Joining Prime Intellect's RL Residency: Building AI That Learns to Solve Small Business Problems

I’m honored to announce that I’ve been accepted into the Prime Intellect RL Residency, where I’ll be contributing open-source datasets, environments, and RL-trained models to help solve real-world problems for small businesses.

For those unfamiliar, Prime Intellect is building full-stack open AGI infrastructure—the Environments Hub already hosts 100+ community-contributed environments for training and evaluating AI agents. The residency provides compute, a stipend, and hands-on support from their research team to accelerate this work. But more importantly, it’s part of a larger mission to democratize AI development and reduce the risks of centralized control.

My focus is specific: bridging the gap between cutting-edge reinforcement learning research and the practical needs of small businesses across America. While most AI innovation targets enterprise software or consumer applications, small businesses—the backbone of our economy—remain largely underserved by modern AI tooling.

The Problem I’m Solving

Small businesses face a unique challenge in the AI era. Enterprise companies have dedicated AI teams and budgets to build custom solutions. Consumer applications get venture funding and massive user bases to justify development costs. But the restaurant owner managing online reviews, the e-commerce shop optimizing product descriptions, or the B2B service company qualifying leads? They’re working with generic tools that don’t understand their specific workflows, constraints, or success metrics.

The irony is that reinforcement learning—where agents learn from experience to optimize specific goals—is perfect for small business problems. A pizzeria needs an agent that learns which review responses turn 2-star ratings into return customers. An outdoor gear store needs an agent that discovers which product description patterns drive conversions while reducing returns. These are well-defined reward functions with clear success metrics, exactly what RL excels at.

But there’s a gap. The RL research community has produced incredible techniques and frameworks, but most benchmarks focus on games, robotics, or abstract reasoning tasks. Meanwhile, small businesses are:

Paying $5-15 per support ticket when 67% could be automated
Achieving only 15-20% email open rates when optimization could double that
Wasting 67% of sales time on unqualified leads

The tooling exists to solve these problems—we just need the environments to train agents on them.

What I’m Building

My contribution to the Prime Intellect Environments Hub is a catalog of dozens of verifier environment concepts across 18 business categories—from customer service and sales to compliance, operations, and crisis management. Each environment defines the interaction protocol, reward functions, and verification logic needed to train AI agents on specific small business tasks.

I’m starting with six priority environments based on interviews with local small businesses over the past few weeks:

1. Email Marketing Campaign Verifier

Learning optimal subject lines, send times, and segmentation for your specific audience. The agent learns which patterns maximize opens, clicks, and conversions for your unique customer base.

2. Review Response Generator

Crafting responses that turn critics into advocates and improve ratings. The agent discovers which empathy patterns, acknowledgment techniques, and recovery offers work best for different complaint types.

3. Product Description Quality Verifier

Optimizing e-commerce listings for both SEO and conversion. The agent balances keyword optimization with compelling copy that reduces returns and increases purchase rates.

4. FAQ Generation & Update Verifier

Automatically maintaining documentation that deflects support tickets. The agent identifies knowledge gaps from customer questions and generates clear answers.

5. Lead Qualification Verifier

Scoring leads in real-time based on your unique conversion patterns. The agent learns which signals predict high-value prospects for your specific business model.

6. Customer Email Response Verifier

Categorizing, prioritizing, and drafting support responses. The agent learns optimal response times, tone, and solution approaches based on customer satisfaction outcomes.

What Makes These Different

What makes these environments unique is the emphasis on synthetic data generation. Most small businesses don’t have massive datasets to train on, so each environment includes 5+ techniques for augmenting limited real-world data—paraphrasing reviews, simulating customer journeys, generating seasonal variations, creating edge cases, and more.

Everything I build will be open source: the environment definitions, the datasets, and eventually the RL-trained models. The goal is to create a flywheel where small businesses contribute anonymized interaction data, the community improves the environments and models, and everyone benefits from better-performing AI agents.

Why Prime Intellect

Prime Intellect’s mission resonates deeply with this work: putting the tools to build AGI into the hands of the many, diffusing chokepoints of control, and reducing the risks of centralization. The Environments Hub is infrastructure for open AGI development—a shared repository where researchers, developers, and domain experts can contribute environments that push beyond traditional benchmarks.

