Know whether your training data should proceed — before training begins.
Phronelis Crucible evaluates training artifacts against a declared training or evaluation objective and turns readiness into an evidence-backed technical decision.
Run it inside your own environment or through a managed Cloud Agent. In either deployment, Crucible remains the deterministic authority behind findings, evidence, hard gates and technical outcomes.
A clean dataset can still be unfit for training.
Schema checks can confirm that data is present, valid and correctly formed. They do not, by themselves, establish whether that data is fit to teach or evaluate what you intend.
Crucible evaluates the artifact in the context of its declared objective, then preserves the findings, controls and evidence behind the resulting decision.
Training should not be the first place you discover that the dataset was wrong.
Make training readiness inspectable.
Crucible converts a training artifact and its declared objective into a controlled technical workflow with explicit evidence and decision boundaries.
Declare the objective
Establish what the dataset is intended to teach or evaluate and the decision boundary that applies.
Audit the artifact
Inspect the structure, consistency and evidence relevant to the declared objective, then establish explicit findings.
Control remediation
When remediation is required, produce a controlled Repair plan and bind it to owner authorization.
Create a derived artifact
Execute only authorized repairs against a new derived curated artifact. The original source remains unchanged.
Verify through A/B
Re-audit the evidence-complete result through canonical A/B verification and deterministic comparison.
Clear the final gates
Apply final deterministic validation and applicable hard gates, then produce a Verified Training Handoff only when the evidence permits it.
From training artifact to verified handoff.
- 01Training artifact
- 02Declared objective
- 03Audit
- 04Findings
IF NO REMEDIATION IS REQUIRED
- 01Final validation / applicable hard gates
- 02Verified Training Handoff — only when the evidence permits it
IF REMEDIATION IS REQUIRED
- 01Controlled Repair plan
- 02Owner authorization
- 03Execute only authorized repairs
- 04Create a new derived curated artifact
- 05Re-Audit A
- 06Re-Audit B
- 07Deterministic A/B comparison
- 08Final validation / applicable hard gates
- 09Verified Training Handoff — only when the evidence permits it
Throughout the process, original and derived artifact identities, approval identity, authorization, provenance, evidence continuity, manifests and checksums remain bound and traceable. The original source is preserved and remains unchanged.
One deterministic system. Two ways to operate it.
Choose the operational boundary that fits your organization. The deployment changes where Crucible runs and how the workflow is operated — not the technical authority of the Crucible core.
Run Crucible inside your own environment.
Operate Phronelis Crucible through Desktop or CLI inside infrastructure you control. Keep control of your training data, execution environment and evidence while Crucible performs its deterministic evaluation and governed workflow.
- Customer-controlled data boundary
- Desktop and CLI operation
- Deterministic findings and verdicts
- Reproducible evidence and traceability
- Direct control of execution and artifacts
Governed automation without autonomous technical authority.
Use a managed agentic workflow to coordinate multi-step Crucible operations through cloud infrastructure. The agent can reason about the workflow and orchestrate admitted capabilities; Crucible remains the deterministic authority behind technical findings, hard gates, policy-bound actions and outcomes.
- Managed cloud experience
- Multi-step workflow automation
- Bounded agentic execution
- Evidence continuity across the workflow
- Deterministic Crucible authority behind every technical outcome
Same core. Same technical authority. Different operational boundary.
Give the workflow autonomy. Not authority.
Agentic systems are useful when they can coordinate complex work. The risk appears when orchestration, execution and technical truth collapse into the same layer.
Phronelis separates those responsibilities. In the Managed Cloud Agent, AI coordinates the admitted workflow, bounded execution performs only permitted capabilities, and Crucible establishes the technical result from deterministic evidence.
Managed Cloud Agent
Coordinates the workflow, interprets state and selects among admitted capabilities.
Bounded execution
Performs only the operations available inside the current workflow boundary.
Phronelis Crucible
Establishes findings, hard gates, policy-bound actions, verification and technical outcomes from deterministic evidence.
Managed Cloud Agent orchestration uses Gemini and Google ADK on Google Cloud. These systems coordinate the workflow; they do not replace Crucible’s technical authority.
Evidence, not promises.
In the controlled BANKING77 benchmark, Crucible completed the full audit-to-curation-to-verification workflow under a frozen evaluation contract.
Before
Controlled workflow
After re-audit
These results apply to the frozen BANKING77 benchmark contract covering exact within-split duplicates, exact train/test leakage and exact classification contradictions.
Inspect the full evidenceA decision you can inspect.
Crucible does not reduce training readiness to an opaque model judgment. Measurements, findings, hard gates and the resulting verdict remain tied to the evaluated artifact, objective and conditions.
Metrics are interpreted within the evaluated contract and reported alongside the evidence and verdict they support.
Every decision has a declared boundary.
Crucible evaluates training readiness under a declared artifact, training or evaluation objective and evaluation contract. Its findings and verdict apply to the evidence and conditions evaluated in that run.
The purpose is not to turn uncertainty into a generic score. It is to make the technical decision explicit, evidence-backed and reviewable before training proceeds.
Establish what the evidence allows before you commit the data to training.
Evaluate a training artifact with Phronelis Crucible and choose the deployment model that fits your operational boundary.