Founder & CEO, Luminik · Oslo, Norway
3x technical founder. Today I'm building Luminik, and Alfred in the open.
Impact
Built a workforce-analytics platform used heavily in PE and growth-equity due diligence, as venture CTO, and took it from zero to $3.6M ARR in 15 months. I owned the data infrastructure it ran on, and moved the warehouse to Snowflake as the analytical load grew.
Founded a multi-agent platform that connects B2B event spend to revenue. I closed the first contract at $48K before the product existed, by running the workflow with scripts, spreadsheets and decks.
From 43,000 registered attendees to 1,840 ICP matches and meetings booked on the floor. Lead-to-opportunity reached 8%, up from 1.3% the year before, roughly 6x.
Work
Founder and operator across GTM tech, private equity, field service, and investment management.
Founder
A Snowflake cost-optimization tool. It was used by one of the global top-3 consulting firms, and the conversations while building it surfaced the bigger problem that became Luminik.
Venture CTO
Took a workforce-analytics SaaS platform, used heavily in PE and growth-equity due diligence, from concept to $3.6M ARR in 15 months. Owned product, architecture, and the engineering team, and built the data infrastructure: a medallion warehouse on Snowflake with a Cube.js semantic layer, plus Lightcast and BLS taxonomies for comparable workforce data.
Co-founder & CTO
Field-service management SaaS for maintenance companies across Europe. Built and launched the MVP solo in 3 months, raised a $2.7M seed, and scaled the team to about 15 across five countries.
Same booth, 6x the pipeline. At RSA, lead-to-opportunity went 1.3% to 8%.
Approach
A few principles I keep coming back to.
I work next to the people who have the problem, so the product comes from what they do day to day.
The real work is the cases that break: evals, a judge I have calibrated, and adversarial tests, so I know how a system behaves before customers see it.
I write the code, review it, and read what the system produces in the wild. This kind of work does not lead well from a distance.
System
Skills
AI-native, and the systems under it. Drawn from what I'm shipping at Luminik and Alfred.
Multi-agent orchestration, autonomous agent fleets (Alfred), LangGraph, MCP, tool use and planning
Vector DBs, embeddings, hybrid and semantic search, context engineering, agent memory
LLM-as-judge, eval harnesses, benchmarking, red-teaming, adversarial tests, safety gates
Tracing (Langfuse), OpenTelemetry, cost and latency control, failure handling, durable execution
RL and reward design, prompt and context engineering, fine-tuning, multi-model routing (Vertex, OpenAI, Anthropic)
Microservices and SOA, event-driven and async services (asyncio, aiohttp), FastAPI, Django, Spring Boot + Kotlin, Kubernetes, Kafka
Next.js, React, TypeScript, React Native and Expo, Tauri, end to end from data model to UI
AWS, Terraform, Snowflake, data pipelines and warehousing, Postgres, CI/CD
Writing
Hands-on writing on AI-native engineering, evals, durable workflows, and the operator side of building companies.
How I run AI adoption for a 200-person org: measuring against a baseline, keeping devs autonomous while checking outcomes, why token spend is the wrong number, governance as config, PII guardrails, and tooling you can swap as the ecosystem moves. Written three weeks into a live rollout.
An LLM that grades other outputs is an instrument, and an unmeasured instrument is not evidence. The discipline that makes an LLM-as-judge trustworthy in production: deterministic gates first, a cached and provenance-stamped verdict, calibration against human labels with Cohen's kappa, and grading against a written policy.
How to run internal AI enablement as a business unit with its own P&L: find the pains worth automating, gate projects on a number, tie every AI dollar to an initiative, and build standards instead of buying seats.
Contact
If you're building something hard in AI, or hiring someone who has, I'd like to hear about it.