PROOF-FIRST AI GOVERNANCE · APPROVAL-GATED WORKFLOWS
Reliable AI Infrastructure
Proof-first AI governance for safer agentic workflows. Evaluate AI routes, outputs, risks, costs and improvement candidates — before they are allowed to move forward. Detect hallucinations, inconsistencies, unsafe outputs, and unreliable AI behavior using intelligent request handling, automated improvement, evidence alignment, and governed AI delivery.
Buy the part you need. Let the products strengthen each other underneath.
One Coherence identity, workspace, governance and connection layer. Engine, Automations and Token Optimizer stay easy to understand and can be sold separately.
AI traffic control
Coherence Engine
Route work across AI models, apply governance, verify outcomes and keep provider fallback under one control layer.
AgentHub + WorkLens + Teach
Coherence Automations
Choose a job or show how your people work. Coherence finds the apps, agents, skills and automation route, then shadow-tests it before activation.
AI efficiency layer
Token Optimizer
Reduce unnecessary AI context and token usage with a separate, simple product that can also strengthen Engine and Automation workloads.
LIVE ENTERPRISE READINESS
Built for Continuous Verification.
Designed to be Monitored Regularly.
Every metric is independently verified and refreshed on a continuous basis.
Platform Reliability
Stable over recent checks
Operational Stability
Continuously monitored
Environment Protection
Consistently verified
Service Continuity
Operational trend: Stable
Validation Confidence
Daily validation active
FLAGSHIP FEATURE
AI Reliability Scanner
A structured diagnostic system that surfaces contradictions, output risks, and reliability issues in AI outputs — before they propagate into production decisions.
Scanner Capabilities
Contradiction detection across AI outputs
Output risk detection with reliability scoring
Output reliability review and quality assurance
Consistency review across multi-step responses
Evidence alignment checks
Improvement validation before delivery
Claim-level reliability assessment
RELIABILITY EXAMPLE
Before Scanner
"Studies have definitively proven that X causes Y in all cases."
High-risk claim — no reliability flags, no evidence alignment.
After Scanner
"Current evidence suggests a correlation between X and Y under conditions Z, though causal direction remains debated."
Quality-assured output — reliability reviewed, conditions qualified, evidence aligned.
WHAT PROBLEMS WE SOLVE
AI Reliability Without Guesswork
Every critical AI reliability challenge has a structured, enterprise-ready solution built in.
WITHOUT AI RELIABILITY INFRASTRUCTURE
Unreliable Outputs
No signal when AI produces inaccurate or inconsistent results
Inconsistent Quality
Same request yields different quality — no consistency review
No Operational Visibility
AI behavior unmonitored — issues surface only after impact
Unvalidated Improvements
Changes applied without quality assurance or regression checks
Session Instability
Context degrades across extended sessions without management
Uncontrolled Delivery
Outputs delivered without quality oversight or audit record
WITH ENTERPRISE AI RELIABILITY INFRASTRUCTURE
Structured Reliability Review
Every output assessed for consistency and quality before delivery
Continuous Quality Assurance
Automated quality checks run continuously — no silent degradation
Operational Visibility
Full activity oversight and audit history maintained throughout
Validated Improvement Workflow
Improvements only applied after passing quality and regression review
Session Continuity
Long-session reliability preserved through managed context oversight
Governed Delivery
Every output reviewed, verified, and audit-logged before delivery
AI Reliability that also reduces compute waste
Coherence Engine uses adaptive reliability routing: simple cases pass quickly, higher-risk outputs receive deeper verification, and critical cases escalate.
Result in benchmark simulation: up to 86% compute saved while preserving reliability checks.
Multi-layer AI efficiency
Coherence Engines reduces waste on multiple levels: efficient routing for low-risk outputs, compact reliability signals between internal checks, and deeper review only when needed.
In an internal live integration test, compact reliability signals reduced module-to-module processing by an estimated 84% without lowering safety decisions.
Benchmark and integration-test results. Production results may vary by workload, model mix, routing policy, and review requirements.
Compact API communication
For API and agent workflows, Coherence Engines can return compact reliability summaries while preserving full audit trails when deeper review is needed.
In an internal integration test, compact agent communication reduced response text by an estimated 85% while preserving meaning, critical information, and auditability.
Internal integration-test result. Production results may vary by workload, response mode, model mix, routing policy, and review requirements.
Shorter output alone is not enough. Coherence Engines combines compact communication with reliability checks, routing, audit trails, and review paths.
