AI, taken to production.
POS sales data pre-processed with an LLM into an external workflow.
AI consulting, AI architecture, and fractional technology leadership from engineers who have run AI in production.
Let's TalkThree practices
Build the AI layer, provide direct or indirect leadership support, engage the team you already have.
AI Consulting & Architecture
Strategy that terminates in running software: readiness assessment, adoption path, governance navigation - and then we design and build the system itself, hands-on in your repositories. Workflow orchestration, data pipelines, retrieval, production patterns.
See AI Consulting & Architecture →
Fractional Leadership
Interim and part-time CTO and CPTO work: org design, roadmaps, budget, and the board. Fractional architecture for the platform underneath. Plus M&A technical due diligence on both sides of the table - IP review, team capability, integration risk.
See Fractional Leadership →
AI Dev Team Enablement
Teaching engineering teams to work AI-augmented: onboarding the codebase for LLM engineers, choosing the right working discipline for each change, and measuring whether any of it actually moved the needle.
See AI Dev Team Enablement →Named frameworks, published data
Six frameworks and seven practices - our AI Engineering Operating Model, published in full. Every piece carries a claim you can argue with.
See the AI Frameworks →Recent work
First AI workload at a heavily regulated company
Production AI on AWS Bedrock inside the company’s first AI governance framework. Governance, not models, turned out to be the hard part.
Honing AI-augmented engineering
A B2B SaaS team measured at a constant 3-4K lines a day, with AI autonomy tuned deliberately and stabilization scheduled from the data.
Building a new AI product line
Hands-on AI architecture for an enterprise technology company: embeddings on pgvector, RAG over vector and structured data - plus technical diligence on AI-native acquisition targets.
We build the AI layer into your platform
A 3-6 month build for a company whose content and data needs an AI capability - delivered by a small embedded team, ending in a system your own engineers run.
Ingest
Your content and data, pipelined in, broken down, enriched with metadata.
Secure
Security and access patterns applied to the data before the model sees it.
Orchestrate
The AI work itself - orchestrated LLM pipelines, deterministic where it counts.
Inject back
Results pushed back into your platform, where your engineers extend them.
Bring us a real problem.
You get engineers who have shipped AI to production and still write code every week. Describe what you’re trying to ship and we’ll tell you whether AI belongs in it, where it will fail, and what production actually requires.
Let's Talk