The AI that matters to a company is the one that reaches production. We build vision that inspects, agents that run complete workflows, and systems that are measured before they ship, with your real data, inside your environment.
vision · agents · MCP
Three pieces we combine based on what your problem needs in each case.
Today’s vision-language models (VLMs) detect, count, and extract data from images and documents without training a custom model. When the problem asks for something else (real time, edge, top accuracy), we still train classic supervised vision.
zero-shot detection, no training data
Systems that do more than answer: they query your tools, run the steps of a workflow, and return a result you can verify. We design them with explicit orchestration, guardrails, and a human in the loop.
62% of organizations test agents (McKinsey)
We connect agents to your data and systems through the Model Context Protocol (MCP), the open standard governed by the Linux Foundation since December 2025, with more than 10,000 active public servers.
MCP stable 2025-11-25 · revision 2026-07-28
We work as forward-deployed engineers: inside your environment, with your real workflows, and we stay after launch. The discipline has twenty years of history, and demand for it exploded (job postings grew 800% between January and September 2025). The reason is uncomfortable: most enterprise AI pilots never produce measurable impact. The model is usually the clean part. The hard part is the workflow nobody documented and the data source people actually trust.
And nothing ships without being measured: we start from 20-50 tasks taken from real failures, we measure how consistent the results are, and we review the transcripts regularly. The system earns trust before deployment, with tests that come from your real operation.
Mature: classic supervised vision is still the right choice for edge, real time, and top accuracy. Traditional OCR still wins on clean scans (98-99% accuracy at near-zero cost). And structured document extraction with VLMs is already settled in production.
Recent and moving fast: agents. Gartner expects more than 40% of agentic AI projects to be canceled before the end of 2027, due to cost and unclear value. Our answer is narrow scope, measurement, and autonomy in stages. One important caveat: VLMs can produce text that looks right but is wrong, so for critical documents we add cross-validation.
The analytics and models in these systems were designed and built by the team that today forms T3 AISAT:
Tell us the workflow and the data. We will tell you what to automate today, what to evaluate first, and what not to try yet.
Talk to the team