The industry
solved building AI.
We solved trusting it.
Tasq is the orchestrated judgment layer between enterprise AI models and the decisions they can’t afford to get wrong. In production at global platforms where the cost of drift is measured in revenue, safety, or trust.
The lane
we run in.
Beyond scale. Precision at the edge.
What makes AI trustworthy isn’t how much it sees, it’s how the edge cases resolve.
Beyond benchmarks. Validation, live in production.
Continuous evaluation on the systems that matter: where the model runs, not where it was trained.
Beyond labor. The right human, at the right moment.
Minimum sufficient expertise per decision. Fast where automation fits, deep where it doesn’t.
Beyond lab metrics. Trust, proven in production.
Every capability tested where it counts: live systems, real stakes, measurable outcomes.
Routing cognition,
not just labor.
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 but the right human, for the right decision, at the moment the model needs one.
Data infrastructure
Structured, high-quality data at scale. Table stakes for any AI system, and the foundation the validation layers above depend on.
Build & train
RLHF, benchmarking, human feedback loops. Training-phase signals that shape model behavior before it hits production.
Refine & validate
Production-time judgment. Continuous evaluation, drift detection, edge-case resolution on live systems. This is where Tasq is structurally different, and where trust is won or lost.
Production AI across
verticals where being
wrong isn't an option.
Expert-grade visual intelligence for mission-critical AI.
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.
Continuous model validation for revenue-critical AI.
Continuous validation of production models in live, revenue-generating systems. A culturally-aware global network evaluates data at scale; ambiguous cases automatically escalate to domain experts for judgment resolution.
Signals feed back into the pipeline in real time, protecting AI where the cost of drift is measured in revenue.
Crowd-scale evaluation for brand-critical AI.
Multi-layer evaluation of a generative AI feature surfacing AI-composed responses to a global user base. Tasq delivered three functions: human feedback for model fine-tuning, validation of synthetic training data against real-world complexity, and golden-set benchmarks measuring model performance against human baselines.
Crowd-scale is what makes this work at that user base size.
Every capability earned where it counts.
Global contributor
network
Credentialed
domain experts
Languages
supported
In production across
Social platforms
Global commerce
Defense & intelligence
Media & content
Fortune 500
Built at the intersection of AI
infrastructure and human expertise.
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. We call the framework HERO: Human Expertise & Reasoning Orchestration.