Three models check every high-stakes decision before it reaches you. A multi-provider executive team with persistent memory, governance zones, and audit trails built for regulated industries.
Every strategic decision runs across Claude, GPT, and Gemini in parallel. Cross-provider independence breaks correlated failures that single-model systems can't detect.
Consensus engine applying NASA Triple Modular Redundancy principles. Pairwise comparison converts parallel compute into trustworthy decisions with audit trails.
File-based knowledge graph that persists across sessions. Your AI team remembers every decision, every correction, every preference. Context compounds, never resets.
No capability ships until a passing end-to-end test proves it works. Quality gates prevent half-baked output from reaching your business. Tested, not hoped.
Every capability above has a passing end-to-end test on record. We don't sell what we can't prove.
Discuss Your Project View PricingThose are single-provider tools. CarbonHelm runs Claude, GPT, and Gemini in structured disagreement on every high-stakes decision. Single-model architectures have correlated failure modes -- if the model hallucinates, there's nothing to catch it. Multi-provider consensus breaks that pattern. Research shows code generation hits up to 99% hallucination on package references. We exist because "close enough" isn't close enough in regulated industries.
Three-tier routing: Premium tier (Claude Opus, GPT-4o) for C-suite strategic decisions, Cloud tier (Sonnet, Gemini Pro) for manager coordination, and On-Prem tier (Ollama/Qwen) for high-volume worker tasks. Costs stay proportional to decision stakes. A routine categorization task doesn't need the same compute as a loan adjudication.
Governance zones enforce hard gates on financial, legal, and clinical decisions. Every multi-provider consensus decision produces a timestamped audit record showing which models agreed, which disagreed, and why. Enterprise customers deploy on-premise with air-gapped infrastructure. We're building toward peer-reviewable clinical validation -- not just marketing claims.
That's the point. Disagreement is signal, not failure. The Boardroom Orchestrator applies NASA Triple Modular Redundancy principles -- pairwise comparison surfaces where models diverge and why. When Claude and GPT agree but Gemini disagrees, you get a structured analysis of the disagreement. The worst bugs in AI are the ones where all models are confidently wrong in the same way. Cross-provider diversity breaks that.
Peer-reviewed research (Snell et al., 2024) shows compute-optimal test-time scaling achieves equivalent quality at 4x less compute. CarbonHelm's architecture implements this directly: routine decisions use small, efficient models. Only high-stakes decisions trigger full multi-provider consensus. Documentable per-decision energy accounting, compatible with green datacenter initiatives.
Precision-priced: usage times inference depth. Single-pass (low stakes) at baseline rate. 3-provider consensus (customer-facing) at 2.5x. 7-agent debate (regulated) at 5x. Full board session (strategic, irreversible) at 10x. You buy precision by the decision, not by the seat. A finance team paying 5x for loan adjudication is buying an audit trail single-model competitors can't produce.
CarbonHelm is in private beta for regulated-industry teams. Three models agree before anything reaches your clients.