Projects
AI systems, scoped and built for the problem in front of you.
We design and build AI systems — agents, automation, and the data foundations underneath them — for teams of any size.
Why scope first
Most AI pilots never make it past the demo.
In MIT's 2025 study of enterprise AI, 95% of generative-AI pilots delivered no measurable business impact — and the reason was rarely the model. It was integration: systems that never reached real workflows. Gartner expects roughly 30% of generative-AI projects to be abandoned after proof of concept by the end of 2025, most often for poor data quality or unclear business value.
We build the other way around: scope the problem first, integrate for real, and hand over something your team runs on its own.
What we build
Working systems — not proofs of concept that stall after the demo.
- Agents — AI systems that take action, not just answer questions.
- Automation — replacing manual, repetitive work with reliable AI-driven pipelines.
- Data foundations — the pipelines and structure that make AI systems trustworthy.
How a project runs
Three phases, every time.
Scope
We define the problem, the data involved, and what a working system looks like — before any code is written.
Build
We build the system against the scope, with regular checkpoints so there are no surprises at the end.
Handover
Your team gets a working system, documentation, and enough context to run and extend it without us.
Not sure what to build yet? A consulting engagement scopes the problem first.
Delivered in Hebrew or English.
Questions
Before you scope a build, the usual questions.
What kinds of AI systems do you build?
Three, mostly: agents that take action rather than just answer questions, automation that replaces manual and repetitive work with reliable pipelines, and the data foundations underneath both. Every build starts from a real problem your team has — not from a technology looking for a use.
How do you avoid the "pilot that never ships"?
By scoping before building. We define the problem, the data, and what a working system looks like up front, then build against that scope with regular checkpoints — so the system lands in real workflows instead of stalling after a demo. That integration gap is why MIT's 2025 study found 95% of enterprise generative-AI pilots deliver no measurable business impact.
Do we own the code, and does it run on our own infrastructure?
Yes. You get a working system, its source, documentation, and enough context to run and extend it without us. Where it runs — your cloud, your servers, or a managed setup — is part of the scope, decided around your security and data requirements, not ours.
What if our data isn't ready?
That's common, and often the first phase of the work. Data foundations — the pipelines and structure that make an AI system trustworthy — are something we build, not a prerequisite you need in place before calling. Poor data quality is one of the top reasons Gartner expects 30% of generative-AI projects to be abandoned after proof of concept by the end of 2025.
How do you scope and price a build?
Every project is scoped and quoted up front — there's no default retainer or license. Scope, data, and what "done" looks like are agreed before any code is written, and pricing follows from that. The intro call is where we size it, at no cost.
Have an AI project in mind?
Book an intro call and we'll talk through scope and fit.
Book an intro call