AI Product Development Accelerator

We work with product teams putting AI into what they sell: mapping safety, compliance, and buyer requirements before the architecture locks, and producing independent evidence for AI you have already shipped. One engagement, both directions.

You are adding AI to your product and nobody can tell you which safety, regulatory, and customer requirements actually apply — so the team guesses, or stalls. Requirements surface late: a procurement questionnaire or a regulator's question arrives after the architecture is set, and retrofitting costs far more than designing for it. And where you have already shipped AI features, "we built it, we're confident in it" is not an acceptable answer to an enterprise buyer's security review.

Your team can build the product. What they cannot do is objectively assess their own work, or anticipate what a buyer, auditor, or regulator will demand next.

This engagement is about the product. How your organization builds AI generally — process, controls, and maturity — is the separate AI Development Lifecycle Evaluation.

What you get

• Requirements & Compliance Map — What actually applies to what you are building: regulatory obligations, sector requirements, and the questions enterprise buyers will ask — mapped early enough to design against rather than retrofit.
• Product Trustworthiness Report — A scored assessment across reliability, safety, fairness, transparency, and governance controls. Evidence-backed findings with severity ratings, for what you have shipped or are about to.
• Risk Register — Product-level risks catalogued and prioritized by business exposure, with likelihood, impact, and recommended mitigations, so you know what to address and in what order.
• Diligence Response Summary — A three to five page shareable write-up built to hand directly to enterprise procurement, regulators, or investors. Independent advisory findings, for when a buyer wants more than your own account of the product.

How it works

I. Scoping and Product Context — Kickoff to establish where each product sits (in design, in build, or already shipped), what you need to prove, and to whom. Collect product artifacts: architecture docs, model cards, data sheets, roadmaps, and user-facing disclosures.

II. Requirements Mapping and Assessment — Map the obligations that actually apply — NIST AI RMF, ISO 42001, EU AI Act, and sector-specific rules — against your product and roadmap. Structured interviews across product, engineering, data, and legal, plus product walkthrough and output analysis.

III. Synthesis and Summary Drafting — Score the trustworthiness dimensions, build the risk register, sequence what to build and fix, and write the tailored Diligence Response Summary. Peer-reviewed by a second principal — the summary gets forwarded, so it has to be airtight.

IV. Delivery and Strategy Session — Present findings and hand off the artifacts for immediate use. A one-hour session on applying the requirements map to your roadmap and using the summary to answer questionnaires.

Who this is for

• Product and engineering leaders adding AI to an existing product, or building a new AI-powered one from scratch
• CPOs, CTOs, and heads of product who own both the roadmap and the launch date
• SaaS, healthtech, fintech, legaltech, and insurtech companies shipping AI features to enterprise customers
• Teams entering regulated markets or moving upmarket, where AI trustworthiness is a procurement gate
• Any organization with a deal stalled for want of independent AI evidence

Engagement details

• Format: Independent product engagement — new builds and shipped products
• Duration: 2–5 weeks depending on scope
• Participants: Six to ten stakeholders across product, engineering, data, and legal
• Frameworks: NIST AI RMF, ISO 42001, EU AI Act, plus sector-specific requirements
• Summary: Tailored to your audience — procurement, regulator, or investor
• Follow-up: All artifacts plus a one-hour strategy session

What this does not include

• Building or implementing the product — we advise the team that builds it
• Certification, formal audit, or legal guarantee — Realis is an advisory firm, not an assessment or certification body
• Ongoing monitoring, continuous assessment, or retainer advisory
• Evaluation of how your organization builds AI generally — see AI Development Lifecycle Evaluation

AWS products supported

• Amazon Bedrock
• Amazon Bedrock AgentCore
• Amazon Bedrock Guardrails
• Amazon Nova
• AWS Audit Manager
• AWS Well-Architected
• Kiro

About Realis Solutions Group

Realis is a senior-only advisory firm at the intersection of artificial intelligence, emerging technology, and enterprise risk — advising across 72% of the Fortune 100 and dozens of public-sector organizations. No generalists, no junior bench: every engagement is principal-led by operators who have held the roles your team is navigating.