Expert in the loop
Domain experts guide task design, review quality, and assess results that require specialized judgment.
Data & Evaluation Infrastructure
Training Data That Drives Model Improvement
We provide training tasks and evaluation environments grounded in real enterprise workflows, designed to strengthen model capabilities through expert review and verifiable outcomes.
Evolocity combines synthetic data generation, expert-guided task design, executable environments, automated verification, and adversarial evaluation to create challenging training tasks aligned with real-world enterprise objectives.
Our approach connects data generation with model performance, enabling continuous improvements as models evolve.
Our approach to training data
Sourced, constructed, and verified
We use enterprise workflows and public and authorized private code repositories as the foundation for task design. Each task targets a defined capability, with outcomes validated through automated checks and expert review.
Scenario diversity
Tasks span varied workflows, codebases, tools, and levels of complexity to reflect the range of work enterprise agents encounter.
Verifiable outcomes
Clear success criteria, reproducible environments, and executable checks connect task performance to the capabilities being trained.
High-quality training data generation
Expert-Guided Data Generation
We develop challenging, verifiable tasks designed to improve model capabilities. AI-assisted generation, expert task design, and systematic verification work together to produce useful training signals.
- Synthetic data generation with expert-in-the-loop supervision.
- Expert-designed task specifications and difficulty calibration.
- Task generation grounded in public and authorized private repositories.
- Multi-turn coding and complex software engineering tasks.
- Domain-specific training tasks informed by enterprise workflows.
- Automated and expert-led quality assurance.
Reinforcement learning environments
Executable RL Environments
High-quality training data extends beyond prompts and answers. Executable environments let agents take actions, use tools, receive feedback, and learn from verifiable outcomes.
- Reproducible task environments.
- Executable test cases and verifiers.
- Environment setup and state management.
- Multi-step agent interaction and tool use.
- Trajectory and execution-trace collection.
- Reward signals based on verifiable task outcomes.
Evaluation and quality assurance
Evaluation Integrity & Adversarial Testing
Reliable evaluation helps distinguish actual capability improvements from superficial benchmark gains. We focus on trustworthy training signals through automated checks, expert review, and adversarial testing.
- Automated task validation and test execution.
- Expert review of ambiguous or abnormal results.
- Task difficulty assessment.
- Detection of shortcut solutions and reward hacking.
- Red-team and blue-team adversarial testing.
- Verification of task correctness and reproducibility.
- Data contamination and overlap analysis.
Domain-specific enterprise data
Enterprise-Specific Training Tasks
We develop specialized training tasks for enterprise workflows and technical environments, combining AI-assisted generation, specialized engineering expertise, and enterprise-specific evaluation criteria. Areas of focus include:
- Proprietary programming languages and domain-specific languages (DSLs).
- Enterprise coding agents.
- Internal software engineering workflows.
- Enterprise application development.
- Specialized tool-use and multi-agent workflows.
- Tasks requiring domain-expert knowledge.
Data that evolves with model capability
Continuous Data Improvement
Training data should evolve as models become more capable. Our approach is a continuous improvement pipeline, with task difficulty and coverage informed by observed model performance.
- Measure task saturation against target models.
- Identify tasks that no longer provide useful learning signals.
- Automatically flag obsolete or insufficiently challenging tasks.
- Generate more difficult tasks based on model performance.
- Analyze training trajectories and failure patterns.
- Refresh task distributions and verification criteria.
- Support iterative post-training and reinforcement learning.
Integration with Enterprise RSI
A continuous loop for model improvement.
Data generation, verification, post-training, and evaluation form a connected learning loop within Evolocity’s Enterprise Recursive Self-Improvement architecture. Observed performance guides the next generation of tasks, environments, and training signals.
- 01
Generate
Create targeted training tasks and environments.
- 02
Verify
Validate correctness, difficulty, and reward integrity.
- 03
Train
Use verified data for supervised fine-tuning or reinforcement learning.
- 04
Evaluate
Measure model performance on held-out and enterprise-specific benchmarks.
- 05
Analyze
Identify failures, reward hacking, and saturated tasks.
- 06
Improve
Update task generation, verifiers, and training data based on observed performance.
Collaborate with Evolocity
Build Better Enterprise AI
Bring us the capabilities you want to improve, the workflows that matter, and the outcomes you need to measure. Let’s explore the training tasks, executable environments, and evaluation criteria that can move your models forward.
info@evolocity.ai