We help companies launch AI products, modernize the systems behind them, and restart important initiatives that have stalled.
Bring us the problemOne accountable team
We can join your team where key expertise is missing or take a new system from idea to production. Either way, we stay accountable for the business result and the engineering needed to reach it.
Capabilities / 01
A good model is only the starting point. The product, software, data, and infrastructure around it determine whether AI actually works for your business.
We build the applications, backends, and distributed systems that turn AI ideas into dependable products.
02We build agents that use real tools, understand your business, and can be tested before they go live.
03We build the data pipelines, retrieval systems, monitoring, and controls that keep AI reliable.
04We help you choose, tune, and serve models that meet your goals for quality, speed, reliability, and cost.
Representative work / 02
These examples show the kinds of problems we have solved and how we approached them. We only include results we can stand behind.
A production search workload was taking 4 to 5 seconds. We fine-tuned a smaller model that delivered relevance comparable to Claude Sonnet, reduced latency to about 500 milliseconds, and saved the client approximately $250,000 a year in infrastructure costs.
We built custom classification and prediction models that processed more than 300 million items at approximately 30 milliseconds per inference and reduced manual review by more than 80%. We also fine tuned large language models for ecommerce workflows, helping drive an approximately 50% increase in revenue.
We built AI agents that connected existing business systems and automated customer and commerce workflows, helping drive an approximately 30% increase in revenue through intelligent automation.
How we work / 03
We agree on the result, test the biggest risks early, and show progress in software rather than slides.
Clarify the problem, the business goal, the constraints, and the decisions that matter.
Use real data and a working slice of the system to test the assumptions most likely to fail.
Build the services, controls, monitoring, and support model needed for real use.
Use production evidence to improve quality, speed, cost, and reliability, then expand where it creates value.
Principles / 04
A small team can move quickly when the people doing the work have the judgment and authority to make decisions.
We keep feedback loops short, show progress early, and make decisions before work stalls.
We take on important systems and hold our work to a high standard.
We learn unfamiliar systems, work through uncertainty, and keep going until the problem moves forward.
We judge technical decisions by what they do for the people using the system and the business paying for it.
Interested in our AI services?
Share what is stuck, what you have already tried, and what needs to happen next. We will respond with a practical point of view.
