From pilot to production, without the compliance gap.
Juniper continuously evaluates production AI systems and generates the audit-grade documentation required by regulators. Built for compliance leads and the engineers shipping alongside them.
Ship with speed
Move fast without breaking compliance.
Juniper gives you a single pane of glass to automate AI evaluation, surface risks early, and continuously prove trust.
Compliance posture
84% readyEU AI Act · Annex IV
- Sections
- 21 / 25
- Attested
- 18
- Stale
- 2
Mapped controls
5- 5.1Data governanceAttested
- 5.2Fairness assessmentDrift
- 5.3Human oversightAttested
- 5.4Accuracy & robustnessAttested
- 5.5Logging & traceabilityAction
Evaluations · last 24h
3Annex IV §5.2 is drifting — fairness delta hit 2.3% on run #142.
Looking now. Pulling the last two fairness runs.
Root cause: demographic shift in approved cohort (+4.1% 60+). Draft mitigation attached.
Integrate
Drop the SDK into any agent, auto-instrument your traces, and scrub sensitive data.
1import juniper_sdk2 3juniper_sdk.init(4 api_key="jun_••••",5 agent_id="credit-risk",6)7 8@watch9def predict(features: dict):10 return model.predict(features)Evaluate
Continuously score every agent on task, tools, policy, conversation quality, and safety.
Attest
Juniper automatically maps evaluations to controls, identifying risks and flagging gaps.
- 1Fairness assessmentNeeds attentionAttestedAt risk
- 2Logging & traceabilityNeeds attentionAttestedAt risk
- 3Human oversightNeeds attentionAttestedAt risk
- 4Accuracy & robustnessNeeds attentionAttestedAt risk
Audit-grade evidence
Proof is a click away, not a fire drill.
Ground every claim in a hashed, timestamped evaluation that your auditor can verify in one click.
Describe how you monitor your model for fairness drift across protected attributes.
[free-text response] We monitor our model for fairness through quarterly internal reviews. The data-science team computes parity metrics across protected groups and escalates anomalies to risk. Drift thresholds are documented in our model-governance handbook…
- Last reviewed
- 4 months ago
- Next due
- Q3 · audit window
No link between this answer and what the model actually did last Tuesday.
§5.2 Fairness assessment
EU AI Act · Annex IVCredit-risk-model is monitored against a stratified fairness holdout v3.2 · ev_8hp0 of 1,200 applicants. The most recent evaluation run #142 · ev_2k4j returned a demographic-parity delta of 1.8%, below the 2.0% threshold. Each prediction is captured as a structured span trace · ev_1m3d with input features, protected-attribute distribution, and outcome.
- ID
- ev_8hp0
- Version
- v3.2 · 1,200 rows
- Cohort
- stratified by age × gender
- Hash
- sha256:c1a4002f9b…
One source of truth
Write evidence once. Pass every audit.
Juniper's evidence maps to every framework you will face so you don't redo the work each time a new one lands.
- ID
- ev_8hp0
- Version
- v3.2
- Rows
- 1,200 stratified
- Hash
- c1a4002f9b…
- MappedEU AI ActAnnex IV§5.2 · Fairness assessment
- MappedNIST AI RMFMap functionMAP-1.1 · Context & risk
- MappedISO/IEC 42001AIMS§8.4 · Operational planning
- MappedColorado AI ActSB24-205§6-1-1701 · Algorithmic discrimination
- MappedNYC LL 144AEDTBias audit · annual
Integrations
Works with your stack
Connect your agents in seconds, run evaluations in minutes, produce audit-ready compliance artifacts.
