We are early in AI adoption — is governance premature?

It is cheapest now. Retrofitting an inventory, risk tiering, and evidence trail across dozens of live models costs far more than establishing the pattern while you have three.

Does governance slow delivery down?

Done well it speeds delivery up, because low-risk use cases get a fast lane instead of ad-hoc committee review. We tier by risk so only high-impact systems carry the heavy process.

How quickly can an engagement start?

Discovery usually begins within one to two weeks of scope agreement, and a delivery team is typically mobilised within three to four weeks depending on the skills involved.

Can you work alongside our in-house team?

Yes — most of our engagements are blended. We work in your repositories, ceremonies, and tooling, and we plan for knowledge transfer from the start so capability stays with you.

How are engagements priced?

Fixed price for well-bounded projects, monthly capacity for dedicated pods, and outcome-linked SLAs for managed services. We are explicit about assumptions and what would change the number.

NeonAITech services — AI and data engineering, product engineering, cloud and DevOps, managed operations, security and quality

Responsible AI Governance & Assurance

Deploy AI you can explain to your board, your auditor, and your regulator.

Book Free Consultation All Services

1 register

Single inventory of every model in use

Audit-ready

Evidence packs per deployed model

Weeks

Not quarters, to first governed release

Overview

What Responsible AI Governance & Assurance means at NeonAITech

AI regulation and enterprise risk policy have caught up with AI adoption. We stand up the governance layer — model inventories, risk classification, bias and robustness testing, and audit evidence — so your teams can move fast while staying defensible under the EU AI Act, ISO/IEC 42001, and the NIST AI Risk Management Framework.

Tools & Platforms

ISO/IEC 42001
NIST AI RMF
EU AI Act
Evidently
Giskard
MLflow

We are not tied to a single vendor — the stack follows the problem, your existing estate, and your team’s skills.

What We Deliver

AI Risk & Control Framework

A practical operating model: model registry, risk tiering, approval workflow, and clear accountability from data owner to deploying team.

Model Assurance Testing

Bias, robustness, prompt-injection, and explainability testing with reproducible evidence packs for every model in scope.

Audit & Compliance Readiness

Documentation, technical files, and monitoring evidence mapped to the frameworks your auditors and regulators actually cite.

Capabilities

Inside the Engagement

EU AI Act and ISO/IEC 42001 readiness

NIST AI RMF control mapping

Bias, fairness, and disparate-impact testing

Prompt-injection and adversarial red teaming

Continuous drift and incident monitoring

How We Work

A delivery rhythm you can see into

Every Responsible AI Governance & Assurance engagement runs the same four phases, with AI used wherever it removes effort rather than adds novelty.

01
Discover
We map the current state, agree the outcome, and size the work — so scope is a shared decision, not a surprise.
02
Design
Architecture, delivery plan, and success measures are set before build, with costed options where trade-offs exist.
03
Build
Short increments with working output you can review, steer, and stop — never a black box until go-live.
04
Operate
We measure against the agreed outcomes, hand over documentation, and stay on for support where you want it.

Engagement Models

Buy it the way that fits

Fixed-Scope Project

A defined outcome, timeline, and price. Best when requirements are clear and the deliverable is well bounded.

Dedicated Pod

A cross-functional team working to your backlog and priorities, scaling up or down with a month’s notice.

Managed Service

Ongoing ownership against agreed SLAs, with a share of capacity reserved for continuous improvement.