Ten capabilities delivered by engineers embedded in your environment, accountable end to end. Engagements are fixed-scope with milestone-driven payments, and most begin with a single process rather than a platform-wide program.
What has to be true before AI works at all: data you can trust, infrastructure that holds, somewhere to serve from.
3 capabilitiesThe foundation every later sprint builds on. Pipelines, warehousing, and data lakes with governance and compliance designed in, not retrofitted.
From legacy systems to cloud-native, AI-ready environments. Migration planned in stages so nothing stops while it happens.
The layer your models are served from. Inference APIs, orchestration, and cost controls, engineered to hold under real traffic.
The systems themselves, from models tuned to your domain through to agents that act on their own.
5 capabilitiesBespoke systems for the cases where general-purpose models fall short, including fine-tuned LLMs trained on what only you have.
Models that read and see. Semantic search, summarization, and document understanding, plus vision-language systems that reason about images.
Agents that carry a decision end to end, with human oversight at the points that matter and an audit trail behind every step.
AI that acts in the physical world. Perception, control, and decision-making on robots, vehicles, and industrial equipment, running at the edge.
The product layer around the model. Full-stack engineering that turns a capability into something your teams and customers will actually use.
What keeps a system alive after launch, and what leaves your team able to run it without us.
2 capabilitiesThe hardening that turns a working model into a production system: monitored, automated, documented, and reproducible months later.
Hands-on enablement so the capability stays after we leave. Your engineers work alongside ours from the first sprint, not the last week.
A 30-minute conversation. We'll tell you whether we're the right fit, and if not, who is.