AI That Works After the Demo

From strategy to production and beyond. We help you figure out where AI creates real value, design the architecture, ship the product, and build the feedback loops that make it smarter over time.

What We Do

Strategy, architecture, shipping, and continuous improvement. Pick what you need, or let us take it end to end.

AI Strategy

The LLM is the easy part. Understanding your problem is the hard part. We audit your workflows, identify the highest-impact opportunities, and decompose the problem at the right resolution. Then we prototype the top candidate so you can validate before committing.

AI Architecture

Design AI systems that work in production, not just in demos. Model abstraction so you can swap providers with config, not code. Prompt versioning, observability from day one, and clean interfaces between components. Architecture that makes experimentation cheap.

End-to-End Product Shipping

We build the system around the LLM, not on top of it. Domain logic encoded in schemas, evals that target real failure modes, and observability baked in. Not a prototype handoff. A working system in production.

Data Flywheel

Make your existing AI systems smarter over time. We build feedback capture, pipelines that surface failure patterns by root cause, and evals generated from real usage. If your system can't tell you what mistakes it makes most, it's not a flywheel. It's a log.

The SLS Process

We build AI systems that are honest, secure, and designed to improve over time.

1

Discover

We audit your workflows, identify where AI creates the most value, and prioritize by impact and feasibility. Then we prototype the top candidate so you can validate the approach before committing.

2

Design

Blueprint the production system: models, pipelines, guardrails, integration points, and data flows. You get a clear architecture document your team can build on and reason about.

3

Ship

Build and deploy the full product. Grounded, secure, monitored, and ready for real users. Not a handoff to your team with a TODO list. A working system in production.

4

Improve

Feedback capture, evaluation pipelines, and drift detection go live with your system. Your AI gets smarter from real usage, and we stay engaged to tune and evolve it over time.

Talk to an Engineer

Tell us what you're working on. We'll get back to you within one business day.