Risk reviews, residency rules, and audits stall good work before production.
Agent analysis rarely produces evidence a reviewer can stand behind.
Repeated data prep, manual experiments, and idle compute eat your capacity for new use cases.
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.
3
Decision layer
Evidence to decision.
Insights, drivers, and a plan — every claim traceable to data, experiment, and code.
Human-in-the-loop approval with full decision history.
Board-ready output, still wired to its lineage.
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.
1
Bring your own LLMs — no forced dependence on a single closed model.
2
Connect seamlessly to your data platforms and compute infrastructure, including Databricks, Snowflake, approved compute targets, and artifact stores.
3
Standard onboarding is deployment, identity and network access, credential configuration, and permissions — not bespoke engineering
4
Your data and proprietary context remain within your approved infrastructure.
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