We build AI for work that has to survive an audit.

Vinetka builds AI that turns messy engineering data, from sparse signals to detailed drawings, into models, procedures, and decisions people can rely on.

Nothing is used until it's been checked, traced to its source, and approved by a person.

Technical knowledge should compound. Any specification, drawing, manual, or test record should become a living digital asset in hours, and stay correct as the world around it changes.

The evidence travels with the answer, so nobody has to take an AI's word for anything that matters.

• Uncertainty is the problem, not a footnote. Our roots are in operations research. We state the constraints, quantify the doubt, and let the system escalate when confidence runs out.
• Autonomy is earned in layers. Agents start with authority over nothing. Each gate they pass reliably widens what they are trusted to do, and every expansion is recorded.
• Data is a first-class artifact. We build the datasets our models need, synthetic where reality is scarce, labeled where it is messy, and we keep them under version control like code.
• The field is the benchmark. We prototype narrow, run it on representative data, measure it, and only then describe what it does. HELM, our architecture, exists because that discipline kept producing the same components.
• Own the software, partner on the hardware. On photonics, imaging, and manufacturing programs we are the computational layer, working beside labs, universities, and manufacturers.

• Audit-first by design. Provenance, versioning, and human sign-off are not compliance features bolted on later. They are the spine of the architecture.
• Scale we have already lived through. Our founder built AI for Adobe Acrobat and Firefly, products in the hands of hundreds of millions of people, with research published alongside Adobe Research and MIT.
• Breadth without dilution. One architecture serves performance support, engineering content, decision records, and AI evaluation. New programs inherit years of hardening instead of starting over.
• Candor as policy. Our public systems carry their readiness level and their limits. We would rather earn trust slowly than lose it once.

Tina Rezvanian, Ph.D., Founder and CEO

Tina joined Adobe Firefly as a machine learning engineer when the team was first forming, and owned its fine-tuning pipeline, dense vector retrieval, multimodal encoders, and generation gap-analysis system. Before Firefly she joined Adobe Document Cloud in 2019, where she built AI systems, products, and pipelines for Acrobat on mobile and desktop, including Liquid Mode, and co-authored publications with Adobe Research and MIT on readability and human-computer interaction.

She holds a Ph.D. in Industrial Engineering and Operations Research from Northeastern University and is an Adobe-funded Research Fellow at Harvard Extension School.

VINETKA LLC is an SBA-certified Women-Owned Small Business (WOSB). CAGE 12K15, UEI N9V5G3DJ51C5, registered in SAM.gov.

We work directly with program offices, team with prime contractors, and partner with university research institutions on SBIR and STTR programs.