80.3%
of AI projects fail to deliver their intended business value - about twice the failure rate of comparable IT projects that don't involve AI.
RAND Corporation, 2025
(analysis of 2,400+ enterprise AI initiatives.)
The evidence gap
The failures aren’t technical – They’re unproven
95%
of generative AI pilots produce no measurable P&L return.
MIT NANDA Initiative, 2025.
88%
of AI pilots never reach production.
CIO Research, 2025.
50%
of generative AI projects are abandoned after proof of concept — exceeding Gartner's 30% prediction.
Gartner, 2025.
80%+
Every heavily regulated sector exceeds an 80% failure rate.
Pertama Partners synthesis of 2,400+ initiatives, 2026.
The mechanism
Assurance runs on two planes
Controls must be enforced on every request in production – and every release must be proven ready before it gets there. Tin.AI covers both: a live inference plane and an offline assurance plane, joined by a promote-and-re-evaluate loop.
Inference plane
The live layer that watches and controls the AI while it's actually running, applying guardrails and monitoring to every single request in production.
Quality & Drift
Production monitoring
Guardrails
Block unsafe & flag
Observability
Continuous re-eval loop
Gateway
Tool limits · HITL
↓Re-Eval
↑Promote
Assurance plane
The offline layer that puts the AI through dataset curation, evaluations and regression tests before it ever goes live, so nothing ships without proof it works.
Dataset
Golden dataset · LLM
Evals
Safety, reliability, etc.
Regression
Re-test on each release
Evidence
Observability · Report
Data governance & infrastructure security
The security groundwork beneath both planes, ensuring the data and systems the AI runs on are properly controlled and protected before any AI is even assessed.
Data
The data itself is kept classified, protected, access-controlled and traceable end to end.
Data
Classification
PII
Access control
Lineage
Infra
The systems running the AI are encrypted, identity-controlled, and secured down to API access and secrets.
Infra
Encryption
API security
Identity / RBAC
Secrets
How Tin.AI runs inside your environment
Tin.AI deploys inside your environment, on-premises, in an air-gapped cloud, or the cloud of your choice, single-tenant, on Kubernetes. No data leaves your environment, ever.
01 - Configure once
Your model-risk team defines the scenarios: the evaluations, metrics and thresholds every AI application must pass. Datasets stay in your control, versioned and immutable once published.
02 - Run on every change
Applications are evaluated automatically — on deployment, on schedule, and on demand. Every result writes to an audit trail your compliance function owns.
03 - Prove when asked
Evidence packs generate in minutes, with reproducibility guaranteed by immutable versioning. Waiver workflows handle the exceptions that real deployments always produce.