Data-driven scaling
without the buzzword tax.

Methodology

How strategic growth actually moves at SFHL.

1. 01

   Outcome lock + revenue thesis

   We start with the financial outcome you want — incremental ARR, contribution margin, LTV uplift — and work backwards. If the data + model can't credibly move that meter, we say so and recommend you don't spend the budget. No-go is a valid outcome of the first conversation.

2. 02

   Data audit + warehouse hygiene

   Most growth-analytics projects fail at the data layer, not the model layer. We audit what you have, fix the warehouse hygiene (event taxonomy, identity resolution, late-arriving data, dimensional modelling) before any model gets trained. Boring step, biggest leverage.

3. 03

   Pick the right tool — model, agent, or rule

   Half the problems we see don't need ML. They need a deterministic rule engine and a dashboard. The other half need an LLM agent that can take action. The third half need a real model. We pick honestly. Most ROI comes from the cheap rules, not the expensive models.

4. 04

   Build · deploy · instrument

   Local LLMs on Ollama where data sensitivity matters. Cloud LLMs for scale. Custom models on Python where the IP is the model. Every deployment ships with an instrumentation surface so the next iteration is data-driven, not vibes-driven.

5. 05

   Experiment cadence + revenue review

   Quarterly revenue reviews tied to the meter we locked in step 1. Two-week experimentation cadence between reviews. We retain on this loop because it's where the compound value sits — the loop, not any one experiment.