Training Data Generation
Training tasks grounded in real enterprise workflows and public and authorized private code repositories, designed around specific capabilities and validated through automated checks and expert review.
Enterprise AI that gets better
Self-Improving
Self-Improving
AI for the Enterprise
We build enterprise AI systems that continuously learn and improve from real-world work — with your data, your models, and measurable outcomes.
- Multi-model
& model-agnostic - Your data
(on-prem or cloud) - Measurable
improvements
The environment
An environment for recursive improvement
Enterprise Recursive Self-Improvement (Enterprise RSI) grounds recursive improvement in enterprise data, real operating environments, and demand for measurable outcomes. These conditions create a practical feedback loop for developing, evaluating, and improving AI capabilities.
Enterprise workflows can bring these conditions together. Recurring tasks create repeated opportunities to learn. Tools and operational constraints make the problems concrete. Execution traces, expert feedback, and observable outcomes provide evidence of what worked—and what needs to change.
In domains where new evidence is expensive or slow to obtain, closing this loop can be difficult. Many enterprise workflows offer a shorter path from an idea to an experiment to a measurable result. That makes the enterprise a compelling environment for developing and testing recursive improvement.
The opportunity is to turn the work itself into a source of capability.
Research at Evolocity
Enterprise RSI grounded in real work.
Our Enterprise RSI research investigates how AI systems can learn from the work they perform. Enterprise workflows provide task data, environments for experimentation, and concrete requirements against which improvements can be tested.
How can an agent learn from long sequences of actions? How can improvements generalize beyond the failures that motivated them? How should a model and its surrounding system adapt together? Can capability improve while the cost of successful execution falls?
These questions become concrete when a system must complete real work under real constraints. Researchers and engineers can take an idea through implementation, evaluation, and deployment—and see whether it makes the system better. The work connects frontier research with engineering whose results matter to the people using it.
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.
RL Environments & Verifiers
Reproducible environments where agents can act, use tools, and learn from reward signals tied to verifiable task outcomes.
Continuous Evaluation & Data Improvement
Evaluation that tests capability, detects reward hacking, and guides task updates as models improve.
Model and system adaptation
Co-evolving models and the systems around them
Enterprise AI performance emerges from the interaction of the model, its harness, tools, context, data, and inference. Improving any one component changes what the others can do.
Evolocity treats model adaptation and system adaptation as connected optimization loops. Some improvements happen quickly in the surrounding system; others require deeper changes to the model. Both are evaluated against the enterprise’s actual work, with attention to reliability, latency, and cost per successful task.
The research challenge is to discover which changes produce durable gains across the complete system. A stronger component is useful when it makes the deployed system stronger.
The learning loop
Evaluation is part of the learning architecture
A system cannot improve reliably if it cannot distinguish progress from overfitting, measurement noise, or regression. Evaluation is therefore part of the architecture that makes improvement possible.
Private enterprise tasks anchor the process in the organization’s definitions of success. New failure cases expand what the system measures. Previously acquired capabilities become constraints on future changes. Candidate improvements are tested on held-out tasks before they are promoted.
- 01Run
- 02Observe
- 03Evaluate
- 04Improve
- 05Verify
- 06Deploy
Over time, this accumulated evidence becomes a durable record of what the system must know how to do. Stronger frontier models can be tested against that record, allowing the enterprise to build on prior learning as the underlying technology changes.
Recursive improvement
Systems that help improve the next generation
As agents take on longer and more complex work, the improvement process must scale with them. Our direction is toward systems that help identify weaknesses, propose changes, and evaluate the next generation of agents.
This is the recursive step: capabilities developed in one generation can contribute to improving the next. Progress remains grounded in measured outcomes, with release controls appropriate to the workflow.
The opportunity extends beyond making an agent better at a single task. It is to build a repeatable process for improving the systems that perform those tasks—and to make that process increasingly capable itself.
The opportunity
Intelligence that compounds
Each validated improvement can become part of the foundation for the next. Better execution produces more useful experience. Better evaluation makes that experience actionable. Model and system improvements turn it into additional capability.
For researchers and engineers, this is an opportunity to help define how recursive improvement works in practice—with the compute to experiment, the data to learn, and real problems demanding better answers.
Build frontier intelligence where its progress can be measured—and its impact can be felt.
Team
Our team brings together frontier AI researchers and engineers from Meta, Google, Together AI and MIT. Our experience spans model harnesses, post-training, inference, model architecture, and domain-specific application across a wide range of verticals.
Let’s build what’s next.
Contact Evolocity to start a conversation.
info@evolocity.ai