AI Reliability by Design.
Verify AI outputs against your trusted facts before they reach enterprise applications—with a deterministic release gate and an inspection record for every decision.
7-scene guided walkthrough
🛡️ Proven Engineering: 76 Automated Test Suites · 1,655+ Continuous Checks
Rules-Based Verification
Audited by code, not guessing AI
Interactive Workflow Builder
Save and load custom verification pipelines
Blocks Errors by Default
If facts don't match 100%, it halts
Cloud Live, VPC Ready
Fast cloud today, air-gapped on roadmap
Interactive Demo
Recorded demonstration mode on fictional ground truth. Switch tabs to explore each shipped workflow.
A customer AI or RAG system generated an answer. ModelFusion checks it against customer ground truth with 0 model calls.
Supplied Fact Sheet
External answer
"Data processed exclusively in the United States."WITHHELD — NOT RELEASED
Conflict detected on Data Residency (Expected: Canada, Received: United States). Answer withheld; 0 model calls made.
Pre-recorded outcome from Slice 9 (PR #71). Not a live system response.
Architecture
Four combinations plus a section-scoring module are in production. The bounded builder is in private preview.
Verify existing AI/RAG answers before application release. 0 model calls, deterministic rule evaluation, no generative step in the check.
Multi-model generation (Haiku 5.5, DeepSeek V4.1 Flash, GPT-6.1 Sol) with non-text active screening by TypeSafe Jev, discarding conflicting drafts before synthesis.
Proposer panel with structured meta-judge analysis across 5 dimensions: Consensus, Contradictions, Partial Coverage, Unique Insights, Blind Spots.
Every release or withhold produces a downloadable, hash-bound record with citations to the supplied ground truth.
Inside Fusion Playground, TypeSafe Jev scores each analysis section before synthesis. Agreement score mode compares each section with the other models’ drafts. Evidence mode compares each section with evidence you supply. Only sections scoring 0.90 or higher go into the fused answer. Scores are advisory and don’t trigger the release gate.
Run preset reliability workflows, checking an existing answer or generating and checking a document answer, with a locked audit gate. Choose the fact source and save or load your setup.
Every builder run will produce a record of the setup and stages that ran, with an offline checker that confirms the record hasn't been altered.
The Seam
Pluggable model adapters propose. A non-text filter screens. A deterministic gate decides.
Invariant Rule
The audit stage is mandatory. No model module can release an answer on its own; the deterministic gate is the sole release authority.
Compounding Flywheel
Pillar 1
Scouts each new model, graph technique, or ontology and maps where it plugs in.
Pillar 2
Codex and AI reviewers ship each feature with tests, independent review, and release records (76 Jest suites, 1,655 tests).
Pillar 3
Fits ModelFusion to client sources, leaving behind reusable enterprise adapters (ERP, SharePoint, Confluence).
Pillar 4
Packages verified adapters into the commercial framework.
The four pillars run clockwise: 1 feeds 2, 2 feeds 3, 3 feeds 4, and 4 feeds back into 1.
Integrations & Ecosystem
Running in real Telegram bot workflows today, integrated with Hermes Agent and OpenClaw for first-tier testing.
Pluggable OpenRouter adapter today. First design-partner connector partnership (SharePoint or Confluence candidate) on roadmap.
Private vLLM and Ollama inference, plus VPC and air-gapped deployments, are on the roadmap. Hosted on Vercel today.
Founder
Founder & AI Product Architect
Building ModelFusion™ to make multi-model AI verification the default for high-stakes, production workflows.
FAQ
Questions? getaccess@modelfusion.ai