Generative AI adoption is accelerating. But results on the ground aren't keeping up. The root cause is the absence of a trusted data foundation. Lemma's trust infrastructure for Verifiable AI fills that gap.
Generative AI is deployed but doesn't deliver results. The top challenge is concentrated on 'data.'
| Challenge Category | Content | Response Rate |
|---|---|---|
| Confidentiality & Privacy | Concerns about handling confidential and personal information | 55% |
| System Integration | Complexity of integrating with existing systems | 51% |
| Data Quality | Not getting expected responses (data quality issues) | 46% |
| Accountability | Unclear output basis and inference process | 40% |
Sensor values, business logs, and contract records are exposed to loss and tampering risks as they pass through multiple points. Feeding them directly to AI induces hallucinations and distorts business reasoning.
Handing over all data necessary for business automation to external parties is not permitted under personal information protection laws and confidentiality management. The contradiction of 'wanting to prove but not show contents' blocks AI utilization.
If agent AI executes autonomously, humans must be able to verify and explain 'why that decision was made.' Traceability of processing grounds becomes a prerequisite for AI adoption.
A 'data refinery infrastructure' that collects, verifies, and delivers real-world data to AI in a trusted form. Three functions—Normalize, Commit, Prove—provide a foundation where AI can safely execute business operations.
Extract only attributes from encrypted documents via ZKP. AI agents can perform conditional reasoning, search, contracts, and payments without touching raw data.
Identify issuers via DID and permanently record provenance information on-chain. Maintain a state where both AI and humans can audit and re-verify at any time.
Prove only the 'fact that conditions are met' via ZKP without disclosing any confidential information. Can be safely presented to trading partners, audit bodies, and government agencies.
Six critical operations — transformed from manual to cryptographically verified.
If even one applies, Lemma is effective. Click to confirm.
Have operations that proceed based on external fact verification for approval, payment, or next process
Spending manpower, time, and costs on that verification work
Considering AI adoption but concerned about internal data quality and confidentiality management
Need to prove traceability across supply chains
Required to prove 'who did what when' for audit and compliance
Reluctant to disclose confidential information when proving to trading partners or government
From ZKP, DID, provenance management technical specifications to PoC design steps that can start in as little as a few weeks. We've compiled 'next actions' for those considering adoption.
ZKP, DID, provenance management technical specifications and implementation approach
Application scenarios for manufacturing, supply chain, and IP management
PoC design, evaluation metrics, and shortest verification steps
Adoption decision checklist and recommended actions