AI governance services for every stage

From first idea to fully regulated operation. Right-sized governance at every step of your AI journey

Stage 01 — Idea Stage

Idea-Stage AI Founders

Early concept or prototype phase. Understand your risks before you build further.

Who this is for: You have an AI product idea or early prototype. You have not yet built a full product. The decisions you make now about data, use cases, and architecture will determine whether you build a responsible product — or one that creates legal, ethical, or commercial problems you cannot fix later.
Key challenges
  • No awareness of what AI-specific risks your use case carries
  • Unclear data usage boundaries — what you can and cannot collect, store, and use
  • Potential misuse scenarios you have not considered
  • Ethical exposure — bias or harm baked into the concept before a line of code is written

AI Idea Risk Scan

A structured review of your AI product concept against known risk categories — regulatory, ethical, data, and commercial. We identify the risks your idea carries before you invest in building. You receive a written risk summary and a go / proceed-with-caution / restructure recommendation.

Use Case Risk Mapping

A detailed mapping of your intended AI use cases against regulatory frameworks including the EU AI Act risk classification system. We identify whether your use cases fall into prohibited, high-risk, limited-risk, or minimal-risk categories — and what that means for how you build

Basic Data Strategy Advisory

A focused advisory session covering what data you plan to use, how you plan to collect it, and what the privacy and compliance implications are. We identify data strategy decisions you need to make before you build — not after.

High-Level AI Architecture Risk Guidance

A review of your proposed AI architecture — models, data flows, integrations, deployment approach — from a governance and risk perspective. We flag architecture decisions that create compliance problems or liability exposure before they are built in.

Outcome

Clarity on the risks your AI idea carries before you commit to building. Make informed architecture, data, and use case decisions from the start.

1–2 weeks depending on complexity of use case

Stage 02 — MVP & AI Agent

AI Agent & MVP Builders

Prototype or AI agent already built. Make it safe before you launch.

Who this is for: You have built something — an AI agent using open-source models or APIs, an MVP using vibe coding tools like Cursor or Bolt, or a prototype ready for testing. You want to launch but are not confident the product is safe. Hallucinations, unpredictable behaviour, data exposure, and open-source licensing are concerns you have not fully addressed.
Key challenges
  • Unsafe to launch — you do not know what the product will do at edge cases or under adversarial use
  • Hallucination and unpredictable AI behaviour — especially for AI agents taking autonomous actions
  • Data leakage and misuse risks — user data, personal information, confidential inputs
  • Open-source licensing concerns — are you using models and code in ways that comply with their licences

AI Agent Risk Audit

A comprehensive risk assessment of your AI agent — covering autonomous action scope, failure modes, hallucination risk, data handling, accountability gaps, and human oversight. Written audit report with specific findings and recommended fixes before launch.

Pre-Launch Safety Review

A structured review against a pre-launch safety checklist covering output safety, data privacy, user harm scenarios, edge case behaviour, and basic incident response. Pass / fail rating with remediation guidance for any failures.

Open-Source Compliance Check

A review of every open-source model, library, and dataset your product uses. We check licence compatibility, usage restrictions, commercial use permissions, and attribution requirements.

Data Handling Risk Assessment

A focused assessment of how your product collects, processes, stores, and transmits data. We identify privacy compliance gaps, consent issues, and cross-border data transfer risks under GDPR, India's DPDP Act, and other applicable frameworks.

AI Behaviour Testing Scenarios

Development and execution of structured adversarial and edge case test scenarios. We test for harmful outputs, jailbreak vulnerability, demographic bias, hallucination patterns, and unexpected refusals. Testing report with findings and recommended guardrails.

Outcome

Your AI product is safe for controlled launch. You know what it does, what it does not do, and what guardrails are in place.

2–4 weeks depending on product complexity

Stage 03 — Early Growth

Seed & Series A AI Startups

Live product with initial users. Scale without stability or reliability risk.

Who this is for: You are live. Real users are using your product. You have initial traction and are focused on growth. But as your user base grows, you are encountering governance challenges — inconsistent outputs, scaling instability, lack of monitoring, and model update risks.
Key challenges
  • Scaling risks — behaviours acceptable at 100 users create serious problems at 10,000
  • Inconsistent outputs — no systematic way to catch when your model misbehaves
  • Lack of monitoring — no alerting, no logging, no ability to detect when something goes wrong
  • Model update risks — you update your model and something breaks in production with no rollback plan

Production AI Risk & Stability Audit

A comprehensive audit of your AI system in production — reviewing performance consistency, failure rate patterns, demographic performance gaps, output quality monitoring, and stability under load. Risk-rated findings report with remediation priorities.

