One workflow, clear controls
The paid discovery and pilot engagement defines one workflow, its data boundary, human review points, evaluation, rollback, and operating owner before anything is built.
The scoping call is free. Written plans, evaluation, and implementation begin only after you approve a paid scope.
The workflow before the model
Secure AI adoption introduces AI agents, copilots, or retrieval-augmented generation (RAG) into a defined workflow with data boundaries, human review, evaluation, audit records, rollback decisions, and an operating owner set before deployment.
AI discovery and pilot work for one agreed workflow, with data boundaries, human review, evaluation, rollback decisions, and ownership defined before work starts.
Nelson works with engineering teams to turn uncertain technical work into an operating decision they can own. This feedback comes from CI/CD handoff work, not an AI project.
Nelson was awesome to work with. He came in and became a great partner to our lead engineer, helped architect a sustainable solution, and then handed over everything smoothly. Adjacent client feedback from a CI/CD architecture and handoff engagement, 2019.
The first four decisions keep the pilot bounded. The final three define what can be built and how it will be grounded, reviewed, monitored, and handed over.
Useful outcome
Name the workflow, the person using it, the current friction, and the signal that would make a pilot worth continuing.
Data boundary
Identify the sources, sensitivity, access rules, logging needs, and limits that shape architecture and provider choices.
Human review and rollback
Set review points, escalation, failure handling, and rollback decisions around the consequence of a wrong output.
Operating owner
Connect the workflow to the product, engineering, operations, security, or legal owners who will make later decisions.
Agents and copilots
Define whether the workflow drafts, retrieves, recommends, or takes an action. Place human approval before a consequential step, and keep a manual path when the system is paused.
RAG sources and access rules
Data governance for retrieval-augmented generation (RAG) defines approved sources, access controls, data provenance, retention, and how retrieved evidence is checked before an output is trusted.
Responsible AI and model operations
Set input and output guardrails, evaluation cases, audit records that form a reviewable audit trail, model and prompt versioning, monitoring, incident handling, and rollback criteria within the agreed pilot boundary.
The pilot engagement
Discovery and pilot work for one workflow
The engagement is bounded around the actual workflow. Data access, integration points, review risk, model choice, and owner availability determine what fits.
Confirm the workflow, owner, data boundary, review risk, evaluation signals, and the smallest useful first phase. The team can decide whether to build or pause.
Create the agreed proof of concept or integration, apply the agreed controls, and test it against the evaluation and review plan. The pilot is judged against explicit limits.
Hand over decisions, operating notes, known limits, and the recommendation to expand, revise, or stop. The next investment is a conscious choice.
Inside the paid engagement
The exact mix depends on the workflow. The work may include:
A pilot is not a company-wide AI program, and it does not guarantee production fit or a business result. Timing and fees depend on the workflow, data access, integration points, review risk, architecture choices, and team ownership. A written proposal sets the boundary before work starts.
The free scoping call
We discuss the workflow, intended user, data involved, review risk, operating owner, and what would make the pilot useful enough to continue.
The call does not include use-case scoring, a proof of concept, architecture, vendor selection, policy work, an evaluation harness, or a written pilot plan. Those begin only in an agreed engagement.
The useful answers come from the workflow, data, review risk, and owner, not a generic model list.
Secure AI adoption introduces AI agents, copilots, or retrieval-augmented generation (RAG) into a defined workflow with data boundaries, human review, evaluation, audit records, rollback decisions, and an operating owner set before deployment.
A useful candidate has a named user and owner, accessible data, a repeatable task or decision, visible review risk, and a signal the team can use to decide whether to continue.
Discovery starts with the data sources, sensitivity, permissions, retention, logging, and where information may be processed. The written plan states the boundary and any architecture work included.
That decision follows the workflow, data, latency, operating, and review constraints. The written plan states whether model or provider evaluation is part of the work.
Potentially. Fit depends on the available interfaces, data access, security constraints, workflow ownership, and what can be tested safely within the agreed boundary.
Use the agreed evaluation signals, review burden, failure patterns, operating effort, user response, and unresolved risk. The handoff should make a stop or revise decision as visible as a scale decision.
Choose the next useful step
Use the free scoping call to test the workflow, data boundary, review model, and ownership before deciding whether a pilot makes sense.