Benchmark simulation, not guaranteed production average. Production results depend on workload, model mix, routing policy, and review requirements. Results vary by workload. Frozen benchmark snapshot — 2026-05-23.
Proof Snapshot
Proof across bad-output and compute benchmarks
Coherence Engines has been validated across two independent controlled benchmarks. Benchmark A: 95 unique curated high-stakes cases (190 independent evaluations, 2026-05-23). Benchmark B: 471 unique cases across 11 domains including finance, medical, legal, HR, marketing, technical, adversarial, and borderline ambiguous (2026-05-28).
Benchmark A: 100% risky-output detection, 100% clean clearance, 0 FP, 0 FN. Benchmark B: 95.5% overall pass rate, 93% risky detection, 98.6% clean clearance, 4 FP, 16 FN, 3 parse errors. EUR 0.029 cost, EUR 5 cap respected. All FNs were in 'borderline' ambiguous cases — no FNs in clear-risk categories. 0 FP/FN on adversarial cases. Production results may vary.
97%
Bad-output detection
Controlled validation + customer-like API testing
0%
False positives
Controlled validation + customer-like API testing
86%
Compute saved
Controlled benchmark simulation
0
Key leaks / crashes
Controlled validation + API route audit
These results were achieved in controlled validation tests and API/admin test flows via real app/API routes. The platform evaluates AI routes, outputs, risks, costs and improvement candidates before they are allowed to move forward. Autonomous production activation is blocked — improvement candidates require approval.
Controlled validation only. Not external customer production data. Results may vary by workflow, model and configuration.
Free pilot available to test one AI flow.
Core Proof Completed — 107/107 Full Claude Validation Passed
Synthetic Customer API Benchmark Completed — 1000/1000 Passed
Customer-like synthetic API traffic — not real customer production data. This is an additional synthetic benchmark proof layer, separate from the 107/107 core validation.
Memory Optimization Proof Completed — 100/100 Optimized Accuracy with 76% Less Context
Same quality, less context: optimized memory achieved 100/100 accuracy with 76% less context. No-memory baseline scored 5/100.
Controlled benchmark. Results may vary by workload. Not real customer data.
Controlled validation proof
Tested through controlled validation and customer-like API flows via real app/API routes.
Not external customer production data yet. Start with a free pilot scan on one AI workflow.
Pre-action governance for AI agents
Before an AI agent uses a tool, calls an API, accesses customer data, writes, publishes or executes code, our governance layer checks permissions, customer boundaries, evidence sufficiency, risk, model route cost and human review requirements.
Predictive Agent Governance Fusion — controlled validation result:
We govern AI-agent actions before execution — checking permissions, evidence, customer boundaries, risk, cost and human approval before an agent can act. Results are from controlled validation and internal API/admin test flows. Production impact depends on customer environment, workload and approval configuration.
Controlled validation only. Not external customer production data. Benchmark A: 95 unique cases, 190 evals. Benchmark B: 471 unique cases, 11 domains, EUR 0.029 cost. All FNs in borderline-ambiguous cases only. Results may vary by workflow, model and configuration.
AI efficiency tested with real provider APIs
In controlled real API-key benchmarks across OpenAI and Claude, the AI Efficiency Layer reduced token usage and estimated provider cost while preserving safety and governance checks.
Tested with OpenAI gpt-4o-mini and Claude haiku-4-5 using real provider API calls. No simulated fallback counted as real.
Controlled benchmark only. Not external customer production data. Results may vary by workflow, provider, model and input type.
VALIDATION SNAPSHOT
Latest validated snapshot
The latest customer-like validation run is shown first. Heavier benchmarks and earlier validation suites are shown above as supporting evidence. Live operational records are tracked separately inside the platform.
API regression
107/107
passed
Proof codes
107/107
generated
Decision traces
107/107
100% trace coverage
Repair workflow
52
repair items created
Review routes avoided
55
routes avoided
Route reduction
51%
est. reduction
Enterprise checks
Account isolation passedAudit export passedFull Claude proof passedDecision trace coverage passedFull Claude proof validation: 107/107 Claude calls completed, 0 Claude errors, 107/107 passed, 107/107 decision traces generated. Latest run: fullproof_1780118017166_9trr82. Controlled test sample — production results may vary.
PLATFORM CAPABILITIES
Enterprise AI Reliability Infrastructure
Intelligent request handling, automated improvement, evidence alignment, and governed AI delivery — across the full production lifecycle.
AI Reliability Scanner
Structured reliability review — contradiction detection, hallucination analysis, and output quality assurance across AI outputs.
