Henk van Biljon. Co-founder and CEO.
Henk is the co-founder and CEO of Fount, the insurance-first AI marketing platform. Before Fount, he built actuarial data science systems for large insurance companies, where the same observation kept recurring: insurance marketers are asked to drive growth without visibility into the economics that determine whether that growth is profitable.
About
Fount emerged from MIT's entrepreneurship ecosystem to close that gap, combining agentic AI, marketing technology and actuarial science. Henk writes here about the practical side of that work: unit economics, attribution and causal inference, and what AI agents can and cannot do for insurance marketing teams.
Why are the leads down? The causal growth marketing problem
A simple Monday-morning question - why are the leads down? - still costs an insurance marketer most of their week, because the answer is scattered across systems that were never wired together to show cause and effect. Part 1 of the Marketer Graph series.
Turning marketing into a graph you can search
The structure underneath Quinley Graph: three simple tables of nodes, edges and events, confidence levels on every fact, and a four-lane hybrid search that surfaces the right knowledge from a question asked in plain language. Part 3 of the Marketer Graph series.
Trust, rails and how we know it's right
A connected graph is only useful if the numbers coming off it can be trusted. How Quinley produces answers on governed rails, keeps every figure traceable, proves it is right by re-running reference logic against the warehouse, and gets sharper every week. Part 4 of the Marketer Graph series.
The Marketer Graph for Insurance
How Fount turns the mess of insurance marketing and customer data into a single connected graph agents can reason over, to measure cause and effect and take trusted, governed actions. The overview of a five-part series.
The evolution of Fount's agent harness
Quinley, our marketing chief-of-staff agent, has run on three significant versions: Quinley Base, Quinley Prime and Quinley Graph. The road from one to the next, and what the internal insurance marketing evals show. Part 2 of the Marketer Graph series.
Causal AI and governed action
The structure, the rails and the verification add up to one thing: an agent that can reason from cause to effect and then act on it, under governance, when a single budget move swings millions. Where the Marketer Graph is taking us, and where it still falls short. The finale of the Marketer Graph series.
Pricing a customer the way an actuary prices a policy
Customer lifetime value in insurance is premium times expected policy lifetime times underwriting margin. Treating CLTV as a pricing problem, with vintages, credibility, and margins for error.
UTM hygiene at 80 campaigns: a field report
What it actually takes to impose a UTM taxonomy on dozens of live campaigns across multiple platforms: the failure modes, the naming convention that survived contact, the validator, and the unglamorous backfill that made CRM joins possible.
Why your ad platform numbers will never match your CRM
Ad platforms and your CRM count conversions with different windows, identity models, and clocks, so they will never reconcile to the dollar. How to read the gap, pick a system of record, and stop chasing a match that cannot exist.
The Seduction of CPL as a Metric
Cost per lead optimizes for cheap leads, not profitable policyholders. A unit-economics framework for insurance acquisition that prices leads off lifetime underwriting profit instead.