AI Engineering — United States
AI engineering for US companies — from prototype to production.
The US has the largest AI market in the world and the highest concentration of AI talent. It also has the largest gap between promising prototypes and production systems that actually ship. Most US companies have the right use case. The constraint is production engineering: evaluation frameworks, cost routing, and the reliability that turns a demo into something a real user can depend on.
What we deliver
- Production RAG pipelines with evaluation, cost routing, and monitoring
- Multi-agent workflow automation for operations and customer-facing products
- LLM integration for SaaS products and internal enterprise tools
- AI cost modelling — from demo-scale to 50,000+ daily active users
- Evaluation frameworks and quality monitoring for production AI systems
- Full-stack AI product development, architecture through launch
The prototype-to-production gap
The most common US AI story in 2026: a team builds a prototype using the OpenAI API. Leadership approves a production budget. Eighteen months later, the system still isn't live, costs have exceeded projections, and the original team has been replaced.
The problem is rarely the model. The four failure modes are evaluation (no way to know if the system is working), cost at scale (GPT-4 rates that work for 50 users cost $40–100K/month for 50,000), reliability (what happens when the model fails), and observability (no logging, no dashboards, no alerts).
We've built production AI systems across this specific gap. The companies that move fastest are those that define the evaluation framework before they start building — not after.
How we work
Use case and cost scoping
Before architecture, we model what this costs at your target scale. 50,000 daily users at GPT-4 rates vs. routed-model rates is a 10x cost difference. You should know this before you commit.
Evaluation framework
A representative query set, scoring criteria, and a baseline. This is how we know if the system is working, and how you'll know if a model update degrades quality three months after launch.
Production build
Retrieval architecture, model routing, streaming, monitoring, fallback handling, and observability from day one. The engineering that handles real production load.
Handover
Your team owns it. Full documentation, operational runbooks, and ongoing availability. We design for your engineers to extend the system, not dependency on us.
Related insights
AI Engineering — United States
Stuck between prototype and production?
Tell us what you're building and where it's stuck. We'll tell you what the production architecture needs to look like, what it will cost at scale, and what we'd expect it to return — before you commit.
Start the conversation