Reinforcement Learning Infrastructure
Enterprise RL Environments for AI Labs
We build high-fidelity reinforcement learning environments that mirror real Fortune 500 enterprise challenges — so AI labs can train models that actually work in the real world.
01 — The Problem
AI labs need enterprise data.
They can't get it.
Enterprise IP Walls
Fortune 500 companies will not share codebases, internal tooling, or implementation data. IP protection and privacy policies make direct collection impossible.
Vendor Data Is Low Quality
Large vendors focus on volume over depth. Their environments are generic and don't capture the complexity of real enterprise implementations.
Open Source Is Contaminated
GitHub is already scraped by every lab. Open source is in pre-training data and doesn't represent how closed enterprise projects actually work.
Product Deployment Has Limits
Even when labs deploy coding tools, enterprise customers don't opt in to data collection. The most valuable training signal stays behind corporate firewalls.
The Gap
Nobody is creating high-fidelity environments that represent what it's actually like to implement technology at a Fortune 500 company. That's where we come in.
02 — Core Insight
Environments beat traces. Every time.
Environments
What labs buy- Reusable — generate infinite traces from a single environment
- Models run hundreds to thousands of rollouts, exploring solutions humans would never consider
- Mirrors RL breakthroughs from DeepMind — models discover novel strategies through exploration
- Docker image + codebase + verification script = complete training loop
- Premium pricing — AI labs pay significantly more for environments than any other data type
Static Traces
Mostly obsolete- One-time snapshots — once collected, you cannot generate more data
- Constrains the model to follow a human's path instead of exploring freely
- Models are now better than most humans — why constrain them?
- Not scalable — you need enormous quantities and they still run out
- Labs almost never pay for trace data anymore
03 — Our Edge
We know what enterprise actually looks like.
Fortune 500 Pattern Depth
Years of implementation experience across the world's largest enterprises. We've seen the patterns in financial services, healthcare, pharma, industrial, and legal — patterns that don't exist on GitHub.
Synthesis, Not Exfiltration
We never use customer data directly. Instead, we synthesize environments based on cross-industry patterns we've observed. What implementing XYZ looks like, what challenges you encounter.
Out-of-Distribution Value
Open source is in-distribution — labs already train on it. Our environments are genuinely out-of-distribution: proprietary patterns, enterprise complexity, real-world constraints no scraper can capture.
Domain Coverage
04 — The Product
Sandboxed environments ready for RL.
Docker-Based Sandboxes
Self-contained Docker images that spin up realistic enterprise codebases. Each environment represents a specific implementation challenge with all dependencies and constraints in place.
Verification Scripts
Automated reward signals that validate whether the model completed the task. Binary pass/fail for RL training loops — reward 1 for success, 0 for failure.
Domain-Specific Tasks
Technology implementations, platform migrations, system integrations, compliance workflows — the high-value enterprise tasks that AI labs desperately need coverage for.
Infinite Rollout Capacity
Each environment supports thousands of independent rollouts. Labs train many model variants, letting each explore different solution paths until they discover what works.
05 — Target Verticals
Deep in domains that matter most.
06 — Market Context
A hot market with a quality gap.
Existing Players
- Scale AI — broad training data, RLHF, evals
- Surge AI — high-trust RLHF, Anthropic partner
- Labelbox — instruction tuning, SFT, RLHF
- Appen — domain-expert RLHF across verticals
- Turing — proprietary human data for SFT/DPO
- Mercor, Datacurve — emerging vendors
Why There's Room
- Big vendors focus on scale, not domain depth
- Generic environments don't capture enterprise complexity
- Niche domain-specific data commands premium pricing
- Labs spending aggressively on RL environments right now
- DPO/RFT reduce need for basic labels but increase need for rich environments
07 — How It Works
From patterns to product.
Identify Patterns
Catalog enterprise implementation patterns from years of Fortune 500 consulting engagements across financial services, healthcare, and industrial verticals.
Synthesize Environments
Build Docker-based sandboxed codebases that mirror real enterprise challenges. No customer data — only cross-industry patterns synthesized into realistic scenarios.
Add Verification
Create automated scripts that validate task completion — the reward signal for RL training. Binary pass/fail enables clean reinforcement learning loops.
Sell to AI Labs
Labs run their models through our environments thousands of times. Models explore, fail, learn, and eventually master enterprise-grade tasks.
08 — Deep Dive
Building a sandbox: Trading Platform
Example EnvironmentImplement a Real-Time Order Matching Engine at a Tier-1 Bank
What's Inside the Docker Image Sandbox
A realistic enterprise codebase — not a toy project. Enterprise patterns, legacy constraints, real dependencies.
The Task Prompt What the model sees
A natural-language task — the same kind of work request a senior engineer would get.
Verification Script Reward Signal
Automated checks that produce the binary reward. Model gets 1 only if ALL gates pass.
Why This Is Valuable Complexity
What makes this different from open-source toy problems.
09 — The RL Loop & Next Steps
How an AI lab uses this environment.
Repeat 100s–1000s of times. Different approaches each run — different concurrency strategies, data structures, architectural decisions — until the model masters the task.