AI and LLM integration
Production AI that earns its place: ambient AI, retrieval-augmented generation, agents, and clinical or enterprise AI with the guardrails your domain requires.
The problem
Most AI pilots never reach production
A demo built on a large language model takes a weekend. A system your customers trust takes careful engineering: grounding answers in your data, measuring quality, controlling cost and latency, and handling sensitive information properly.
Teams often stall between the two, unsure whether the problem is the model, the data, the prompts, or the product. In regulated settings like healthcare, the stakes and the scrutiny are both higher.
What I do
Find the use cases worth building
I work with you to separate the AI features that create real value from the ones that only demo well, and to estimate what each will cost to build and run.
Design the system around your data
Retrieval-augmented generation (RAG), agent workflows with tool use, and ambient AI that listens and drafts in the background: I design the architecture, choose the models, and decide what runs where.
Build evaluation in from day one
I set up test sets and automated evaluation so you can measure accuracy, catch regressions, and change models or prompts with confidence.
Handle privacy and safety
For clinical and enterprise AI I design for data minimization, de-identification where it fits, audit trails, human review, and the documentation your compliance team will ask for.
What you get
- AI opportunity assessment with cost and effort estimates
- Reference architecture for RAG, agents, or ambient AI
- Model and vendor selection with trade-offs documented
- Evaluation suite and quality benchmarks
- Privacy, safety, and human-in-the-loop design
- Working prototype or production implementation oversight
How we'd work together
AI work usually starts as a fixed-scope project: an assessment, or a prototype through to a production launch. Ongoing oversight after launch works well as a monthly retainer.
Monthly retainer
Ongoing technical leadership with a set amount of time each month.
Project-based
A defined outcome, a fixed scope, and a fixed fee.
Questions
Which models and providers do you work with?
I'm vendor-neutral. I've built with the major commercial model providers and with open-weight models, and I recommend based on quality, cost, latency, and where your data is allowed to go.
Can AI be used with patient health information?
It can, with the right design: appropriate agreements with vendors, data minimization, access controls, and audit logging. I design for HIPAA and PHIPA requirements and work alongside your privacy officer or counsel, who make the final compliance call.
What is ambient AI?
Software that listens to a conversation, such as a clinical visit or a sales call, and produces structured notes, summaries, or follow-up tasks without the person having to type. It saves time when it's accurate and reviewed, so evaluation and review workflows matter.
Do you build the system or advise on it?
Both, depending on your team. I can design and prototype the system myself, or lead your engineers through building it.
Other services
- Fractional CTO and technical leadership
A senior technical leader in your company a few days a month: owning architecture, the roadmap, hiring, and the conversations with your board.
- Healthcare software and EHR integrations
HIPAA- and PHIPA-aware architecture, HL7 and FHIR integrations, and connections to labs, pharmacies, and e-prescribing networks.
- Software architecture and cloud
Architecture that fits your stage and scales with you: system design reviews, cloud strategy, reliability, security, and cost control.
Move your AI idea from demo to production
Tell me what you're trying to automate or improve, and I'll tell you honestly whether AI is the right tool for it.