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An agent’s access should depend on the task it is doing. The same action can be appropriate for one task and wrong for another. Access decisions need to adapt in real time as the agent works, but companies can’t turn every business judgment into a fixed rules or have people manually approve every step.
We train small, company-specific models that approve AI actions in real time. They consider the company’s policies and interests, the agent’s current task, and its past actions to decide whether each proposed action should be allowed. Access is specific to the task at hand and reassessed as the agent works.
Companies will rent intelligence from AI labs or run open-source models to power their agents. They’ll own the models that keep their entire AI workforce acting in their interests. Decawork is building those models.
- Built AI compliance platform at Barclays used by internal teams globally - Founded a fintech and scaled it to 52K families and $1.5M - Led product at a consumer AI startup, scaled to 100K+ MAU (Google Play Best App 2024) - IIT Kharagpur
- AI systems at NVIDIA (deployed at OpenAI, Meta) - Enterprise AI at Ema (used by Microsoft, Hitachi) - Led Conquest, Asia's largest student-run accelerator - Google Code-In Global Winner + Open source since the age of 12 - BITS Pilani
Founded 2026 · Source
Entrepreneur First · Product Hunt · Y Combinator
Source-based assessment · automated evidence rules
The retained text does not establish product availability or customer use.
Maturity is an evidence-based assessment, not a success rating. It may understate the current stage where evidence is limited. Coverage records documented areas, not a complete or independently audited company history.
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