AI Project Delivery Roadmap: From Scope to Production

Start with one useful workflow, establish the data and access boundary, then build the smallest complete implementation you can evaluate. Release it to a bounded group only after the team can explain its failures, operate a fallback and take ownership. Revisit earlier decisions when the evidence changes.

This roadmap is for founders, CTOs and engineers delivering an AI feature. It gives each stage an output, a reviewer and a reason to pause. The sequence is a planning recommendation, not a certification or a fixed delivery schedule.

Our running example is an internal policy assistant that drafts a cited answer for a support reviewer. It reads one approved policy collection and cannot send a customer message or modify a record. This is an illustrative workflow, not a client result. The linked code examples use synthetic fixtures or explicitly hypothetical cost inputs.

By Rohit Raj. If you are still choosing an engineer, use the scope and acceptance guide alongside this delivery map.

Write the trigger, approved input, expected output and person who decides whether it is useful. For the policy assistant: a reviewer asks a question, the system retrieves permitted policies, and the reviewer receives a draft with source references or a visible handoff.

Record the current process before adding AI. Measure the entire task, including finding a policy, checking it and correcting a reply. A faster model response is not a time saving if review becomes slower. Keep the baseline and assisted workflow comparable.

Produce: a one-page brief with a business owner, technical owner, representative questions, exclusions and acceptance criteria. The AI delivery brief provides a starting format. Write the costly failure in plain language: for example, exposing another team's restricted policy.

Move forward when: the reviewer can distinguish an acceptable draft from a misleading one and the data owner can provide approved examples. If nobody can judge the result, resolve that before selecting a model.

For the running example, the first scope decision is “draft for review.” Sending a reply is a later change with separate permissions and acceptance tests. Keep that exclusion visible in the plan and product interface.

List every source the feature needs, its owner, update mechanism and access rules. Include what happens after a document is corrected, deleted or restricted. A convenient exported folder is not yet a plan for maintaining production data.

For our assistant, an inventory could contain:

These are proposed test conditions, not measured results. Enforce identity and authorization in the application and retrieval layer before supplying context. Microsoft's RAG overview identifies granular access control as a core retrieval requirement.

Produce: a source inventory, permission matrix and an explicit freshness expectation. Review with: the data owner and application engineer. Move forward when: the restricted-user and revoked-access paths work through the actual connector. Pause if the demonstration depends on a shared administrator account.

Agree which request data may be retained in logs and for how long. Troubleshooting should not create a second, less protected copy of the source collection.

Compare approaches against the workflow instead of choosing a framework first.

These are design starting points, not universal architecture rules. Anthropic's guidance on effective agents recommends starting simply and adding complexity when warranted.

For the policy assistant, try authorized retrieval → draft → source review. Compare it with the existing search-and-write process. Add reranking, a different model or another retrieval step only when a recorded failure suggests that change.

Produce: a short decision record with the alternatives, rejected options, expected costs and the evidence that would reverse the choice. Estimate both build work and operation using the RAG cost worksheet. Its figures are editable hypothetical inputs, not prices for your engagement.

Move forward when: the technical owner can explain the components and the team has a credible way to test the benefit.

Connect login, source access, retrieval, generation, review and the destination for the result. Put the first useful flow in the application where the user already works. A notebook can test a model, but it cannot establish that the integration works.

For the assistant, show the source title and version beside the draft. Let the reviewer open the permitted evidence and reject an unsupported claim. If the source is unavailable or the question cannot be answered, return an explicit handoff instead of an invented policy.

Define a request identifier and the state transitions before adding retries. If saving a draft times out after the destination accepts it, a repeated request must not silently create another draft. Check the destination's recorded outcome; an assistant saying “saved” is not sufficient evidence.

Produce: one complete path, an inspectable request trace and a demonstrated failure path. Keep secrets and private source text out of broadly accessible logs. Move forward when: an intended user can finish the task and recover from a failed dependency.

Use the existing-app AI integration guide for integration planning. If the work spans users, data and several systems, a forward deployed engineer can own that delivery boundary.

Agree the release criteria before scoring the final implementation. Use development examples for iteration and a held-out set for the release review. Include ordinary questions, ambiguous requests, missing evidence, source updates, restricted users and revoked access.

