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A useful generative-AI path needs approved sources, integration and a test method before it can be released. If you own that work, you need to decide which workflow to build first, what the system may use, and what evidence a release decision requires.
YPAI builds and integrates the generative-AI workflow around that decision. Start with one useful path where the sources, systems and owner are already known.
The core deliverable is a generative-AI workflow you can evaluate: the integration, the sources it may use, and the acceptance checks before release.
That workflow may use retrieval, an assistant, or an agent as the means for the named job. Those means stay inside this engagement; they do not replace the specialist routes for chatbots, knowledge assistants or multi-step automation.
Source grounding, integration and evaluation readiness come before a production recommendation. Answers, actions and failures are tested together.
The result is a tested generative path rather than an unreviewed prompt demo.
Choose one workflow with a clear next step and a person who already handles the exceptions. If it cannot finish, it passes the attempt, the sources used and the failed check to that owner.
That design links approved sources, the generative step, system integration and the evaluation set used for release.
You choose the workflow. The engineering or operations counterpart turns it into explicit steps across the sources, model access and systems already in use.
For a connected build, the technical work is concrete: source grounding, model or prompt configuration, integration, logs and evaluation. The team can name each allowed read, what the workflow may generate, what happens after a failed call and what belongs in the release record.
Does the output stay inside the approved sources and actions?
Does a failure reach the named owner with enough context?
Can the team inspect the source, prompt and result used?
Use representative tasks, source checks and failure cases to verify those three things. Run those checks again after a change to a source, model, prompt or integration. The results show what passed, what failed and what needs correction before the next release decision.
The workflow may use only the sources, identities and tools you name. Grounding and permissions are designed before production, so a generated step cannot invent an unapproved source or write.
Representative and failure cases are checked before anyone treats the workflow as ready. A failed generation, missing source or unsafe output reaches the named owner with the attempt and the next allowed move.
First agree the workflow, sources, allowed generation, integration and test method. Depending on the uncertainty, a scoped pilot may be useful. Pilot scope and commercial terms are agreed before it begins.
The pilot can show the tested tasks, source boundaries, failed generations and acceptance results. The relevant owners can also inspect data handling, logs and named operating ownership.
Production is a separate decision after the team reviews those results. If it proceeds, managed improvement can cover source updates, monitoring and regression tests for the accepted workflow.
Tell us the workflow, the sources, the systems and when a person should take over.
The intake records that context, and YPAI reviews it before proposing project scope and commercial terms.