Multimodal ingestion
Engineering drawings, technical data packages, publications, structured records, and sparse or noisy signals. Each source is normalized, fingerprinted, and kept as evidence rather than flattened into text.
Multi-agent · Closed-loop · Human-in-the-loop
Governed agentic AI for consequential work.
Vinetka builds multimodal agentic systems for engineering, defense, and research programs where an unverified answer is a liability. Our architecture reads drawings, documents, signals, and records; reasons across them; and produces models, procedures, and decisions with the evidence attached. Every output is checked, versioned, and traceable before a person signs it.
SourcesDrawings · documents · signals · recordsNormalized, fingerprinted, kept as evidence
HELMAgents · gates · provenance · reviewOrchestrated, checked, versioned, approved
OutputsModels · procedures · decisionsSigned, with lineage, replayable
Live systems
POINTLive lab prototypeReadinessTRL 4 · not accreditedFORGELive · 4 packagesPOINTLive lab prototypeReadinessTRL 4 · not accreditedFORGELive · 4 packages
Our stance
Most AI is built to answer. Ours is built to be audited.
Try it live
Ask a running system. Watch it cite, fence, or refuse.
This console calls POINT, our lab system, on this server. Every answer is a quote from an approved publication, scoped to your role. If the source is not there, it says so.
TRL 4 lab prototype · synthetic unclassified corpus · not accredited
Open the full lab- 01Retrieve
- 02Role fence
- 03Gate
- 04Answer
Pick a scenario or type a question. Try the same question as an operator and as a maintainer.
The architecture
HELM: one architecture, every modality, no unverified step.
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Multimodal ingestion
Orchestrated agents
Specialized agents draft, retrieve, compute, and critique. An orchestrator routes work by task and confidence, runs agents in parallel, and escalates what it cannot resolve.
Deterministic gates
Rules, geometry and unit checks, tests, and gold-set evaluations run on every proposal. A failed gate stops the pipeline. A passed gate becomes part of the record.
Provenance and versioning
Every artifact carries its lineage: sources, agent, checks passed, reviewer, and the reason anything changed. Any output can be replayed from its history.
Human authority
Experts approve, reject, or correct at defined points. Review is an authority tier, not a bottleneck: low-risk work flows through, high-impact changes wait for a signature.
Closed-loop learning
Corrections feed back into prompts, checks, and models. Where real data is scarce or restricted, we generate labeled synthetic data to train and measure against.
Proof, not adjectives
What is true today, measured on this site.
20/20
evaluation cases pass, rerun on every change
2
live systems you can open, with their limits stated
Operator
Maintainer
Admin
Same question, different answer by role. Fenced at retrieval.
Drawings
Documents
Signals
Records
Proposals submitted to all three services.
SBA-certified Women-Owned Small Business (WOSB). SAM.gov Active.
Government factsWhere it runs
Built once. Pointed at the hard problems.
Technical knowledge and performance support
Cited answers from controlled corpora, role-fenced access, and refusal when the source is not there.
Engineering content from technical data
Specifications, drawings, and manuals turned into verified, articulated digital assets for training and maintenance.
Auditable engineering decisions
Requirements, design reviews, and acquisition studies captured as living, explainable decision records linked to their evidence.
AI test and evaluation
Gold sets, failure-mode analysis, and performance measurement for AI under test, before it earns operational trust.
Computational partnerships
The modeling, reconstruction, and analysis layer for hardware and science programs, from photonics to medical imaging.
Why Vinetka
Trust is a design requirement.
- 01Operations research at the root
We model decisions under uncertainty formally. Agents are components inside an optimization problem with constraints we can state, not a layer of guesswork on top.
- 02Production AI, not demos
Our founder shipped AI at Adobe on Firefly and Acrobat, products used by hundreds of millions. We know what breaks between the lab and the field.
- 03Model-agnostic, deployment-ready
Local or cloud, frontier or fine-tuned, air-gapped if needed. The gates and provenance do not change when the model does.
- 04We claim what ran
Our public systems state their readiness level and their limits. We think that should be standard.
Leadership
Founder-led. Senior from day one.
Vinetka was founded by Tina Rezvanian, Ph.D., who built production AI at Adobe on Firefly and Acrobat and holds a doctorate in operations research. She leads every program personally, with a network of senior engineering, physics, and systems-engineering collaborators.
Start a conversation
Bring us the problem you cannot afford to get wrong.
Program teams, primes, and research partners: we will scope a focused pilot, a technical approach, or a teaming role.
Discuss a program