OpenCorum runs your critical problem through 5+ independent AI models in parallel and delivers one verified, consensus-backed recommendation — with full audit trail.
Built for healthcare diagnostics, legal analysis, financial risk assessment, compliance review, and any scenario where a single AI's answer isn't enough. Open-source core. Enterprise-grade reliability.
When stakes are high — healthcare, legal, finance, compliance — relying on a single AI model is like getting a second opinion from the same doctor. Here's what can go wrong.
Even the best models hallucinate 3-7% of the time on complex tasks. In critical domains, that's an unacceptable risk. A single model has no built-in fact-checking mechanism.
When an AI makes a wrong decision, can you trace why? Most deployments lack comprehensive logging of the decision chain, making compliance and debugging nearly impossible.
Running a single premium model for every query adds up fast. Without intelligent routing and consensus optimization, AI costs can spiral out of control as usage grows.
Building your entire workflow around one provider's API means you're vulnerable to price changes, rate limits, service outages, and policy shifts outside your control.
Based on 10,000 complex queries per month. OpenCorum uses intelligent model routing to reduce costs while improving accuracy through consensus.
We don't replace AI models — we orchestrate them. OpenCorum runs your query through multiple independent models, compares their reasoning, and delivers a consensus-backed answer with full transparency.
Run your query through 5+ independent AI models simultaneously. Each model provides its answer and reasoning. Our consensus engine identifies agreement, flags disagreements, and surfaces the most reliable conclusion.
Every decision is logged with full lineage: which models were queried, what each responded, how consensus was reached, and what confidence level was achieved. Export for compliance, debugging, or quality review.
Not every query needs GPT-4. OpenCorum routes simple queries to cheaper models and reserves premium models for complex tasks. Consensus ensures quality while keeping costs predictable and controlled.
OpenCorum works with any model via OpenRouter API. Switch providers instantly, mix and match models, and never be held hostage by price changes or service outages from a single vendor.
OpenCorum's consensus engine processes every query through four distinct layers, each adding verification, validation, and confidence scoring.
Your query is simultaneously sent to 5+ independent AI models via OpenRouter API. Each model receives identical context and instructions. Parallel execution ensures sub-second latency regardless of model count. Models are automatically selected based on task type, cost constraints, and performance history.
Raw model outputs are parsed, structured, and normalized into a common format. Key claims, reasoning chains, and confidence indicators are extracted. This layer handles different output formats, token limits, and response structures across all supported models.
Our proprietary consensus algorithm compares all responses across four dimensions: factual agreement, reasoning alignment, confidence correlation, and contradiction detection. Disagreements are flagged with specific claim-level granularity. Confidence scores are calculated using weighted model reliability history.
Final consensus answer is synthesized from agreeing responses. Disagreements are presented with supporting evidence from each model. Complete audit trail is generated including all raw responses, consensus calculations, and confidence metrics. Output is ready for production use or compliance review.
Our consensus algorithm doesn't just count votes — it analyzes reasoning, detects contradictions, and weights responses based on historical accuracy.
Each claim in every response is extracted and compared. Models receive agreement points when their factual claims match other models. Contradictory claims are flagged with specific text spans for review. Final score: 0-100% factual consensus.
Beyond final answers, we compare how models reached their conclusions. Similar reasoning chains increase confidence even if wording differs. Divergent reasoning with same conclusion triggers deeper analysis. This catches lucky guesses vs. sound logic.
Not all models are equal for all tasks. OpenCorum tracks historical accuracy per model per task type. A model with 95% accuracy on legal queries gets higher weight than one with 70% accuracy. Weights are continuously updated based on consensus outcomes.
When models fundamentally disagree, OpenCorum doesn't hide it. Contradictions are surfaced with evidence from each side. Low-confidence decisions can trigger automatic escalation to human review or additional model queries. Transparency over false certainty.
Based on production deployments in healthcare, legal, finance, and enterprise operations. All metrics verified through third-party audit.
Our development roadmap is transparent and community-driven. We ship new features every quarter based on user feedback and enterprise needs.
We've worked on AI systems in healthcare, finance, and legal tech. We know what happens when AI gets it wrong. That's why we built OpenCorum.
Founder & CEO
Former AI Lead at major healthcare tech company. 12+ years building mission-critical systems. Saw firsthand the cost of AI errors in medical diagnostics. PhD in Computer Science, Stanford.
CTO
Ex-Google AI Infrastructure. Built distributed systems at scale serving billions of requests. Expert in consensus algorithms and fault-tolerant architecture. MS in Distributed Systems, MIT.
Join 500+ developers and enterprises using OpenCorum to make critical decisions with confidence. Start free, scale when you're ready.