One-shot AI isn't enough. Ungoverned AI isn't an option.
Move faster on AI or keep tighter control of your data. Most tools make you choose. PiEvolve doesn't.
1
Speed without control never ships
Risk reviews, residency rules, and audits stall good work before production.
2
Your context leaves the building
Generic assistants need your sensitive data in someone else's environment.
3
One-shot pipelines miss the drivers
Outputs, not the patterns hidden in complex enterprise data.
4
Answers aren't decisions
Agent analysis rarely produces evidence a reviewer can stand behind.
5
Cost compounds quietly
Repeated data prep, manual experiments, and idle compute eat your capacity for new use cases.
6
Governance arrives last
Bolted on as a final gate instead of built into the workflow.
1
Grounded and cost-efficient
Domain-grounded operations with data-science tools built for large-scale data, cutting agent cost.
2
Iterative optimization
Iteratively optimizes ML pipelines and combinatorial optimization problems, and evolves solutions.
3
Data prep to reports
Data-prep and research agents make raw data ML-ready, then generate technical and business reports.
How PiEvolve comes together
Evolutionary engine
1
Sovereign workplace
Controls by default, not by review.
Runs in your VPC, on-premise, or air-gapped. Your models, your compute, your boundary.
Least-privilege access, PII masked at source, policy-as-code guardrails, human-in-the-loop on high-stakes calls.
Full provenance; every run, artifact, and output auditable.
2
Domain grounding and autonomous optimization
Iterative search across ML and combinatorial problems
Agentic data prep and grounded deep research; messy data to ML-ready, with real data-science tooling.
Evolutionary search over ML pipelines and NP-hard problems. Every run reproducible.
Rank-1 on OpenAI MLE-Bench
Months to Days
End-to-end ML lifecycle
Lower cost
Fewer tokens, DS-specific stack
The end-to-end lifecycle
1
Raw data
2
ML-ready data
3
Modelling
4
Insights & reports
Cost-efficient by design: a data-science- and ML-specific ecosystem means lower token usage than general-purpose agents.
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