02 Sept 2026
Deciding where the truth lives
Making a company AI-ready starts with agreeing where files go. Four boring folder rules, the one mechanism behind them, and the August I found the same dead rule stated as live fact in six of my own files.
Where I advise
AI, and the data it runs on.
Can your data carry agents? Lineage, entity resolution, governance and quality, judged against what agents actually need. Thirty years of data and architecture behind one question.
See the proof →What I buildSee the full list →
Real systems, real trade-offs.
The credibility layer that separates a consultancy from a CV, and the receipts for learning by doing: every project exists to find what doesn't work as advertised.
Active
Foreman
Foreman keeps AI-written software honest. A feature passes three gates only I sign, and an agent does the work in a sealed room: a hard cap on attempts, escalate instead of guess. The process is public. The judgment stays private, because that is the part nobody can copy.
Flow · Sprint · Expertise PacksActive
DataStream Intelligence
A sustainability benchmarking engine on public emission registers: 8 national PRTRs, 4.8 million rows, row-level lineage, running in Azure. The hard part is not the pipeline, it is entity resolution: plants are named after sites, companies after legal entities, and nothing joins for free. The data has a hard ceiling, and that finding is worth more than the product would have been.
Azure SQL · Python · Public registersIn daily use
Operator toolkit
The small tools that keep a one-person company honest. BurnRate measures real token spend from session logs: 95-98 percent of input was cached context, not fresh work. Voice-driven Time Registration turns spoken sessions into an audit-grade hour log. claudecost (a portable Claude cost dashboard) and perfadvisor (a Windows diagnostic advisor) are two more, built to scratch a real itch first. SentinelPR and ReleaseScribe, two open-source GitHub Actions, round out the drawer.
Python · JSON stores · SQLCipherAdvisoryProof →Track recordPRICESPrices are published, half day to retained. See the price list.Start with an email →
Buy the opinion, not the build-out.
For teams deciding what to do about AI who want a tested opinion instead of a vendor pitch. You bring the decision. I bring what broke when I built the equivalent system myself.
What you get
Advisory day
Half or full day, on site or remote, plans and architecture checked against what has been built and measured, ends in a short written opinion.
Talk
45-60 minutes for engineering audiences, built from real experiments, not a vendor deck.
Proof of concept
A small PoC that answers the one risky question, with numbers, then ends.
Technical / vendor due diligence
An independent read on a vendor, an acquisition, or a build proposal, before budget commits to it.
Where I say no
No implementation contracts, no staffing, no reselling somebody else's software. A scoped PoC to settle a decision is fair game, a team on the ground for six months is not. I'd rather say that now than after the first invoice.
Proof
Measured 85-98% of token spend in long agentic sessions was overhead, then cut it with concrete session-management fixes.
Built a multi-country emissions data pipeline: eight national registers plus the EU-wide E-PRTR, 4.8 million verified rows with row-level lineage.
Run the company on a portable file-based control plane; a voice agent on an 8 GB laptop GPU is one surface. Moved the whole tree across machines and AI subscriptions in an afternoon, nothing lost.
Cut design-review time by roughly 84% with a self-tested expertise agent, and threw out its perfect self-test score after auditing the test.
Let an agent take a data-pipeline change from spec to merged PR overnight, 73 tests green, while humans kept push, merge and declare-done.
Thirty years in technology. Took a Competitive Market Intelligence platform at Valona Intelligence from a Gmail inbox and a spreadsheet to a six-layer, multi-tenant SaaS product serving hundreds of enterprise customers, engineering team from 2 to 28 over eight years without accumulating legacy. That platform is a Leader in the 2026 Gartner® Magic Quadrant™ for Competitive and Market Intelligence Platforms. At Damen Shipyards, turned a solo big-data/IoT initiative into a CIO-sponsored, 30-person program that became its own business unit, Damen Digital. Advised HuurPrijsHulp, an Amsterdam start-up named third most innovative social-impact company in the Netherlands at the 2024 KVK Innovatie Top 100.
Writing
Latest thinking
23 Aug 2026
My code graph does not need a new name
"Graph engineering" is three weeks old and already means four different things. The thing underneath it, a code knowledge graph, already has a name and I have been running one across twelve repos for weeks. A January 2026 paper settles the part worth settling: deterministic graphs beat LLM-extracted ones, and mine is honest about which half of it is which.
16 Aug 2026
Agentic AI does not fail on the model. It fails on the join.
Months of building an emissions benchmark on public registers, and the model was never the bottleneck. Identity was. Coverage was. Lineage was. What a data foundation has to do before an agent's answers mean anything.
Contact
Connect on LinkedIn
Get in touch.
Advisory enquiries, talk invitations, or questions about the tools. I read everything.
Wilco de TreeOwner, ZeroNonsense.dev. Director of Architecture & Software Engineering at Valona Intelligence.