About

Thesis

Access to capable models is no longer the scarce resource. The scarce resource is the engineering around them: knowing what to measure, how to integrate a probabilistic component into a deterministic business process, how to keep a human accountable, and how to operate the result at a predictable cost.

psiberia exists to be excellent at that part. Every engagement leaves behind a working system in the customer’s environment, an evaluation set that describes what “good” means, and a team that can run it without us.

What compounds

We deliberately reuse and refine a small number of assets across projects:

• Evaluation harnesses — patterns for building labelled sets with domain experts, versioning them alongside code and running them in CI.
• Agent tooling — permissioned tool interfaces, replayable traces and human checkpoints that we have shipped more than once.
• Deployment patterns — reference architectures for keeping data inside a customer’s cloud boundary while still using hosted or self-hosted models.
• Domain knowledge — the specifics of document-heavy back-office workflows, where most of our work has been so far.

How we engage

Most engagements start with a short, fixed-scope framing phase: a written problem statement, success metrics and a first evaluation set. From there we build a thin end-to-end slice, evaluate it honestly and only then decide together whether to go deeper. We prefer a handful of long relationships to a long list of pilots.

What we will not do

We will not ship a demo dressed up as a product, promise an accuracy number we have not measured, or build something that only we can operate. If a rules engine, a spreadsheet or no AI at all is the right answer, we will say so.

Contact

Project conversations: hello@psiberia.com

Investor inquiries: investors@psiberia.com