The difference between a cool demo and a production-grade AI system is craft: prompt architecture, RAG, evals, guardrails and operations. Anthropic Claude, OpenAI, Vercel AI SDK, LangChain – platform-agnostic.
The organizations we work with typically face at least one of these.
The model works in a Jupyter notebook but crashes on real data, real edge cases and real compliance requirements.
Your agent answers correctly 7 times out of 10 – and 3 times wrong. Without evals and guardrails it's not reliable.
Someone wrote a long prompt that works – sometimes. Nobody understands why, nobody dares change it.
The API bill grows as features are added. Latency makes end-users lose patience.
A selective portfolio where senior expertise makes the biggest difference.
From ad-hoc prompts to structured, testable systems: roles, instructions, format specs, edge case handling, version control.
Retrieval-augmented generation that actually finds the right context: embedding strategy, chunking, reranking, source attribution.
Agents with tool-use, decision logic and controlled capabilities – LangGraph or Vercel AI SDK depending on your stack.
Measurable quality: golden datasets, eval suites, regression tests, dashboards on latency, cost and success rate.
Input/output validation, jailbreak protection, PII handling and clear boundaries on what the agent can do.
Monitoring, cost tracking, prompt versioning, A/B testing, gradual rollouts. Your AI system runs without surprises.
A clear process from first conversation to delivered result.
Free initial conversation about your use case, data and success criteria.
A working proof-of-concept with real data in 1–2 weeks.
Full implementation with evals, guardrails, monitoring and documentation.
Your team owns the system. Documentation, training and ongoing support.
Transparent pricing with no hidden costs. All prices exclude VAT.
A working prototype with your data in 1–2 weeks. De-risk before investing further.
Full production-ready AI system with evals, guardrails and monitoring. Fixed price per defined scope.
Ongoing AI strategy, architecture reviews and team mentoring. Flexible commitment.
Answers to the questions I hear most often.
Yes. I am platform-agnostic – Claude, GPT, Gemini, open-source models. The choice depends on your use case, data and compliance requirements.
Through evals (golden datasets, regression tests), guardrails (input/output validation), and observability (latency, cost, success rate dashboards).
Yes. I start with an audit of what exists, what works, and what needs to change. Common issues are prompt fragility, missing evals and unmanaged costs.
All solutions are designed with GDPR/Swedish compliance in mind. Models can be deployed on Azure/AWS within EU region. No data leaves your control.
Yes – with controlled tool-use, approval steps for critical actions, and clear boundaries. The agent operates within guardrails you define.
Next step
Do you have an ambitious idea or a technical decision where it pays to think right from the start? Get in touch – no obligation.
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