tasq.ai is the AI evaluation platform that combines model, expert, and crowd judgment so enterprises can trust their models in production.
What makes AI trustworthy isn’t how much it sees, it’s how the edge cases resolve.
Minimum sufficient expertise per decision. Fast where automation fits, deep where it doesn’t.
Continuous evaluation on the systems that matter: where the model runs, not where it was trained.
Every capability tested where it counts: live systems, real stakes, measurable outcomes.
AI models excel at pattern. They break at the edge — where the decisions are ambiguous, the stakes are real, and a wrong call carries consequence. Tasq sits at that edge.
We deconstruct every high-stakes problem into micro-decisions, route each one to the right level of judgment — machine, contributor network, domain expert — and resolve it in real time. Not more humans. The right human, for the right decision, at the moment the model needs one.
When ground truth exists. Fast, cheap, automated.
When human intuition matters. Real-time access to millions, statistically validated for empathy, threat, appeal, and sensory signals.
When it doesn’t. Domain specialists judge, validate, and provide feedback for fine-tuning.
All three run separately or in tandem. Automatically. Continuously. Batch or API. Backed by cycle-time and quality SLAs.
Empathy. Threat perception. Cultural nuance. Appeal.
When AI needs to evaluate what a normal human would feel, tasq.ai delivers it — at production scale, across languages and cohorts.
Critical for: advertising, world models, physical AI, customer care.
Selected production engagements where drift costs revenue, safety, or trust.
Massive volumes of robotic task footage evaluated frame by frame. Each task reviewed across three layers: executed vs prompt, execution quality, and success.
Findings fed back into both training data and production validation, sharpening the model on every dimension a physical AI system has to get right.
Multi-layer evaluation is what gets physical AI to perform flawlessly.
The agency’s cleared experts couldn’t produce operational-grade data volumes alone. Tasq’s network handled the bulk of visual recognition on declassified micro-decisions from aerial thermal video; only judgment-grade calls escalated to in-house experts.
Clearance-free by design, and the only architecture that makes this scale possible.
Live validation of production models in revenue-generating systems. A culturally-aware global network evaluates data at scale; ambiguous cases escalate to domain experts. Signals feed back into the pipeline in real time – protecting AI where the cost of drift is measured in revenue.
Crowd-scale is what makes this work at that user base size.
Live validation of production models in revenue-generation systems. A culturally-aware global network evaluates data at scale; ambiguous cases escalate to domain experts.
Signals feed back into the pipeline in real time – protecting AI where the cost of drift is measured in revenue.
Tasq was formed from the merger of Tasq.ai, the AI orchestration platform built for edge-case decisions, and BLEND, the world’s largest network of credentialed domain experts across 120+ languages.
One company. Full-stack ownership of the trust layer: the decomposition algorithms, the task-management platform, and the global judgment network, all in-house. No other player has all three.
L3 production-time validation. Continuous, live, in the systems that matter — not pre-launch batch. Competitors are annotation vendors. We are the operating layer for AI you can actually trust.
No strategic investor from within the client base. No conflict of interest. Our largest deal was won on exactly this basis, and it’s become an active buying criterion.
100M+ culturally-aware crowd contributors. 75K+ credentialed domain experts. 120 languages. All in one platform. No vendor switching, no coordination overhead.
Free batch-evaluation on your production data. See where your model breaks, and how Tasq catches it.
Capital, channel, M&A. We’re building the independent trust layer for production AI, and scaling fast.