Monitoring & Logging Gap Analysis

A review of your current monitoring, logging, and alerting infrastructure. We identify what you are not tracking that you should be, and what minimum monitoring infrastructure you need for responsible operation at scale.

Model Lifecycle Risk Assessment

A review of how you manage your AI models across their full lifecycle — selection, deployment, versioning, updating, and retirement. Lightweight model management framework that reduces update risk and maintains accountability.

Human-in-the-Loop Design Review

A review of where human oversight exists — and where it does not — in your AI system. We identify decisions that should have human review and design appropriate controls that do not kill product velocity.

System Architecture Risk Review

A governance-focused review of your AI system architecture — data flows, model integrations, third-party dependencies, and failure cascade risks.

Outcome

Improved reliability and controlled scaling. Visibility into what your AI system is doing, systematic monitoring for when things go wrong, and governance controls that grow with your product.

3–6 weeks depending on system complexity

Stage 04 — Enterprise-Bound

Enterprise-Selling AI Startups

Selling to enterprise clients. Stop losing deals to governance questions.

Who this is for: You are in active enterprise sales conversations. When the due diligence stage arrives — vendor questionnaires, security reviews, compliance assessments — you do not have the documentation or frameworks to answer credibly. Deals stall or die at this stage.
Key challenges
  • Failing enterprise due diligence — you cannot answer governance, risk, and compliance questions
  • Security and compliance questionnaires — 40+ question vendor assessments with no answers
  • Lack of documentation — no risk register, no governance policies, no model documentation
  • Deal blockers — governance gaps preventing you from closing deals you should be winning

Enterprise AI Readiness Assessment

A structured assessment of your AI product against the governance, security, and compliance requirements enterprise buyers apply. You receive a readiness scorecard — what you pass, what you fail, and the gap-closing work required.

AI Due Diligence Simulation

We put you through a simulated enterprise due diligence process — asking the same questions a sophisticated enterprise buyer or investor would ask. You experience exactly where you fail, and we build a remediation plan.

AI Documentation Setup

Creation of the core AI documentation package enterprise buyers expect — model documentation, data governance documentation, system architecture overview, performance and testing documentation, and limitations disclosure.

Risk Register Creation

Development of a formal risk register covering all identified AI risks — with descriptions, likelihood and impact ratings, current controls, risk owners, and review schedule.

Security & Compliance Gap Analysis

A focused review of your security and compliance posture from an enterprise buyer's perspective. We identify the gaps most likely to surface in vendor assessments and prioritise remediation by commercial impact.

Lightweight Governance Policy Setup

Creation of a core governance policy suite — AI use policy, data governance policy, model risk policy, and incident response policy — tailored to your company size and product.

Outcome

You can answer enterprise governance questions credibly and completely. Governance stops being a deal blocker and starts being a competitive advantage.

4–8 weeks depending on gap size

Stage 05 — Scale & Compliance

Scaling & Regulated AI Companies

Operating at scale or entering regulated industries. Full governance and compliance readiness.

Who this is for: You are operating at significant scale or entering heavily regulated sectors. Compliance requirements are mandatory. Regulatory frameworks impose formal obligations. Audits are a real and near-term prospect.
Key challenges
  • Formal compliance requirements — regulations impose specific documented obligations
  • Audit readiness — need documented evidence of governance for third-party audits
  • Legal exposure — non-compliance carries real financial and reputational penalties
  • Governance structure gaps — informal governance no longer sufficient at this scale

AI Governance Framework Implementation

Design and implementation of a complete AI governance framework — governance structure, policies, processes, controls, accountability assignments, and continuous improvement. Built to ISO 42001 standards and aligned with NIST AI RMF.

Regulatory Alignment — EU AI Act

Full compliance alignment with the EU AI Act — risk classification, conformity assessment preparation, required technical documentation, transparency obligations, human oversight requirements, incident reporting, and registration obligations.

AI Risk Management System Setup

Design and implementation of a formal AI Risk Management System — risk identification methodology, assessment processes, treatment procedures, monitoring and review mechanisms, and documentation standards.

Audit Preparation & Support

Preparation for third-party governance audits — documentation review, evidence collection, gap remediation, and support during the audit process.

Explainability & Transparency Setup

Implementation of explainability and transparency requirements — user-facing notices, model explanation documentation, decision audit trails, and explainability testing.

Internal AI Compliance Training

Design and delivery of internal AI compliance training — covering governance framework, regulatory obligations, employee responsibilities, and how to identify and escalate AI governance concerns.

Outcome

Full compliance readiness and structured governance. You can face regulatory scrutiny, third-party audits, and enterprise due diligence with complete confidence.

8–16 weeks depending on scope

Stage 06 — Regulated Sector | Available by Application

Finance, Healthcare & Government AI Companies

Highly regulated environments. Long-term governance partnership and continuous compliance.

