25+ years of RL research applied to real business problems. We bridge the gap between academic theory and production deployment — depth consultancies can't match, velocity academics never deliver.
Typical engagements start at $250/hr · Project work from $50K
Four core offerings, each grounded in formal methodology. Pick one engagement or stack them — we scope everything to fit your timeline and budget.
End-to-end reinforcement learning for your business problem. We formalize your environment as an MDP, select and tune the right algorithm, train the policy, and deploy it to production. You get working code, full documentation, and a team that understands it. We use industry-standard algorithms and our own peer reviewed algorithms that mimic biological intelligence.
Autonomous decision-making for complex, sequential business processes. Pricing engines, supply chain optimization, adaptive recommendations, resource allocation. Systems that improve over time without manual re-tuning. Our peer-reviewed algorithms mimic decision making in brains.
Independent, research-grade evaluation of your existing models and methodology. We find the flaws, quantify the risk, and give you a remediation roadmap. Useful before a major launch, a fundraise, or when something is inexplicably underperforming.
You have a model. It works, but not well enough. We benchmark it rigorously, identify the failure modes, and optimize performance — whether that means better features, better training, or replacing the algorithm entirely.
25+ years building the mathematical foundations that other researchers cite — with 70+ peer-reviewed publications cited over 6,500 times.
Computational modeling, neural networks, reinforcement learning, and the formal study of how systems learn from experience. Not as a practitioner following frameworks — as an architect who built some of them.
Formal models of associative learning and reward-based policy optimization. Research that predates and informs modern deep RL — 20+ years of primary contributions.
Quantitative models of complex adaptive systems. The same mathematics that describes biological learning also describes how machines should learn from feedback.
Architecture, training dynamics, and generalization. Research-grade understanding of why neural networks succeed and fail — not just how to run them.
Bridging formal statistical theory and real-world data science practice. The difference between models that look good in papers and models that work in production.
Most RL consultancies are one of two things: academics who've never shipped production code, or engineers who've never read a paper. DataWorks is neither — and both.
We don't just recommend an algorithm and leave. We stay until it works, explain why it works, and make sure your team can maintain it.
Research-backed implementations for real commercial problems — performance engineering, model auditing, predictive analytics, and cross-source data engineering.
This engagement demonstrates what "research-backed implementation" means in practice: porting a complex, patent-specified inference algorithm to production-grade C while preserving every semantic detail — including subtle correctness properties like fallacy-prevention in backward propagation that are easy to get wrong. The result is a >100× speedup and >10× memory reduction, unlocking production workloads that were previously infeasible. The same approach applies to any client who has a working prototype in a high-level language and needs production performance without sacrificing correctness.
| Network Size | C Engine | Previous Engine | Speedup |
|---|---|---|---|
| 5k nodes | 0.003s | 0.384s | 131× |
| 10k nodes | 0.006s | 0.750s | 128× |
| 50k nodes | 0.031s | 3.905s | 128× |
| 100k nodes | 0.079s | 12.361s | 156× |
| Memory (5K) | 3.3 MB | 12.2 MB | 3.7× less |
| Memory (100K) | 22.3 MB | 249.0 MB | 11.2× less |
Benchmark auditing applicable to LLM evaluation, AI due diligence, and stress-testing "the model can reason" claims.
Diffuse cultural-choice data as leading indicators for brand sentiment, product reception, and public-figure risk — decades before traditional polling catches up.
Fragmented multi-source evidence → defensible schema → single model → actionable verdict. Directly applicable to multi-vendor benchmark audits and cross-study synthesis.
No retainer traps. No surprise overruns. We scope tightly and stick to it.
Architecture review, technical advisory, model evaluation, or any focused work that doesn't require a full project scope. Good for getting unstuck fast.
Book time →Full RL system design and implementation. We define the scope, agree on milestones, and deliver. Typical engagements run 6–16 weeks depending on complexity.
Get a scope →8 hours/month of dedicated access. Code review, architecture guidance, and a direct line to a 25-year RL expert when your team needs backup.
Start a conversation →We respond within one business day. If the project is a good fit, we'll set up a 30-minute call to scope it properly.
We'll review your project and respond within one business day.