CleeAI is an enterprise-grade AI infrastructure layer positioned as a "Knowledge Operating Layer." It does not produce its own large language models (LLMs), but instead sits above the entire LLM technology stack, between applications and the underlying models. Its core component is called LKM™ (Large Knowledge Model™), with the goal of reducing AI waste, enforcing accuracy verification, and preventing data leakage before outputs reach the production environment.
Main capabilities:
- Cost control: Through intelligent routing, each query is sent to the appropriate model rather than defaulting to the most expensive model; verification is performed only when needed.
- Accuracy verification: For query content, verify whether the cited sources or data actually exist. For example, in clinical research, it can identify whether cited studies exist and clearly state when evidence is missing; in financial research, it returns data based on disclosures, and if a metric is not publicly disclosed, it truthfully states that it is missing rather than inferring or estimating.
- Security and governance: Intercept requests that may violate compliance policies (such as assisting in evading AML thresholds), process them before the model sees the request, and retain a complete audit trail. At the same time, when performing AML and KYC rule evaluations, strictly apply established rules to avoid introducing undeclared assumptions and ensure consistent results for each evaluation.
- Consistency assurance: Verify whether cited analyst reports or forecasts exist and are retrievable, summarize only confirmed documents, and reject prompts based on fabricated or unverified reports.
How to use: CleeAI is deployed as an infrastructure layer on top of the existing LLM technology stack, and users can continue to use models such as GPT, Claude, and Gemini. Enterprise teams can apply for access through the early access program, with the CleeAI team assisting in mapping their own failure modes and testing LKM performance against compliance environments, the models used, and policies before making a commitment. Early access teams are individually screened by the official team.
Applicable scenarios: The materials list five high-risk scenarios, mainly involving healthcare and pharmaceuticals (preventing hallucinated medical evidence), financial research (preventing fabricated financial metrics and intercepting nonexistent analyst reports), and banking and finance (blocking regulatory evasion requests and eliminating AML/KYC rule drift). The common thread among these scenarios is that AI errors may lead to regulatory incidents, misdiagnosis, or financial loss.
Limitations: The materials do not provide specific details about the technical architecture, supported model list, integration methods, data storage, and privacy policy. Some pricing information ($199/month, $399/month, and packages for up to 5 users, 20 users, and 50 users) appears in the official website text, but the complete billing model, feature differences, and specific conditions for early access are not yet clear. In addition, some data sources cited in the materials (such as Leaked Azure Data, AllAboutAI, etc.) and future years (2025, 2026) indicate that they may contain predictions or non-public information, and actual results need to be independently verified.
