Enterprise procurement
CrowdSmart preserves the evidence, reasoning, confidence, disagreement, and deliberation history behind every outcome. That is what gets a decision through a board, a regulator, or an auditor — and what separates a trust layer from another chat window.
The trust model
Human expertise, approved AI agents, and source materials enter with clear role, context, and permissions.
Reasoning is evaluated independently of title or hierarchy. Comparative judgment surfaces what truly matters.
We measure confidence, variance, alignment, and unresolved concerns instead of forcing false consensus.
Every conclusion stays connected to who contributed, what evidence mattered, how judgment changed, and why the action was authorized.
A probability-based decision with a Bayesian confidence score · a ranked reasoning model of every factor that drove it · a full audit trail of who contributed what, when, and why · a persistent Knowledge Model that compounds into Collective IQ.
Provenance
Every Knowledge Agent answer and every Collective IQ concept lists the sources it used. Source type, author, title, and timestamp travel with the citation, so an official policy is never confused with a Slack message.
The specific excerpt, chunk identifier, and similarity score are retained. An operator can reconstruct exactly what the model saw at the moment of generation.
A download link returns the original PDF or DOCX. The reader never has to trust the summary, or even the extract.
For Collective Reasoning, the chain runs participant → proposition → review → ranking → result. Agent citations still resolve to source documents. The interaction graph itself is part of the record.
Run the same question in foundation-assisted mode and in referenced-retrieval mode. The difference between the two answers is exactly the model's unsupported inference — exposed, not hidden.
Identity masking
During deliberation, identities are masked. Status hierarchies are removed from the evaluation. Adaptive item selection and blind peer ranking make the system resistant to manipulation and gaming.
Reviewers evaluate propositions without knowing who wrote them. Title, seniority, and politics are stripped from the signal.
Participants contribute their own positions before seeing others — preserving informational diversity and reducing conformity.
Analytics measure which ideas shaped the conversation by how they connect — not by who submitted them.
Privacy posture
Private corpora, session content, and Collective IQ models are scoped to your tenancy. They are not used to train foundation models for other customers. Publishing outside your organization is always an explicit, reviewed act.
Organizations need to oversee when the outside world is subtly influencing their knowledge. Strict-mode Knowledge Agents refuse to invent answers from foundation-model priors when the corpus is silent — giving you a clean boundary between your evidence and everyone else's.
Private (individual wiki) → Team / organization (Collective IQ default) → Community (selected packs) → Public (curated export only). Each step up is intentional.
Governance, audit, private models, API / MCP access, and multi-client deployment are available for enterprise and partner deployments. Talk to us →
Strict-mode abstention
In a regulated or high-trust deployment, statistical noise is risk. Knowledge Agents in referenced-retrieval mode abstain rather than invent a plausible sentence — giving you the chance to curate the base of trusted sources instead of papering over a gap.
Answers only from retrieved passages. Declines when the corpus cannot support a claim. The production default for trusted bots.
Controlled synthesis grounded in retrieved evidence, with the option to compare against a strict-mode run of the same question.
Audit trail
Regulatory pressure — EU AI Act, SEC AI disclosures, board-level accountability — is forcing organizations to answer: "How did you decide this?" Most cannot. CrowdSmart can.
Full provenance for every output: contributor identity (masked during deliberation, recoverable for audit), timestamp, and the reasoning that carried weight.
Reconstruct the input, the configuration, the retrieved evidence, the confidence, and the deliberation state at the moment of the decision.
Agents augment the group; they never replace accountable human judgment. Override and authorization remain with people at every step.
Intellectual property
The learning engine is protected by foundational IP developed over more than a decade of applied research. Four issued patents cover the core.
Diagnosing sources of noise in decision-making processes. Analysis and measurement of errors in human judgment — a critical component for improving decision accuracy.
Converting asynchronous human interaction into persistent live knowledge models that can be queried for deep understanding.
Free energy is a probability measure of alignment with a decision. High free energy may create passive-aggressive behavior concerning a decision — and the system measures it.
Uses dimensionality reduction and geometric analysis to optimize efficiency and accuracy of learning relevance and semantic coverage of statements used in a collective reasoning process.
A patented multi-agent AI architecture that synthesizes the reasoning of human and AI agents. An AI Facilitator manages a Partially Observable Markov Decision Process using a pre-trained Transformer to manage the semantics of the collaboration; the system learns the resonance modes of the group, builds a Bayesian causal model by minimizing collective free energy, and produces a generative language model of the collaboration itself.
Or get in touch and see the audit trail on a session of your own.