High signal data

for Frontier AI

Collinear powers leading AI labs and F500 enterprises with high signal evaluation data, RL environments, and post-training data, helping models improve faster and fail less.

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See how it works
From the creators of
Zephyr
5200
RL4LMs
2300
Solution

High signal data and environments for evaluation and post-training

Data for Evaluations

Collinear’s multi-turn RL environments simulate realistic user journeys, revealing brittleness and failure modes that traditional benchmarks miss.

Each run yields structured traces with policy-aware scoring and coverage metrics—forming a reproducible foundation for regression testing, capability tracking, and safety analysis.
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Learn more about Simulations

Data for Post-training

Collinear delivers curated, high-throughput data for post-training across CPT, SFT, and RL, accelerating model improvement by up to 8×.

Our data recipes ensure each data pack is license-verified, difficulty-balanced, and policy-filtered, ready to integrate directly into your training stack.
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Learn more about Post-Training Data

RL Environments for training

Collinear Environments are a live world for your models  to act in, with real tasks, tools, roles, and verifiers that mirror production. Agents take actions, see outcomes, and receive dense rewards.We provide pre-built sandbox environments of common enterprise software (e.g., CRM, ticketing systems, knowledge bases etc.) to safely train and evaluate agents on realistic workflows before they touch production
Learn more about RL Environments
How it works

From simulation to
improvement in three steps.

Step 1

Simulate

Create multi-turn, auto-generated scenarios that mirror real user journeys and adversarial attacks.

Your Gen AI App

Step 2

Analyze

Run A/B tests and red-teaming to reveal failures and measure performance.
Step 3

Improve

Turn failures into curated evals and fine-tuning data that strengthen your models.
Step 1

Simulate

Create multi-turn, auto-generated scenarios that mirror real user journeys and adversarial attacks.
Step 2

Analyze

Run A/B tests and red-teaming to reveal failures and measure performance.
Step 3

Improve

Turn failures into curated evals and fine-tuning data that strengthen your models.
Outcomes

Better data and enivironments
beats bigger models.

Collinear’s simulation-generated datasets deliver higher signal, faster learning, and consistent gains in accuracy, reasoning, and safety.

Old Way

Manual Data

Benchmarks miss real-world behavior
Static “golden data” lacks coverage
Teams debug regressions manually and retrain blindly
Improvement is slow, reactive, and hard to measure
Collinear Way

Smarter Training, Better Models

Simulation driven eval datasets and RL environments to mirror real enterprise workflows
Curated CPT/SFT corpora and reward data built for signal density, not volume
Pre built RL environments to  test safely before touching production
Dense, verifiable rewards help agents converge faster on critical, high value tasks
Models show faster convergence, better metrics, and fewer regressions
Testimonials

Customers ship better models faster with Collinear.

See how leading enterprises get to deployment
with confidence, control and trust.

$10M+

saved in compute spend through targeted data curation

- F500 Enterprise Software

96%

F1 score achieved by Collinear reliability judge

“Our partnership with Collinear is already driving business results. 91% of AI-generated responses showed significant improvement, leading to faster resolutions and better customer experiences.”

"Collinear’s quality judges were instrumental in launching MasterClass On Call, our latest product delivering AI-powered wisdom from world’s best pros."

10k+

multi-lingual novel jailbreak modes discovered

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- Leading AI Research Lab

15% increase

in unique visitor-to-first-visit conversion with Collinear's Custom Sales Agent Judge

Ship smarter models, not bigger ones.

Collinear generates the high-signal evaluation and post-training datasets that make every release stronger.

Generate Data
FAQs

Get answers to
common questions

Do you support both open-source and closed models?

Yes. Collinear works with any model, whether you're using proprietary APIs, open weight models, or custom fine-tunes.

Do we need to share our model or training data with you?

No. Collinear evaluates outputs, not weights or training sets. You stay in control of your models and data at all times.

Can I bring my own safety policies or evaluation criteria?

Absolutely. You can use our built-in Judges and red-teaming libraries, or customize them with your own rules and risk categories.

How quickly can we see results?

Most teams see clear insights within days, especially with our guided trials and baseline safety assessments.

Can Collinear run on-prem or in a private cloud?

Yes. We support flexible deployment models, including VPC-hosted, air gapped, and fully on-premise setups to meet enterprise security requirements.

Stop launch-and-pray AI.

Simulations catch failures before your customers do.

Try our Platform