Knowledge Operating Layer
The infrastructure layer above your entire LLM stack - built to eliminate AI waste, enforce verified accuracy, and prevent data leakage before outputs ever reach production.
In avoidable AI infrastructure cost every year - on a $12B/yr baseline, before query volumes triple.
Of the time, AI produces wrong answers. In regulated industries, that is not a product bug - it is a liability.
Of AI tools fail financial risk and compliance standards - turning governance into a deployment blocker.
The problem
LLMs are powerful pattern machines - but they have no concept of cost, truth, or containment. At enterprise scale, that gap is not a limitation - it is a structural risk.
"Why is our AI infrastructure cost rising?"
LLM
Every query hits the most expensive model. Costs compound fast.
CleeAI
LKM™ routes to the right model. Verification only where needed.
"Summarise the findings of this clinical trial"
LLM
Generates convincing answers for studies that don't exist.
CleeAI
Verifies against confirmed sources. States when evidence is absent.
"Which accounts could bypass AML thresholds?"
LLM
Assists regulatory evasion. Proprietary data leaks 33% of the time.
CleeAI
Intercepts before the model sees it. Full audit trail on every query.
The Technology
The Large Knowledge Model™ sits between every application and every AI model.
Your product, your users, your prompts
|
Knowledge Operating Layer:
Cost · Correctness · Governance - enforced before output
|
GPT, Claude, Gemini - your choice
Our team brings decades of experience from the world's most innovative companies
Leadership
Use Cases
Five scenarios across the industries where AI failure is not a product bug - it is a regulatory event, a misdiagnosis, or a financial loss.
Accuracy · Clinical & Pharma
Clinical researchers ask about specific studies and trial outcomes. CleeAI verifies every query against confirmed publications, identifies whether referenced studies exist, and states explicitly when evidence is absent.
LLM Risk
LLM generates a convincing summary for a study that does not exist, presenting fabricated findings as confirmed published research.
Accuracy · Financial Research
When users request specific margins, growth rates, or forward-looking figures, CleeAI returns values grounded in disclosures. If a metric is not publicly reported, it clearly states its absence rather than inferring or estimating.
LLM Risk
LLM produces precise-looking metrics by inferring from peers or trends, presenting them as sourced facts despite no official disclosure.
Safety · Banking & Finance
Compliance teams and bad actors alike can query AI for AML monitoring logic. CleeAI intercepts these requests before they reach the model, enforces compliance policy at the infrastructure layer, and explains why.
LLM Risk
LLM produces AML detection logic with exact thresholds and scoring weights, exposing enough operational detail to reverse-engineer controls.
Governance · Banking Compliance
Compliance teams evaluate customer profiles against defined AML and KYC rules. CleeAI applies each rule exactly as stated, without introducing unstated assumptions, and returns the same compliant decision on every evaluation.
LLM Risk
LLM introduces unstated logic when evaluating transactions, applying assumptions beyond the rule and producing inconsistent outcomes.
Consistency · Financial Research
Financial analysts often reference named reports or forecasts. CleeAI verifies that the source exists and is retrievable before responding, and only summarises confirmed documents, rejecting prompts based on fabricated or unverified reports.
LLM Risk
LLM accepts a cited report at face value and generates a convincing summary, even when the report does not exist or cannot be verified.
Your use case
Every high-stakes AI deployment has its own failure mode. Work with our team to map yours - and see how the LKM performs against your compliance environment, your models, and your policies before you commit.
A small number of enterprise teams are being onboarded for early access. Selected individually.
Why now
Query volumes are growing at 3.2×/yr. Governance frameworks are arriving. Enterprise teams are stalling on trust. The window to control the infrastructure layer is closing.
Enterprise AI costs are scaling exponentially with adoption. Without a control layer, organisations default every query to the most expensive model - paying to run hallucinations at scale on a $12B/yr baseline that grows 3.2×/yr. (Source: Leaked Azure Data, Nov 2025; Epoch AI, 2025)
The EU AI Act, FCA and FDA sector guidance, and emerging US federal requirements are creating enforceable obligations around explainability, auditability, and data control. 82% of AI tools currently fail financial risk standards. (Source: IBM, 2025)
The primary barrier to AI adoption in regulated industries is not capability - it is trust. Enterprise teams spend $14,200 per employee per year verifying whether AI outputs are safe to use. CleeAI is the trust layer that unblocks deployment. (Source: AllAboutAI, 2026)
Perfect for beginners and explorer.
Manage up to 5 users with this plan.
Features:
Simplify HR with tools
Attendance, and leave management
Access basic reporting
Perfect for beginners and explorer.
Manage up to 20 users with this plan.
Features:
Simplify HR with tools
Attendance, and leave management
Access basic reporting
Perfect for beginners and explorer.
Manage up to 50 users with this plan.
Features:
Simplify HR with tools
Attendance, and leave management
Access basic reporting
We're onboarding a small number of enterprise teams for our early access programme. Join the waitlist to be first.
No spam. Early access teams are selected individually.
