Beaconsfield Labs

BF Labs

Adaptive experimentation, rigorous by design. Built on peer-reviewed research from Cambridge.

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The Team

Founded at Cambridge

We're repeat founders backed by experience scaling products to tens of thousands of users. Our previous startup raised £4M and served 30,000 users — now we're channelling that into something new.

C

Christopher

Co-Founder

Student at the University of Cambridge. Previously co-founded and scaled a venture-backed startup to 30,000 users, raising £4M in funding. Focused on product, growth, and go-to-market.

L

Lucas

Co-Founder

PhD researcher at the University of Cambridge specialising in biostatistics — specifically adaptive and covariate-adjusted randomised control trials. His research is the technical foundation of everything we build.

£4M

Previously Raised

30K

Users Served

2

Cambridge Founders

The Research

Adaptive Experimentation,
Grounded in Theory

Our work is rooted in Lucas's doctoral research at Cambridge — bringing clinical-trial rigour to every domain where decisions are made under uncertainty.

Adaptive Allocation

Dynamically shift traffic towards better-performing variants during the experiment — not after. Fewer users see losing treatments.

Covariate Adjustment

Account for known confounders — device, geography, time of day — that classical A/B tests ignore, giving you cleaner and faster results.

Statistical Guarantees

Every method we use comes with formal error control — Type I error, power, and confidence intervals that hold up under adaptive designs.

Current Focus

Smarter A/B Testing for Marketing

Traditional A/B tests lock you into a 50/50 split — half your traffic goes to the losing variant for the entire experiment. That's lost revenue you can't get back. We fix that.

The 50/50 Problem

Fixed splits send equal traffic to every variant regardless of performance. If variant B is clearly worse, you're burning budget until the test ends.

Adaptive Splits

Our algorithms shift traffic toward winning variants in real time. You still get statistical rigour — but with dramatically less wasted spend.

Less Lost Revenue

By minimising exposure to underperforming variants, companies recover revenue that traditional testing methods throw away.

Traditional A/B Test 50 / 50 split

Equal traffic to both variants throughout the test

BF Labs Adaptive Dynamic split

Traffic shifts to the winning variant in real time

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