AI Engineering & AI Agency
I help you ship real AI features, RAG systems, AI agents, and self-hosted or gateway-fronted LLMs, with the infrastructure, cost controls, and reliability to run them in production. Something your users can actually depend on.
Sound familiar?
- ✕You have a slick AI demo but no idea how to run it in production
- ✕LLM API costs are unpredictable and climbing
- ✕You need a RAG system over your own data and it has to be accurate
- ✕You want to self-host or gateway models for privacy or cost reasons
- ✕An AI agent works 70% of the time and that's not good enough to ship
- ✕You need GPU/model serving infra that doesn't fall over under load
What you get
- ✓A RAG or agent system that's accurate, observable, and ready to ship
- ✓LLM cost controls via caching, routing, and a gateway out front
- ✓Self-hosted or hybrid model serving with sane autoscaling
- ✓Evaluation and guardrails so quality is a number you can watch
- ✓Infrastructure your team can operate after I leave
The gap between an AI demo and a production AI feature is mostly infrastructure
A weekend prototype that calls an LLM is easy. Something your users depend on, accurate and fast and cost-controlled and observable and still standing when a provider has a bad day, is an infrastructure problem as much as an AI one. That intersection is where I spend my days.
What I help with
- RAG systems. Retrieval pipelines with measured accuracy, sane chunking, freshness handling, and guardrails against hallucination.
- AI agents. Tool calling with validation, retries, fallbacks, evaluation harnesses, and a human in the loop where it matters.
- Self-hosted and secure LLMs. Model serving with autoscaling, on your infra or a private cloud, for privacy and cost.
- LLM gateways. Routing, caching, rate limiting, cost tracking, and multi-provider fallback in front of your models.
- MLOps and LLMOps. The deployment, monitoring, and cost discipline that keeps AI features running once the launch buzz fades.
Why me for this
Infrastructure for AI is my day job at TrueFoundry. LLM gateways, model serving, and GPU infrastructure at production scale. You get someone who understands the model layer and the cluster underneath it, rather than one without the other.
How an engagement works
- Scope. What you’re building, your accuracy, latency, and cost bar, and the realistic path to it.
- Architect. The simplest design that clears the bar. RAG or fine-tune, hosted or self-hosted, gateway or not.
- Build and measure. Ship it with evaluation and observability so quality is a number you can point at.
- Hand off. Infra and runbooks your team can operate without me.
Frequently asked questions
What does it take to get a RAG system production-ready?+
Demos fall over on the unglamorous parts. Chunking and retrieval quality, evaluation so you can actually measure accuracy, handling stale or conflicting data, latency, cost. Production-ready means a measured retrieval pipeline, guardrails against hallucination, observability, and a serving layer that scales. I work exactly the gap between "it worked once" and "users depend on it."
Should I self-host LLMs or use an API?+
It depends on volume, privacy, and latency. APIs win on time-to-market and frontier quality. Self-hosting wins on cost at scale, data privacy, and control. A lot of teams end up on a hybrid behind an LLM gateway that routes by cost and capability. I model your actual usage and give you the honest break-even instead of a dogmatic answer.
What's an LLM gateway and do I need one?+
It's a proxy in front of your models that handles routing, caching, rate limiting, cost tracking, and fallbacks across providers and self-hosted models. If you're spending real money on LLMs or running more than one model, a gateway pays for itself fast in cost control and reliability. This is squarely my day-job domain.
Can you build AI agents that are reliable enough to ship?+
I build agents with the unglamorous parts that make them trustworthy. Tool and function calling with validation, retries and fallbacks, evaluation harnesses, observability, and a human in the loop wherever a mistake is expensive. The goal is a measured reliability number you're comfortable putting in front of users.
related work
Where I’ve done this
Shipped a self-hosted RAG assistant with an LLM gateway
A team with a promising RAG demo couldn't ship it. Accuracy was unmeasured and API costs were unpredictable. A measured pipeline behind an LLM gateway with hybrid serving made it production-ready and ~70% cheaper.
~65% lower monthly LLM API spendCut a company's LLM API bill by ~65% with a gateway
A product team was sending every request to a frontier model and watching the bill climb past usefulness. A gateway with caching, model routing, and prompt trimming cut spend ~65% with no drop in output quality.