For RAG, evaluate the chain in separate parts:

• Retrieval: did the required sources reach the context, in a useful order?
• Authorization: was every retrieved item permitted for that identity?
• Answer: does the source support each factual claim, and is the source current?
• Workflow: did the intended draft or handoff reach the receiving system?
• Operation: what were latency, cost, retries and human review effort under the stated workload?

The runnable RAG evaluation harness checks document IDs, ordering, access boundaries and abstention. Its synthetic fixtures demonstrate evaluator behavior; it does not score semantic answer correctness or certify an application. Add human-reviewed answer checks and end-to-end integration cases.

Produce: versioned cases, the tested application/model/prompt versions, per-case outcomes and a failure register. Specify denominators: correct answers out of answerable questions differs from correct outcomes across all questions, including necessary refusals.

Move forward when: the reviewers have examined each agreed criterion and any exceptions. Keep serious access failures visible rather than blending them into an average. Repeated runs can reveal variability, but no small finite test proves that failures are impossible.

Choose the initial users, permitted actions and conditions for pausing the feature. Verify configuration, secrets, capacity limits and dependencies in the target environment. Start with enough exposure to learn without assuming that a successful demo represents every workload.

For the assistant, keep human review in place and preserve the original search-and-write process. Rehearse an unavailable policy source, a provider timeout, a repeated request and a rollback. Decide how to find unfinished work and which requests may be retried.

Produce: a release plan, runbook and a completed release decision record. Record the evidence links, known limitations, launch boundary, decision maker and next review date. The template intentionally leaves thresholds and approvals blank.

A rollback plan must account for more than the application binary. Document prompt and model configuration, retrieval/index versions, caches and persistent writes. Restoring old code does not automatically undo an external action or make an old index compatible with current data.

Move forward when: the operator can pause the feature, route users to the fallback and recover without relying on the original builder. Use the AI pilot release guide if the evidence suggests narrowing or stopping the pilot.

Have the receiving engineer run the deployment and evaluation commands from the documentation. Transfer repository and cloud access through the team's normal access process, and identify who owns incidents, costs, source updates and future changes.

For the assistant, track useful completed drafts, correction effort, abstentions, dependency failures, latency and cost using agreed definitions. Count all relevant attempts, not only successful generations. Review representative outputs with the people doing the work.

When a failure is reported, capture the source and configuration versions, expected behavior and observed result in approved storage. Create a test case after removing data that should not be retained. Re-evaluate relevant behavior after changes to permissions, sources, retrieval, prompts, models or tools.

Produce: a handover record, owner list, operating dashboard and repeatable evaluation instructions. Move forward when: the receiving team can diagnose a failed request, run the checks and explain the escalation path.

A fractional AI engineer can help with an ongoing improvement backlog. Agree the review cadence and incident responsibilities explicitly; recurring development time does not automatically include continuous support.

StellarMIND is a public chat-to-SQL project. Its repository documentation describes a Spring Boot MCP server, schema retrieval with pgvector and SQL generation through Spring AI. The repository also includes a Postman test collection.

Use it to inspect how a concrete project separates retrieval, generation, execution and client integration. The architecture differs from the illustrative policy assistant above. This roadmap does not claim that StellarMIND has passed every proposed release check or produced a measured client outcome.

For SQL delivery, carry the same questions into the database boundary: which identity executes the query, which rows it can read, which statements are permitted and what happens on timeout. A generated statement or a successful demo is only one piece of evidence.

The delivery checklist brings the seven stages into one editable document. Attach real evidence, name the reviewer and leave unresolved work visible.

Does every AI implementation need RAG? No. Use retrieval when the task needs relevant external information at request time. A structured lookup, deterministic rule or single model call may fit a narrower task. Choose from the data requirements and evaluated behavior.

How long does an AI project take? This roadmap does not assign a universal week count. Estimate after checking source access, integration dependencies, evaluation effort and reviewer availability. Record assumptions and revise the plan when they change.

When should we hire an AI consultant or FDE? Bring in an AI consultant when the unresolved decision is whether and how to build. An AI engineer can implement and evaluate the bounded feature. An FDE engagement can connect users, existing systems and delivery ownership. Agree responsibilities rather than relying only on a title.

What should be ready before the first delivery conversation? Bring one workflow, approved input examples, expected output, source owners and a person who can judge the result. Use the scope guide to document the boundary, then this roadmap to plan delivery.