Case studies, full page.
Each case study opens the operating problem, implementation, rollout controls, and results. Small businesses get proof they can promote. Larger teams get the engineering depth to judge the work.
Turning a lean operator into an AI-native business without hiring a full AI team
The first win was not a giant transformation program. It was a focused operating system: find the highest-leverage workflow, ship it, train the team, and turn the result into growth proof.
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Building a price and promotion system that moved margin and demand together
A margin problem was not treated as a dashboard problem. The work became an operating layer for price, markdown, wholesale, and seasonal campaign decisions.
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Turning marketing mix modeling into an executive planning system
The goal was not another attribution report. It was a reusable planning layer that could forecast demand, allocate spend, and explain the tradeoffs behind every plan.
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Propensity and throughput models for a lending operation that needed cleaner signal
Classical machine learning still wins when the problem is ranking, routing, and cycle-time reduction. This build improved lead quality and shortened operational crossing time.
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A multi-agent orchestration layer with evals before autonomy
The work centered on making agents cheaper, more reliable, and easier to test before they touched real customer workflows.
Read case studyA 25‑minute call to pick the first workflow.
Bring one slow, manual, or expensive workflow. We will pressure test the use case and tell you the cleanest next step.
- Where the workflow is stuck today
- What data, tools, and people it touches
- Whether it fits a partner retainer, sprint, or pod