The practical benefits are significant. The residency provides compute for training and experimentation, which is essential when you’re iterating on dozens of environment designs and testing synthetic data generation techniques. More importantly, it provides access to Prime Intellect’s research team and a community of builders working on everything from GPU kernel generation to agentic web research to formal theorem proving. That cross-pollination of ideas—seeing how others structure interaction protocols, design reward functions, and handle evaluation—accelerates learning in ways that solo development can’t match.

But beyond the resources, there’s alignment on values. Small businesses are the definition of decentralized economic power. Giving them access to cutting-edge AI capabilities—not through proprietary black boxes they can’t control or afford, but through open-source tools they can adapt and own—is exactly the kind of democratization that matters.

Technical Approach

These environments are built using Will Brown’s Verifiers framework, which provides a flexible framework for defining custom interaction protocols between language models and evaluation environments. Unlike traditional RL benchmarks that focus on games or robotic control, Verifiers enables multi-turn reasoning, tool use, and interactive evaluation—exactly what’s needed for business workflows.

Each environment defines three key components:

Dataset - Business scenarios with ground truth outcomes (e.g., past email campaigns with their open rates)

Interaction Protocol - How the agent interacts with the environment (single-turn Q&A, multi-turn with tools, stateful workflows)

Rubric - Multiple weighted reward functions that evaluate different aspects of quality

Concrete Example: Review Response Generator

Let me make this concrete with the Review Response Generator environment. The agent receives a customer review—say, a 2-star Google review from a pizzeria customer complaining about cold food. The environment defines rewards for:

Response speed (simulated time to draft)
Acknowledgment quality (does it address the specific complaint?)
Empathy score (tone analysis of the response)
De-escalation success (likelihood to improve rating, based on historical patterns)
Brand voice consistency (matches the business’s established style)

The rubric combines these weighted criteria into a final reward. Over thousands of rollouts with different review types—positive celebrations, mild criticism, angry complaints, edge cases mentioning competitors—the agent learns which response patterns maximize retention and reputation recovery.

And because we generate synthetic variations (same complaint with different sentiment levels, different complaint types, platform style variations), a small business with just 50 real reviews can train on thousands of scenarios.

We Want to Hear From You

This work only succeeds if it’s grounded in real-world needs and built with community input. I’m looking for collaboration on two fronts:

For Small Business Owners

If you’re struggling with any of the problems I mentioned—review management, email marketing, lead qualification, customer support, product descriptions—or have other pain points you think AI could help with, I want to hear from you.

Your feedback directly shapes which environments get prioritized and how they’re designed. You can share your challenges through our RL Problem Submission form, and all conversations are confidential.

We’re specifically looking for businesses that:

Have repetitive tasks that require optimization
Face decisions that need to be made frequently
Want to improve outcomes but don’t have clear rules to follow
Have some data (even limited) about what works and what doesn’t

For RL Researchers & Developers

These environments are open source and designed to be extended. If you’re interested in contributing environment designs, improving reward functions, testing synthetic data techniques, or training models, the environments will be published to the Prime Intellect Environments Hub as they’re completed.

Supporting code will be on my GitHub, and I’ll be sharing progress updates on X/Twitter and publishing trained models to HuggingFace.

Next Steps

Over the next few months, I’ll be implementing the six starter environments, running initial training experiments, and iterating based on feedback. The goal is to have working demonstrations that small businesses can actually deploy—not just academic benchmarks, but practical tools that improve real operations.

I’ll be documenting the journey here on the Appalach.AI blog, sharing:

Environment design decisions and trade-offs
Synthetic data generation techniques that work
Training results and performance metrics
Real-world deployment stories from beta testers
Lessons learned from bridging research and practice

The Opportunity Ahead

The opportunity to work with Prime Intellect on this is genuinely exciting—not just for the resources and support, but for what it represents. AI development shouldn’t be concentrated in the hands of a few massive companies. The businesses that form the backbone of our communities deserve access to the same cutting-edge capabilities that enterprises take for granted.

If we can build these environments right—grounded in real needs, open for anyone to use and improve, and practical enough to deploy in actual businesses—we have a chance to shift how small businesses compete in an AI-driven economy. Not by replacing human judgment, but by giving people better tools to focus on what they do best while agents handle the repetitive, optimizable tasks that drain time and resources.

I’m grateful for this opportunity and excited to build in public. Let’s see what we can create together.

Ready to share your business challenge? Visit our RL Problem Submission page to describe a problem you’d like AI to help solve. We’ll assess whether reinforcement learning is a good fit and keep you updated as we build these solutions.