Get AI into production — and keep it there
Most funded startups ship an AI demo in weeks. Getting that demo into production — handling real traffic, real failures, real costs, and real investor scrutiny — is a different problem entirely. We help you close that gap without rebuilding from scratch.
Most early-stage AI products are built on a single model chosen by whoever wrote the first prompt. That works for a demo. It doesn't work when you're handling real traffic, getting rate-limited at 2am, or trying to explain your AI risk management to a board or an investor.
The numbers confirm what we see in practice. According to Datadog's 2026 State of AI Engineering report, drawn from production telemetry across more than 1,000 organizations, 70% of engineering teams now run three or more AI models — and most have no governance plan for them. Rate limit errors are the number-one production failure mode. LLM technical debt compounds when teams add models without retiring old ones.
What getting AI into production actually requires
- Model selection for your specific workload — not the most popular model, the right one
- Rate limit and fallback architecture so outages don't become user-facing failures
- Observability: tracing, latency monitoring, cost-per-call visibility, and quality scoring
- Prompt caching and cost controls so your AI budget doesn't surprise you at month end
- Evaluation framework so you know when output quality drifts before users tell you
- Written architecture decision record you own — not locked in our heads or our tools
Why Tristella
John has spent the last several years at the intersection of AI architecture and engineering leadership — advising funded founders on the stack decisions that will either compound or cost them at Series A. We've seen what happens when teams skip this work. We've also seen how fast it can be fixed when someone with the right experience steps in early.
How engagements work
| Engagement | What's included | Fee |
|---|---|---|
| AI Stack Audit | Full review of current models, prompts, observability, cost profile, and fallback architecture. Written findings and prioritized recommendations. (1–2 weeks) | $8,000–$12,000 |
| AI Stack Design & Implementation | Model selection, fallback architecture, observability setup, prompt optimization, and evaluation framework. Written ADR included. (2–4 weeks) | $15,000–$35,000 |
| Ongoing AI Architecture Retainer | Monthly architecture review, model evaluation, cost monitoring, and availability for production incidents. | $5,000–$10,000/mo |
Nonprofits and mission-driven organizations receive a 10–15% discount on all engagements.
Not sure which engagement fits? Start with the AI Stack Readiness Assessment — 8 questions, instant score, personalized report. Or read our guide on what actually changes between a pilot and a production AI system.