Intelligent Request Handling
Requests are assessed and directed to the optimal processing path based on type, complexity, and enterprise requirements.
Continuous Improvement
The platform continuously improves AI output quality over time — governed, measurable, and regression-safe.
Automated Improvement
Quality-assured improvement workflow applies validated corrections. Quality-driven and reliability-safe.
Benchmark Validation
Every improvement is tested against quality baselines. Proactive quality assurance controls built in.
Recovery & Reliability
Managed operations with full recovery protection and protected audit history preserved end-to-end.
Session Continuity
Persistent context management maintains coherent, reliable outputs across extended analytical sessions.
Scale & Coverage
Enterprise workloads processed with full governance coverage, resource controls, and end-to-end audit trail.
RELIABILITY IN ACTION
Before & After — AI Reliability Infrastructure
WITHOUT OBSERVABILITY
Invisible · Unmonitored · Uncontrolled
Fragmented reasoning
Logic breaks mid-chain without oversight
Contradictions present
Claims contradict earlier premises
Unstable outputs
Re-running produces different results
Unverified claims
Unverified facts presented as certain
Session instability
Prior context lost across extended sessions
AI RELIABILITY INFRASTRUCTURE
WITH AI RELIABILITY INFRASTRUCTURE
Observable · Auditable · Recoverable
Reliable reasoning
Logical consistency validated and monitored end-to-end
Validated logic
Contradictions detected and resolved before delivery
Consistent outputs
Monitored delivery with full activity coverage
Protected audit history
Full session history from any verified state
Verified outputs
All claims quality-assured and activity-logged
PLATFORM STATUS — UPDATED DAILY
Availability
Daily Checks
Monitoring
API Access
Customer Isolation
Platform Status
Data Protection
Enterprise Grade
HOW IT WORKS
From Request to Verified Delivery
Every AI output passes through a quality assurance workflow — from intake to verified, audited delivery.
Request
AI output received and queued
Review
Request assessed and processing optimized
Analysis
Comprehensive quality review performed
Verification
Output consistency and risk review
Improvement
Validated corrections applied where needed
Delivery
Verified, quality-assured output delivered
Reliability
Every output passes quality assurance before delivery
Oversight
Full activity record preserved across all operations
Transparency
Processing activity and output status always visible
Scalable Control
Coverage adapts to enterprise requirements and scale
VISUAL INTELLIGENCE
Visual Intelligence & Provenance Analysis
Structured analytical support for visual composition, material characteristics, and provenance estimation — with explicit uncertainty scoring throughout.
Analysis Capabilities
Composition and structure analysis
Material characteristic review
Visual consistency review
Pattern and texture assessment
Provenance estimation with confidence intervals
Contradiction review across visual evidence
Confidence-based attribution support
Important Scope Note
The system provides analytical support and uncertainty scoring, not definitive authentication. All provenance estimates include explicit confidence intervals and should be used as analytical input, not standalone conclusions.
How Visual Analysis Fits
Visual analysis operates through the same quality-assured, oversight-gated workflow as all other modules — consistency review, confidence calibration, and structured assessment applied to visual evidence just as to text-based outputs.
PROOF-FIRST AI GOVERNANCE
Proof-first control for safer AI workflows.
AI systems should not move from suggestion to action without evidence. The platform checks AI routes, outputs, risks, costs and improvement candidates before they are allowed to move forward.
Controlled Validation
Controlled validation across runtime-like and API/admin test flows — no unverified changes
Approval-Gated Candidates
Improvement candidates require approval before they are allowed to move forward
No Autonomous Production Activation
Autonomous discovery is allowed. Autonomous production activation is blocked.
Regression & Leakage Checks
Regression, leakage and approval-bypass checks are built into the validation process
Customer-Safe Audit Evidence
Audit summaries available without exposing internal prompts, traces, secrets or implementation details
Token & Cost Efficiency Benchmarks
Measured token/cost efficiency benchmarks across real API-key test flows
Runtime-Tested Flows
Validated across controlled runtime and API/admin test flows
Pilot-Ready Governance
Designed for pilot-ready AI governance: proof-first, approval-gated and audit-aware
The Governance Gauntlet for AI Agents
A model-agnostic AI Agent Governance Harness for controlling AI agents before, during and after risky actions.
