The Science Behind BaseSSM
Every AI assistant gives you one model's opinion. BaseSSM gives you the statistical consensus of 170+. We adapted the same verification methodology that has saved lives in clinical research — and applied it to AI.
1 model. 1 opinion.
Ask ChatGPT or Claude a question and you get a single model's best guess. Research shows 15–30% of responses contain hallucinated information. You have no way to know which answers to trust.
170+ models. Verified.
Every answer is cross-verified across multiple AI models. Statistical consensus is measured. Disagreements are flagged. You get a confidence score (0–100%) with every response.
Five steps. Sub-second execution. Mathematically verified.
Type your question naturally, speak it using voice, or upload documents for deep analysis. BaseSSM understands intent across text, voice, and files.
The Supervisor Agent decomposes your query, identifies the domains of expertise required, and routes sub-tasks to specialized agents — all in milliseconds.
Your question is dispatched to multiple foundation models simultaneously. Each model processes independently — no model sees another's output. This ensures true independent verification.
Our proprietary consensus engine applies advanced statistical methods — adapted from decades of peer-reviewed clinical meta-analysis research — to measure agreement, weight reliability, and detect conflicts across all model responses.
You receive your answer with full transparency: a confidence score, response latency, agent attribution, and — when models disagree — an explicit uncertainty flag so you know exactly what to trust.
Proprietary Technology
At the heart of BaseSSM lies a proprietary statistical consensus engine. Built on methodologies proven across thousands of peer-reviewed clinical studies, adapted and extended for multi-model AI verification.
Each model's response is weighted by its measured reliability. More precise models have greater influence on the final answer. Not all models are equal — our engine knows which to trust more.
When models genuinely disagree, our engine detects it — distinguishing real conflicts from random noise. Significant disagreements are surfaced transparently rather than hidden.
The final confidence score (0–100%) is mathematically derived from the strength of consensus. High agreement across diverse models = high confidence. It's not a guess — it's statistics.
Our consensus methodology is not invented from scratch — it's adapted from statistical techniques with decades of rigorous academic validation.
Clinical Meta-Analysis Standard
The gold standard for combining effect estimates from multiple independent studies. Used in thousands of medical research papers to synthesize evidence — because when lives are on the line, you need mathematical rigor, not guesswork.
We adapted this methodology for AI: each model response is treated as an independent "study," weighted by its precision, and combined into a unified estimate with a mathematically derived confidence interval.
Disagreement Quantification
Not all agreement is meaningful, and not all disagreement is problematic. Our engine uses advanced heterogeneity testing to distinguish genuine conflicts from random variation — a critical distinction that single-model AI systems simply cannot make.
When true disagreement is detected, BaseSSM doesn't hide it. It surfaces the conflict explicitly, adjusts the confidence score downward, and lets you — the human — make an informed decision about how to proceed.
170+
AI Models
Orchestrated
0–100%
Confidence
On Every Answer
<2s
Avg. Latency
For Consensus
4
Domain Agents
Specialized
Full transparency. No black boxes.
A 0–100% score showing how strongly models agree. Based on statistical consensus, not heuristics.
Exact time taken to query, process, and verify across models. Typically under 2 seconds.
Which domain agents handled your query and how tasks were decomposed and routed.
When models significantly disagree, you see it. Uncertainty is made explicit, not hidden.
Which foundation models contributed to the answer. Full visibility into the consensus process.
How the system arrived at its answer. The thinking process is exposed, not a black box.
See multi-model consensus in action. Ask a question and watch 170+ models converge on a verified answer — with a confidence score you can trust.
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