AI Development Lifecycle Evaluation — Realis Solutions Group

The problem

Your team believes it follows best practices — but without an independent assessment, you don't actually know where the governance gaps are.
Customers, investors, and regulators are asking about your AI governance and you don't have credible, independent evidence to show them.
The EU AI Act, NIST AI RMF, and state AI regulations are accelerating faster than most internal frameworks can adapt.
Governance gaps are invisible from the inside — baked into the process from day one, and your team can't audit its own work objectively.
One production failure, a regulatory inquiry, or a single lost enterprise deal costs 10–50x what an assessment costs.

Who this is for

CTOs and VPs of Engineering at $5M–$50M ARR companies with in-house ML or LLM development.
Engineering teams 6–24 months into building AI features without dedicated AI governance or MLOps infrastructure.
Product and engineering leads fielding AI governance questions from customers or investors.
Companies preparing for Series B/C fundraising, M&A diligence, or entry into regulated or enterprise markets.
Leadership that knows AI governance is a problem but can't articulate what specifically needs to change.

What you get

You walk away knowing exactly where your governance gaps are — with a prioritized plan to fix them, not just a list of problems.

AI-DLC Risk Assessment

A scored evaluation across data, model, deployment, monitoring, and human-oversight dimensions — shareable with your board, customers, and auditors as evidence of governance maturity.

Prioritized Action Plan

Your governance coverage vs. gaps, benchmarked against NIST AI RMF, ISO 42001, and the EU AI Act — turned into sequenced, owned next steps rather than a static gap map that sits on a shelf.

Remediation Roadmap

A 90-day action plan with sequenced recommendations, effort estimates, and ownership assignments. A plan you can execute — tied to your strategic priorities.

Executive Summary

A 2-page board- and investor-ready summary of your AI risk posture, key findings, and recommended actions. Ready to share the moment you receive it.

How it works

Scoping + Document Collection

Kickoff with the CTO/VP Engineering to define scope and primary use cases. Collect architecture docs, model cards, data pipelines, CI/CD configs, and governance policies. Schedule stakeholder interviews.

Interviews + Framework Analysis

6–10 structured interviews across engineering, product, data, and operations. Review artifacts against NIST AI RMF, ISO 42001, and the EU AI Act, scoring each dimension against the evidence.

III

Synthesis + Deliverable Production

Score gaps, prioritize remediations with sequencing and effort estimates, and draft the report and executive summary. Peer-reviewed by a second principal before delivery.

Delivery + Strategy Session

Present findings to the executive team, deliver all artifacts, and run a 1-hour strategy session on remediation sequencing, ownership, and how to communicate your posture.

Logistics

Format	Process & governance assessment
Duration	2–5 weeks depending on scope
Participants	6–10 stakeholders: eng, product, data, ops
Frameworks	NIST AI RMF · ISO 42001 · EU AI Act
Purchasing	Direct · AWS Marketplace · Partners
Follow-up	All deliverables + 1hr strategy session

What this does not include

Implementation of the remediation roadmap (available as a follow-on engagement).
Ongoing monitoring, continuous assessment, or a governance retainer (available separately).
Legal opinion, regulatory certification, or adversarial pen testing of the AI system.

NEXT STEP

A 20-minute intro call with a Realis principal. We'll define scope, identify which AI systems are in scope, and give you a fixed price before you commit.

AI-DLC Risk Assessment

Prioritized Action Plan

Remediation Roadmap

Executive Summary

Scoping + Document Collection

Interviews + Framework Analysis

Synthesis + Deliverable Production

Delivery + Strategy Session

Know where your gaps are.