes, documents escalations. Human decides on conflicts.
Checks incoming contracts against compliance rules, flags risk clauses, creates a structured review report.
A chatbot answers questions — structured or with RAG, but it answers. An AI agent acts: it researches, calls APIs, writes to databases, escalates to humans, plans multi-step tasks. Tool use and function calling are the key capabilities. A voice agent is a special form with audio input/output.
As safe as their architecture. We build with: sandboxing (agent can only act in its environment), permission layers (minimum required rights), human-in-the-loop safeguards for critical actions, complete audit logs (every action is traceable). Default strict, optionally gradually relaxed based on trust.
More than a simple chatbot. Multi-agent systems are significantly more complex in architecture, testing, and security. Fixed prices from Phase II after individual discovery. Cost factors: number of agents, tool integrations, security level, model choice (local open-source models drastically reduce operating costs).
8–16 weeks for a first productive version. Complex multi-agent systems with many tool integrations, regulated industry, or special security requirements can take up to 6 months. Discovery is always 3 weeks — then the realistic schedule is set.
Claude (Anthropic) and GPT (OpenAI) for reasoning-heavy agents — these are currently leading for tool use and multi-step planning. Local open-source models (Llama, Mistral, Qwen) for sensitive data or high-frequency tasks. In practice, hybrid architectures are the rule — the right model choice per subtask.
That's exactly what the safeguards are for. Critical actions require human approval — you see in advance what the agent intends, and can stop or correct. Audit log documents every decision, so errors are traceable and correctable. Also: agents are tested before being deployed productively — we don't do "live experiments" on your data.