Who this is for: You operate AI systems in financial services, healthcare, legal services, or government — sectors with mandatory regulatory oversight, periodic audits, regulatory reporting obligations, and high liability exposure.
Key challenges
  • Mandatory audits — periodic governance audits required by regulators or contracts
  • Regulatory reporting — formal reporting obligations to sector regulators
  • High liability risk — AI failures carry serious legal, financial, and reputational consequences
  • Continuous compliance — governance is an ongoing operational requirement not a project

Continuous AI Compliance Monitoring

Ongoing monitoring covering regulatory developments, compliance posture, and emerging risks. Monthly reports, regulatory update briefings, and proactive gap identification.

Regulatory Reporting Support

Support for formal regulatory reporting — preparation of required submissions, incident reporting documentation, performance disclosures, and communication with regulatory bodies.

Third-Party Audit Coordination

Management of third-party AI governance audits — from preparation through evidence submission to findings response and remediation.

Governance Advisory Retainership

Senior governance advisory retainer for strategic decisions, regulatory navigation, incident response, and governance evolution as your business scales.

End-to-End AI Governance Lifecycle Management

Complete ongoing management of your AI governance function — framework maintenance, policy updates, training delivery, audit coordination, regulatory monitoring, and incident management.

Outcome

Long-term governance partnership and compliance sustainability. Your AI governance function operates continuously, proactively, and to the standard your sector requires.

Ongoing — minimum 6-month engagement

FAQ

How long does each engagement take?

It depends on your stage and the complexity of your AI systems. As a guide: Stage 01 idea-stage engagements typically take 1–2 weeks. Stage 02 pre-launch safety reviews take 2–4 weeks. Stage 03 growth audits take 3–6 weeks. Stage 04 enterprise readiness takes 4–8 weeks. Stage 05 full governance framework implementation takes 8–16 weeks. We agree timelines at the start of every engagement based on your specific situation.

Do you sign NDAs?

Yes. We sign a standard mutual NDA before every engagement begins. Your product details, architecture, and data practices are confidential. Nothing leaves the engagement without your explicit consent.

Do we need to share our code or model weights with you?

No. We do not require access to your source code or model weights. Our assessments are conducted through structured documentation reviews, interviews with your technical team, and system behaviour testing. You control what you share with us at all times.

What if we are between stages or not sure which stage we are at?

That is exactly what the free discovery call is for. Most clients are not sure which stage describes them when they first reach out. We ask the right questions, identify your stage, and recommend the right starting point. Book the call and we will work it out together.

Can you work directly with our technical team?

Yes. We work closely with technical teams at every stage. Our assessments involve structured sessions with your engineers, data scientists, and product managers. We do not just talk to founders — we get into the detail with the people building the product.

What happens after the engagement ends?

Every fixed-scope engagement ends with a written deliverable — an audit report, a governance framework, a compliance roadmap, or a documentation package depending on the service. We include a 30-day post-engagement support window for questions about the deliverables. Clients who want ongoing support can move to the Fractional AI Governance Officer retainer or a Stage 06 ongoing engagement.

We are a very small team — is this relevant for us?

Yes — often more so than for larger teams. Small teams move fast and ship fast. The governance gaps that create real problems — unsafe product launches, failed enterprise due diligence, EU AI Act exposure — do not wait until you reach a certain team size. Our Stage 01 and Stage 02 services are specifically designed for small teams and are scoped and priced accordingly.

Not sure which stage describes you?

Book a free 30-minute discovery call. We will identify your stage, your most important governance gaps, and the right starting point.

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Recruitment Process

These cases are perfectly simple and easy to distinguish.

  • Reducing Redundancy
  • Uncovering Hidden Resources
  • Increasing Company’s Agility

Recruitment Process

These cases are perfectly simple and easy to distinguish.

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Employee Relations

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  • Improving Communication
  • Employee issue resolution
  • Proper Documentation Process

Employee Relations

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Compliance Audits

Prevents our being able too what get like best every pleasure.

  • Handling of employment
  • Greater retention rates
  • A fully engaged workforce

Compliance Audits

Prevents our being able too what get like best every pleasure.

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Making What’s
Possible in Human Resource
01

Employee Compensation

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Health Care Benefits

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Talent Management

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Risk Management

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Successful &
Sustainable Growth

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Recruitment Process 48%

Employee Relations 79%

Compliance Audits 65%

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Flexible Plan
Perfect for Small & Lare Brands

Bronze Package

Pricing plan for startup company

149

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Silver
Package

Pricing plan for startup company

249

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Gold
Package

Pricing plan for startup company

349

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Bronze Package

Pricing plan for startup company

349

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Silver
Package

Pricing plan for startup company

549

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Gold
Package

Pricing plan for startup company

949

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