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What's new?
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For AI-first companies & labs
We build ultra-realistic RL environments that mirror production complexity - delivered by FAANG level engineers, not sub-par contractors. Your agents learn from loss-inspired examples and agentic data—not toy problems that leave you months behind when reality hits.
You've trained your agent on clean datasets and controlled scenarios. It aces your benchmarks. Then you deploy—and it crumbles under noisy, ambiguous real-world workflows.
The problem? Most RL environments are static, predictable, and divorced from the complex, multi-tool dynamics your agents will face in production. They need long-horizon tasks, interruptions, and real-world noise—not toy problems.
Long-horizon tasks in environments that replicate real-world workflows. Multi-tool orchestration, dynamic state changes, and genuine uncertainty—not sanitized simulations.
Every failure is a learning opportunity. We generate loss-inspired examples that target specific capability gaps, creating a feedback loop that continuously improves performance.
Our pipeline generate agentic data—traces of decision-making, tool usage, and recovery from failures that reflect authentic agent behavior patterns.
We systematically identify failure modes and mine for loss-heavy scenarios. Your agents spend time on hard problems, not easy wins they've already mastered.
We scout and work exclusively with FAANG level engineers - whether full-time or contractors - to deliver best-in-class solutions. Weekday founders will be your "project managers". Our 4 years building a people search company gives us an unfair advantage: we know how to identify exceptional talent and assemble custom teams tailored to your exact needs.
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Weekday founders will work your team as project managers. For engineering, we handpick senior engineers from top tech companies who've built production ML systems at scale.
Four years building a people search company means we have proprietary methods to identify, vet, and engage top-tier technical talent faster than anyone else.
Every engagement gets a bespoke team assembled specifically for your problem domain, tech stack, and business requirements. No cookie-cutter approaches.
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Build agents that thrive in production complexity. Get ultra-realistic RL environments tailored to your workflows, with agentic data that actually reflects how your agents will behave in the real world.
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