Discover
Quantify volume, handling time, response gaps, owners, systems and risk. Decide whether the workflow is worth pursuing.
Delivery model
Scope the risk before you scale the system.
A credible enterprise AI deployment is a series of decisions, not a model demo. NeuBlink makes the decisions visible and testable.
Map
Write the workflow as states, tools, permissions, sources, escalations and acceptance tests before building.
Pilot
Implement one narrow path with synthetic or customer-approved data. Keep humans on consequential actions.
Validate
Run an evaluation set, capture failure modes, test fallbacks and confirm the operator can inspect what happened.
Deploy
Promote the accepted artifact into the customer environment with secrets, access control, observability and rollback notes.
Improve
Measure agreed operating KPIs, review exceptions and expand only when the first workflow is stable.
What gets checked
A deployment is only credible when the boring edges are handled.
Evidence
Approved sources, document owners, version and retrieval tests.
Boundaries
Allowed tools, human handoff, data categories and explicit exclusions.
Evaluation
A test set, failure taxonomy, fallback behavior and acceptance threshold.
Operations
Access control, secrets, logs, retention, cost caps and rollback.
Release
An owner, deployment record, environment matrix and support path.
Start with a decision