Cemented AI
Cemented AI provides a research platform that grounds AI agent outputs in verifiable source material, ensuring every claim and quote maps to a cited document. The platform supports bring-your-own-agent workflows, including Claude, ChatGPT, or on-prem open-source models, and uses deterministic checks to flag hallucinated quotes. It offers account-scoped data controls, encryption, and enterprise options for provider scoping and self-hosted models.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Teams increasingly rely on AI agents to conduct research, audits, and analyses, but outputs can be incomplete, unsourced, or difficult to verify, forcing humans to manually check every claim. Without a grounded workflow, organizations risk acting on inaccurate or unverifiable AI-generated information.
Solution
Cemented AI provides a research platform that grounds AI agent outputs in solid, verifiable source material. Users can either use Cemented's own agents or integrate existing AI agents, including Claude, ChatGPT, or on-prem open-source models, into the workflow. The platform scopes all documents, conversations, and answers to the account that created them, with project-based sharing only when explicitly granted. Data is encrypted in transit and at rest, and queries and results are tied to the documents users provide, giving every output a checkable foundation. The platform uses deterministic checks so that exact quotes highlighted in green cannot be hallucinated or misattribute their origin, and agents receive feedback on failed assertions to revise reports until checks pass.
Target Audience
Primary customers are organizations and teams that rely on AI agents for research, audits, and analyses and require verifiable outputs, including enterprises with strict security or privacy requirements.
Features
- Bring-your-own-agent support: works with existing AI agents, including Claude, ChatGPT, or on-prem open-source models
- Built-in research agents with a try-for-free option
- Deterministic verification of quotes and claims, with green-highlighted exact quotes that cannot be hallucinated or misattribute their source
- Iterative agent feedback loop where drafts receive deterministic feedback on failed assertions and are revised until checks pass
- Account-scoped data model where every document, conversation, and answer is tied to the creating account, with project-based sharing for controlled collaboration
- Encryption in transit and at rest for all stored data
- Enterprise controls including provider scoping, self-hosted model options, data residency and retention commitments, and reduced staff access
- No training of frontier models on customer content; LLM providers are contractually prohibited from doing so under standard API terms