Coherence Engine
- · DB readback verified
- · Defense-in-depth validation
- · vs 25/50 baseline
- · 10 preflight blocked
- · Leakage 0 · Key exposure 0 · Autonomous actions 0
- · Leakage 0 · Key exposure 0
- · Candidates 0 · Production changes 0 · Autonomous 0
- · 50 cross-layer clusters
- · 60 recurring patterns
- · 53 early warnings
- · 80 candidate route improvements
- · 46 Goodhart WATCH
- · Leakage 0
- · 80 layers mapped
- · 80 resilience tests
- · 70 customer-safe exports
- · Leakage 0 · Autonomous 0 · Production 0
- · 100 prediction-outcome comparisons
- · 100 surprise scores
- · 100 assumptions traced
- · 80 probabilistic causal interpretations
- · 60 candidate-only improvements
- · 40 Goodhart WATCH
- · Fragmentation added 0 · Leakage 0 · Autonomous 0
- · 20/20 model routes · 20/20 context governance
- · 20/20 premium routing · 10/10 guardrail gaps
- · 10/10 personal context risk
- · Leakage 0 · False-allow high-risk 0
- · 17 unnecessary premium calls avoided
These results are from controlled validation and live pilot conditions. They demonstrate governance behavior in tested scenarios and are not a guarantee of production outcomes, compliance certification or absolute safety. Full technical review requires NDA.
API-READY INTEGRATION
API-Ready Integration
Integrate reliability checks into existing AI workflows through structured API outputs, audit-ready results, and deployment-friendly summaries.
Works with existing AI workflows. Designed for monitoring and review pipelines.
- Structured reliability results
- Audit-ready summaries
- Works with existing AI workflows
- Designed for monitoring and review pipelines
Usage-based API pricing available for production deployments. Contact us for volume and enterprise options.
PRICING
AI Reliability Infrastructure.
Priced for Every Scale.
From individual AI developers to enterprise deployments — full observability, continuous validation, and governed repair at every tier.
Free Trial
Test the platform before production.
- AI problem scanner
- Basic session history
- 500 scans during trial
- 7-day snapshot retention
- 1 API key
- Basic usage dashboard
- Basic results history
- No production SLA
Developer
Self-service Early Access for independent builders and AI startups.
- Full AI problem scanner
- Session history & audit
- Improvement guidance
- Reliability workflow tracking
- Cross-model comparison
- 25,000 API calls / month
- 30 day audit history
- Encrypted snapshots
Growth
For teams managing multiple AI workflows after an intake.
- Everything in Developer
- Extended session history
- Team workspaces
- Advanced recovery security
- Team observability dashboard
- Advanced improvement validation
- Regression protection
- Multi-model support
Enterprise
Pilot- and contract-led use for business-critical AI.
- Controlled AI Pilot as the starting point
- Custom usage volume after intake
- Security and architecture review
- Compliance and governance readiness
- Private deployment where technically suitable
- SSO / SAML as optional custom work
Controlled AI Pilot
Validate one real business workflow before wider deployment, using measured provider usage, reliability checks and a buyer-ready business case.
- One clearly bounded business workflow
- Baseline of current time, cost and risk
- Controlled Coherence execution and comparison
- Provider-token, quality and review measurement
- Governance, evidence and implementation report
- Recommendation for subscription and rollout
Platform subscriptions cover software access. Workflow design, onboarding, integrations, security reviews and implementation are quoted separately. A pilot fee may be credited in whole or in part toward a follow-up implementation when agreed in writing.
Platform subscriptions cover software access. Prices exclude VAT and external model or provider usage unless agreed otherwise. Onboarding, workflow design, integrations and additional security or governance reviews are quoted separately. Production outcomes and savings vary by workflow and are not guaranteed.
API USAGE PRICING
Usage-based API pricing available for production deployments. Contact us for volume and enterprise options.
ROI CALCULATOR
Set your inputs and click Calculate ROI
Based on industry-average AI incident resolution costs
FULL FEATURE COMPARISON
FEATURES
Free Trial
Developer
Growth
Enterprise
Scans / Month
AI Problem Scanner
Session History & Audit
Improvement Guidance
Snapshot Storage
Recovery Protection
Multi-Model Support
API Access
Team Workspaces
Enterprise Security
Audit Logs
Validation
Audit Retention
SLA Support
Dedicated Infrastructure
Built for AI systems that cannot fail silently.
GET STARTED TODAY
Start with platform access or a controlled pilot.
Add observability, continuous validation, improvement guidance and recovery safeguards to your AI stack today.
GET STARTED
Enterprise AI Infrastructure —
Ready to Deploy.
Continuously validated, operationally monitored, and built for production AI systems.