01
Data Platform
Pipelines, integration, quality, and infrastructure the company can run. Security and cost stay visible.
Data and AI Partnerships
We join a company’s engineering effort and expand what that team can ship. The company keeps the code, the infrastructure, and control of the roadmap.
02
Intelligence Layer
An ontology, a knowledge graph, entity resolution, and embeddings over governed business data.
03
AI Applications
Search, analytics in plain language, and copilots tied to decisions the business already makes.
How the partnership works
- A dedicated lead engineer stays on the backlog.
- A principal architect takes the hard design problems.
- Specialists join for platform, AI, and cloud when the work needs them.
- One AI workstream runs beside the platform backlog. Larger programs are scoped on their own and start only after approval.
What the company keeps
- The code, configuration, infrastructure, and documentation.
- A stack that runs without a make tech license.
- Open standards where they are the practical choice.
- A written record of the system as the work proceeds.
The First 90 Days
Engineering continues while the roadmap is written.
Days 0–30
Stabilize and Map
Current architecture, dependencies, data sources, and ownership. Priority platform work keeps moving.
Days 31–60
Design the Intelligence Layer
Ontology, knowledge graph, and the first AI workstream, chosen by the value of the decision it supports.
Days 61–90
Ship and Operationalize
The first AI capability goes into use, with runbooks, architecture standards, and a 12–18 month roadmap.