// consulting

Get answers before you spend a budget.

A feasibility answer, an architecture, or a second opinion on your ML team's results — grounded in 200k hours of production computer-vision work.

// who we help
[01]

Executives in industry

You want AI to cut costs or automate a process, and need a realistic read on what's feasible, what it costs, and what it returns.

[02]

Teams stuck on accuracy

Your ML team's scores are good but not good enough. We review the pipeline and propose concrete algorithms to close the gap.

[03]

New ventures

You have an idea with a vision component. We critique it at the earliest phase — before you commit a roadmap to it.

// how an engagement runs
step_1

Scoping call

You describe the problem and the data. We ask the questions that decide feasibility. Free.

step_2

Feasibility & PoC

A short, fixed-price study on your data: achievable accuracy, approach, risks, and a production estimate.

step_3

Production build

We build and integrate the system — weekly demos, metrics you can check, no black boxes.

step_4

Operate & improve

Monitoring, retraining, and scaling as your data grows — or a clean handover to your team.

// why we're not like others

Vendor-neutral by design.

We're not financially attached to any cloud, framework, or vendor. We assemble the best available components into an instrument that serves your business process — the way good systems engineers integrate rather than reinvent.

A team, not freelancers.

Every engagement is staffed as a cross-functional team — researchers, engineers, and a technical lead — so problems get attacked from several angles and never stall on one person's calendar.

Start with the scoping call.

Thirty minutes. No commitment. A straight answer on feasibility.

Get a quote