Explore more ways to improve your production system.
Pavo develops and evaluates competing changes across prompts, workflows, heuristics, models, training, and optimization, then carries the strongest candidates toward production.
Interventions
The worldview
The best production improvements are discovered through exploration, not assumed in advance.
Every production system has more ways to improve than any team can explore manually. Pavo expands the credible search space while concentrating effort where evidence is strongest.
Four stages, and most candidates stop before production.
Investigate credible ways to improve the system across prompts, agent behaviour, workflows, heuristics, data, features, models, training, and optimization.
Turn multiple promising directions into lightweight prototypes and working candidates at the same time, reusing data, infrastructure, and learning across branches.
Evaluate early, stop weak branches, and progressively invest in approaches showing the strongest benchmark performance, mechanism fit, feasibility, and guardrail behaviour.
Refine the leading candidates, harden implementation, resolve difficult cases, validate second-order effects, and create the production experiment and rollout plan.
Stage 01 · Explore
Explore broadly
Investigate credible ways to improve the system across prompts, agent behaviour, workflows, heuristics, data, features, models, training, and optimization.
The tournament, in full
Many lines explored, most closed with a reason, one proven in production.
Candidates scored against the benchmark, learnings carried across dead ends, and the result written back.
State-of-the-art AI
From prompt optimisation to reinforcement learning.
Pavo performs intervention work across agents, training systems, models, and production infrastructure to turn promising ideas into production-ready improvements.
- 01
Agent & Workflow Optimisation
Prompts, context, retrieval, tools, routing, memory, planning, orchestration, and human handoffs.
- 02
Data & Training Systems
Training datasets, synthetic data, labels, features, pipelines, GPU training, and experimentation infrastructure.
- 03
Models & Learning
Model training, fine-tuning, rankers, classifiers, reward models, preference learning, bandits, and reinforcement learning.
- 04
Production Optimisation
Inference, latency, cost, model routing, serving, rollout, monitoring, and production performance.
Intervention examples
Change the layer that constrains the outcome.
- 01
Agent workflows
Improve tool selection through prompt changes, validation logic, workflow redesign, learned routing, fine-tuning, or reward-based policies.
- 02
Recommendation
Improve content allocation through exploration heuristics, new features, objective changes, contextual bandits, or constrained reinforcement learning.
- 03
Search
Repair tail-query performance through candidate-generation changes, hybrid retrieval, reranking, fine-tuning, or optimization of the full search policy.
- 04
Pricing
Increase conversion without leaking margin through eligibility rules, uplift models, constrained optimization, or journey-aware sequential policies.
The result is not another generated suggestion. It is an intervention that has earned the right to be tested in production.
Research behind production interventions.
Decision systems, causal inference, reinforcement learning, recommender systems, and the foundations for exploring changes under production constraints.
FAQs
An intervention is any deliberate change intended to improve a production outcome: a prompt, workflow, heuristic, feature, model, training objective, reward model, policy, or optimization strategy.
Pavo develops and evaluates working candidates alongside your systems and team. The exact implementation boundary is agreed with you, from prototypes and evaluation assets through production-ready changes and rollout plans.
Pavo begins with the opportunity, the suspected mechanism, system constraints, prior evidence, and feasible intervention families. It expands the credible search space without treating every generated idea as equally worth building.
Candidates share data, infrastructure, benchmarks, and learning. Pavo evaluates early, stops weak branches, and deepens only the directions showing promising performance, fit, feasibility, and guardrail behaviour.
Yes. Interventions can target agents and workflows as well as recommendation, search, pricing, ranking, and other production ML or decision systems.
Serious candidates are compared on production-grounded benchmarks, simulations, slices, guardrails, cost and latency checks, and offline-to-online evidence. The leading candidate also receives an experiment and rollout plan.
Your team retains control over access, objectives, constraints, implementation, and what enters production. Pavo makes the evidence and trade-offs explicit so decisions remain reviewable.
Failed candidates are useful evidence. Their results, constraints, and failure modes are written back into System Knowledge so the next investigation does not repeat the same dead ends.