--- title: "Enterprise AI discovery and architecture | YPAI" url: https://ypai.ai/ai-implementation/discovery-and-architecture/ description: "Turn an AI opportunity, workflow or failing prototype into a buildable system design, evaluation plan and implementation roadmap." source: "src/copy/routes (route /ai-implementation/discovery-and-architecture/)" --- # Define the system before you commit to the build. > Turn an AI opportunity, workflow or failing prototype into a buildable system design, evaluation plan and implementation roadmap. - YPAI turns an operational workflow, AI opportunity or failing prototype into a buildable system design. - We map the work, data, systems, permissions, failure conditions and economics. Then we define the architecture, evaluation plan, operating controls and production path your teams can act on. ## One engagement, from your workflow to a build decision. Show us where the work begins, which systems and data it touches, who makes the decision, what outcome matters and what cannot fail. - YPAI studies the workflow as it operates today. We identify where deterministic software is sufficient, where AI adds measurable value, where human judgement remains necessary and where the operational risk outweighs the expected return. - Working sessions resolve open decisions and validate findings. - YPAI will scope the discovery required to reach a build decision. - Discovery does not require a downstream build commitment. ## Start from the point you are actually at. ### A defined opportunity - You know where AI may create value, but the workflow, architecture and production requirements are still unresolved. ### A manual workflow - The process is slow, inconsistent or difficult to scale, and you need to determine which steps should be assisted, automated or left unchanged. ### A failing prototype - The demonstration works under controlled conditions but breaks against real data, integrations, permissions, latency, cost or user behaviour. ### A broader implementation portfolio - You have multiple possible use cases and need to determine what should be built first, what depends on shared infrastructure and what should not be built. Every one of those starting points ends in the same place. A system design your team can price, build and test. Discovery delivers a defined system with an intended purpose, an operating boundary and a measurable path to production. ## Discovery ends with decisions. - This is not a sequence of open-ended workshops. - Each engagement produces a decision package that product, engineering, security, legal, finance and operations teams can use to approve, build, test or reject an implementation. - The scope can cover one workflow, one existing system or a tightly defined portfolio of related use cases. ## What will we leave with? A discovery engagement ends with concrete implementation decisions: the workflow definition, architecture, evaluation plan, controls, economics and implementation path. The architecture record becomes the control document for the build. ## The engagement ends with one of five decisions. ### Build - The requirement, architecture, data, controls and economics support implementation. ### Pilot - A limited production-representative test is needed to validate the highest-risk assumptions. ### Fix the foundations first - Data quality, permissions, integrations, process ownership or evaluation material must be resolved before an AI build is justified. ### Use an existing product - A configured commercial system meets the requirement more effectively than a custom implementation. ### Stop - The expected value does not justify the cost, risk or operating burden. Discovery can end in any of these five outcomes, including stop. ## The model is one component. How will the system actually work? - A model can generate text, classify an input, extract information or propose a next step. - A production system must also control identity, context, permissions, actions, exceptions, evidence and failure. ## How will we know it works before production? Acceptance is designed before the prototype: the test set, the metrics, the thresholds and the release gate are defined before implementation begins. Not every metric belongs in every system. ## Continue with YPAI or use the architecture independently. YPAI can continue from the approved architecture into: - The architecture record can also be handed to your internal team or another implementation partner. ## Frequently asked questions ### Is this a general AI strategy engagement? The work is tied to a real workflow, system or tightly defined use-case portfolio. The output is an implementation decision and a buildable architecture. ### Can YPAI assess an existing prototype? Yes. YPAI can examine the prototype’s workflow fit, prompts, retrieval, integrations, permissions, evaluation results, logs, latency, cost and failure modes, then define what is required for production. ### Does discovery include a prototype? A focused technical spike can be included when a specific architecture decision cannot be resolved through inspection and analysis. A production-representative pilot is scoped separately against agreed acceptance criteria. ### Can our internal team build from the output? Yes. The workflow specification, architecture record, evaluation plan, control model and implementation backlog are designed to support handoff.