The verification layer for regulated industries. Up to five frontier models cross-evaluate every response. One quality score. Full audit trail. EU AI Act ready.
In regulated industries, an unverified AI output is not just wrong — it is a compliance violation.
Single models hallucinate without warning. No second opinion. No cross-check. No safety net for high-stakes decisions.
You cannot measure if the answer is trustworthy. No confidence metric. No way to flag low-quality outputs before they reach production.
Regulators require traceability. EU AI Act Articles 12, 14, and 15 mandate logging, human oversight, and transparency for high-risk systems.
Fines up to €35M or 7% of global revenue under the EU AI Act. Non-compliance is not a technical problem — it is a business-ending one.
GPT-5.4, Claude Opus 4.6, Gemini 3.1 Pro, Mistral Large 2, and Llama 3.3 70B cross-evaluate each response with 5 specialist roles. Discrepancies are flagged. Consensus is measured. Every step is logged.
Every response comes with a measurable confidence score. Set thresholds. Flag low-confidence outputs automatically. Prove quality to auditors.
Timestamp, models used, individual model contributions, discrepancies logged. Every decision is traceable and reproducible.
Drop-in REST API. OpenAI-compatible. Integration in hours, not months. No infrastructure to manage. No model orchestration to build.
Where a single AI error carries real consequences, consensus verification is not optional.
Contract review, legal research, due diligence, regulatory analysis. Multi-model consensus catches errors that single models miss in complex legal reasoning.
One error = malpractice liabilityAML/KYC analysis, credit scoring, fraud detection, risk assessment. Verified AI outputs that satisfy regulatory scrutiny and internal compliance teams.
One error = regulatory fineClinical decision support, triage, drug interaction analysis, medical summarization. Multi-model consensus adds a verification layer before critical outputs.
One error = patient harmfrom openai import OpenAI client = OpenAI( base_url="https://llmconsensus.io/v1", api_key="orch_your_api_key" ) # One call. Up to five models. Verified output. response = client.chat.completions.create( model="consensus-deep", messages=[{ "role": "user", "content": "Analyze this contract clause for regulatory compliance risks..." }] ) # Quality score + full audit trail included print(response.choices[0].message.content)
If your team already uses the OpenAI SDK, integration takes minutes. Change the base URL. Change the model name. That is it. Full consensus verification with no code refactoring.
Built from the ground up for industries where trust is not negotiable.
US 19/215,933 & EU EP25176020.3. Novel multi-model consensus technology with protected IP.
GPT-5.4 (OpenAI), Claude Opus 4.6 (Anthropic), Gemini 3.1 Pro (Google), Mistral Large 2 (Mistral AI), Llama 3.3 70B (Meta/Together AI). Five frontier models in deep mode.
Designed for compliance with Articles 12 (record-keeping), 14 (human oversight), and 15 (accuracy) of the EU AI Act.
No storage of your prompts or responses beyond processing. We never train on your data. Your intellectual property stays yours.
Full GDPR compliance. Data processing agreements available. EU-hosted data processing on request.
Data Processing Agreements available on request for enterprise customers. Contact privacy@llmconsensus.io.
100 expert-domain questions. 3-judge multi-vendor blind evaluation. 100% non-inferiority, 44.9% win rate, 0% loss rate vs. the best individual frontier model. View benchmark →
One-time credit packs. No subscriptions. No hidden fees. Credits never expire.
Start a proof of value in 72 hours. No commitment. 5 free credits included.
Questions? hello@llmconsensus.io