The Model
How we work.
One model. Four phases. Predictable from the first day.
One senior engineer. Two weeks. Inside your environment.
We map the workflow, understand the constraints, and identify the highest-leverage place to start. You walk away with a written deployment plan whether or not we continue.
This phase is designed to be cheap to start and useful regardless of what happens next.
A small team — typically two to four engineers — embeds with your operators.
We design the system in your reality: your data, your security model, your tools, your people. The architecture decisions get made by the same engineers who will have to live with them.
We build inside your security model, your compliance constraints, your existing infrastructure.
The goal is production, not pilot. The system goes live in your environment, on your terms, in the hands of the people who'll operate it every day.
We document, we train, we transfer.
Your team takes the wheel. We stay close for long enough to make sure the system stays in production after we step back. The goal is to leave you self-sufficient.
Capabilities
What we deploy.
Engineering categories, not service-line menus. Each is a way we ship.
AI systems that run inside your firewall, on your hardware, under your control — without compromising on capability. Air-gapped deployments, on-prem inference, sovereign clouds, bring-your-own-compute architectures for industries where standard offerings don't fit.
Voice agents, transcription pipelines, and natural-language interfaces that work in Indian languages, regional dialects, and the code-mixed reality of how people actually talk. Systems that work in the field, not just in English on a quiet day.
Computer vision and inference systems on the constrained hardware your operation actually has — Raspberry Pi, Jetson, Hailo, custom silicon — at the latency, power budget, and unit cost your environment allows.
Autonomous agents and multi-step workflows, then compiling what they learn into deterministic, verifiable artifacts. The reasoning stays adaptive. The mechanical parts become reliable. The work compounds.
Data foundations, schema knowledge graphs, and natural-language interfaces that turn legacy systems into something your operators can actually use. The CFO gets her answer in a sentence. The dashboard becomes a conversation.
Engagement Shapes
Three ways to work with us.
Pick the shape that fits the problem. If you're unsure, we'll help you pick.
Discovery Sprint
2 weeks, fixed price
One senior engineer, embedded in your environment for two weeks. You walk away with a written deployment plan, a clear recommendation, and a confident go / no-go.
Best for
Organizations that know there's an AI opportunity somewhere and want a builder's view of where to start.
Embedded Build
8 to 16 weeks
Our core engagement. A team of two to four Forward Deployed Engineers, scoped to one workflow that matters, ending with a system live in your production environment.
Best for
Organizations with a defined problem and the operational readiness to put a system into production.
Embedded Team
Ongoing, quarterly
A dedicated team of three to six engineers operating as an extension of your in-house engineering function. Quarterly outcomes, not staffing-firm hours.
Best for
Organizations building AI as a core capability and looking for senior engineering depth alongside their own team.
Plan A POC
Start a project.
Tell us about the workflow you want to put AI inside. If we're a good fit, we'll say so. If we're not, we'll point you toward someone who is.
Two weeks to a deployment plan you can act on.