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. Expand any card for engagement models, pricing, and delivery details.
End-to-end RL for your business problem. We formalize your environment as an MDP, select and tune the right algorithm (PPO, SAC, custom bio-inspired), train the policy, and deploy it to production. You get working code, full documentation, and a team that understands it.
Problem formalization as a Markov Decision Process. State and action space definition. Reward signal design. Algorithm landscape review (PPO, SAC, DDPG, custom bio-inspired). Feasibility assessment and implementation roadmap. Delivered as a technical report your engineering team can act on — regardless of whether you proceed with us.
End-to-end training pipeline, environment simulation, policy optimization, convergence analysis, and production deployment. Includes comprehensive test suite, failure mode documentation, and team knowledge transfer. Pricing depends on environment complexity and integration requirements.
Continuous policy monitoring, model drift detection, periodic retraining, and performance benchmarking. Keeps your RL system performing as the environment evolves. Includes 8–12 hours/month of direct access and priority support.
Phases can be engaged independently. Most clients begin with a Research Audit to de-risk before committing to implementation.
Autonomous decision-making for complex, sequential business processes. Systems built on peer-reviewed algorithms that mimic decision-making in biological brains — improving over time without manual re-tuning.
Our Decision AI algorithms are grounded in 25+ years of peer-reviewed research on how biological systems make sequential decisions under uncertainty. We bring formal mathematical rigor to problems where off-the-shelf RL frameworks make hidden assumptions that break in production. View publications →
Engagements scoped and priced per project. Typical range: $75K–$250K depending on system complexity.
Research-grade data science: experimental design, statistical modeling, and cross-source data curation. We find what your current methodology is missing — and build the infrastructure to get it right.
In a published case study, we harmonized 108 experiments from 14 species across dozens of independent labs into a single unified model — achieving mean correlation 0.88 and 5.9% MAE with no per-study tuning. The same methodology applies to multi-vendor benchmarks, cross-geography analytics, and any environment where data sources weren't designed to talk to each other.
Audit engagements start at $15K. Full analytics projects scoped per requirements.
Strategic guidance for companies evaluating whether RL and ML belong in their product. You get a 25-year expert on call — not to write code, but to make sure the direction is right before you spend the budget.
8 hours per month of dedicated strategic access. Direct line to Stefano Ghirlanda, PhD for architecture decisions, vendor assessments, team interviews, and technology roadmap review.
Most advisory clients convert to project work within 3–6 months. The retainer is intentionally structured so you can move from strategy to implementation without switching providers.
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.