AI Governance Consulting
We Govern What We Build, From the First Commit
RTS Labs designs and implements AI governance for regulated industries — frameworks, controls, and audit-ready documentation embedded in the systems we ship. With 14+ years of enterprise AI delivery across 600+ engagements, we bring the engineering depth most governance advisors lack.
AI Governance Lifecycle
AI Inventory & Discovery · 12 systems
Risk Classification · In review
Policy & Framework Mapping
Continuous Monitoring & Audit
Years in enterprise AI
Engagements delivered
Frameworks Supported
4
Extraordinary Results for Our Clients
Extraordinary Results for Our Clients
Definition
What Is AI Governance?
AI governance is the framework of policies, controls, and oversight mechanisms that ensure AI systems are built, deployed, and operated in ways that meet regulatory requirements, manage risk, and maintain clear accountability.
AI governance addresses model-specific risks that general data or IT governance was not designed to address: bias, drift, hallucination, and the gap between how a system behaves in testing and in production.
A qualified AI governance consultant delivers more than a policy document: inventory and risk classification of AI systems in use, framework design aligned with applicable regulations, policy development with audit-ready evidence, control implementation within existing workflows, and a continuous monitoring program.
Quick Comparison
AI Governance
Policies and controls for AI systems — bias, drift, and regulatory compliance.
Data Governance
Quality, lineage, and access controls for the underlying data.
Responsible AI
The ethical principles AI governance is built to operationalize.
The Reality
Why Most AI Governance Programs Stall
AI governance fails for predictable reasons. These are the six patterns RTS sees most often.
01
Policies on Paper, No Controls in Code
Frameworks written and filed; nothing enforced in actual AI systems. Audit-ready on paper, not in practice.
02
AI Deployed Faster Than Governance Keeps Up
Business ships pilots monthly; the governance committee meets quarterly. The gap widens every sprint.
03
Multiple Frameworks, No Unified Approach
NIST AI RMF in one team, EU AI Act in another, SR 11-7 for model risk — three plans, none operational.
04
Bias and Drift Go Unmeasured
Models tested at launch, not monitored after. Performance degrades silently until a regulator or customer notices.
05
No Clear Ownership Across Legal, IT, and Business
When governance spans three departments, it often belongs to none of them. Accountability gaps cause program collapse.
06
AI Launched Without a Governance Baseline
Building AI without a governance foundation means inheriting the debt later. Before you govern, you may need to build first.
What We Deliver
Governance Services That Ship
Six service areas — a complete program or targeted gap-fill engagements.
01
AI Governance Readiness Assessment
A 2–4 week diagnostic: inventory AI systems, map risk posture to target frameworks, and produce a prioritized governance roadmap.
02
Framework Design & Implementation
Custom governance frameworks on NIST AI RMF, EU AI Act, ISO/IEC 42001, and industry-specific regulations, mapped to your controls environment.
03
Policy Development & Documentation
Audit-ready AI policies, standards, procedures, and evidence generation built for regulatory examination.
04
Risk Assessment & Bias Testing
Model evaluation across fairness, accuracy, and adversarial inputs, with documented findings that hold up in regulatory review.
05
Generative AI Governance
Guardrails, content controls, RAG safety, and agent oversight for GenAI systems in customer-facing and high-stakes internal applications.
06
Continuous Monitoring & MLOps Governance
Drift detection, retraining cadence, incident-response procedures, and ongoing regulatory-update tracking.
Vertical Depth
Governance for Regulated Industries
Most AI governance frameworks are written for a generic enterprise. RTS delivers governance calibrated to your industry’s specific regulatory landscape.
Financial Services
- Model risk management for ML credit scoring & underwriting (SR 11-7)
- Fair-lending audits for AI-driven loan decisioning
- GenAI policies for customer-facing tools
- Third-party AI vendor risk assessments
- Explainability requirements for adverse-action notices
Insurance
- Claims AI fairness testing and documentation
- NAIC AI Model Bulletin compliance across underwriting & pricing
- Automated underwriting governance
- Customer-service AI oversight in regulated contact centers
- Policy documentation for state regulatory examinations
client outcome
+40%
Increase in CSAT after deploying AI-powered customer service in a regulated insurance environment
Logistics
- Routing and dispatch AI bias documentation
- Predictive-maintenance model accountability & audit trails
- GenAI governance for driver & dispatch workflows
- Third-party AI vendor risk across supply-chain partners
- Safety-critical AI oversight: documentation & incident response
client outcome
25%
Reduction in company-wide costs after introducing a modern, governed, secure data stack — sports & manufacturing client
Real Estate & Construction
- Contract-analysis AI governance: compliance, accuracy, audit trails
- Valuation-model fairness documentation for lending & appraisal
- Fair-housing compliance for AI-assisted tenant screening
- Construction-safety AI oversight & incident response
- Regulatory readiness for state-level AI and fair-housing laws
client outcome
70/40/30
Time reduction / accuracy improvement / cost reduction — commercial real estate contract-analysis AI
Regulatory Fluency
Regulatory Frameworks We Deliver Under
AI governance requires operational experience to map controls, document evidence, and sustain compliance as frameworks evolve.
International AI Standards
US Federal & State Regulations
Industry-Specific Frameworks
Privacy & Security
How We Engage
The RTS Governance Sprint
A defined delivery methodology — five stages with clear milestones, designed for mid-market and enterprise organizations.
01
Audit
Wk 1–2
Inventory AI assets across the organization, identify shadow AI, and map current risk posture against target frameworks.
02
Frame
Wk 2–4
Design a governance framework tailored to your industry and risk tolerance. Define accountability, ownership, and review cadence.
03
Build
Wk 4–12
Develop policies, controls, monitoring procedures, and documentation. Integrate with existing GRC and MLOps tooling.
04
Embed
Wk 8–16
Operationalize controls into AI development workflows. Train legal, IT, and business owners. Generate audit-ready evidence.
05
Sustain
Wk 12–24+
Continuous monitoring for drift and bias, regulatory-update tracking, quarterly governance reviews, and annual policy refresh.
“Governance done well isn’t a policy document. It controls the systems we ship. We design for an audit from day one — not after the regulator calls.”
Alexander Hogancamp — Director of AI and Automation, RTS Labs
Counter-Counsel
When AI Governance Is the Wrong Starting Point
AI governance is the right investment for organizations with AI in production facing regulatory pressure or governance gaps. It is not the right investment in every situation.
No AI in Production Yet
If your organization has plans for AI but no deployed systems, a governance framework has no scope. Start with an AI strategy or readiness assessment.
A Single AI Use Case
A full governance program is disproportionate for one tool. A targeted, use-case-specific risk review is a better starting point.
No Executive Sponsor
Governance that sits below the executive level rarely survives the first budget cycle. The program needs a named owner with authority to enforce it.
Already in Active Regulatory Crisis
If you’re responding to a regulatory investigation, legal counsel should lead. Governance consulting follows the legal response.
Looking for a Paper Certificate, Not Controls
RTS builds governance that functions under examination. If the goal is a checkbox document, we are not the right partner.
The Honest Comparison
RTS vs. Your Other Options
Regulated-industry buyers typically evaluate four paths. Here’s an honest look at what each delivers.
Comparison reflects RTS’s understanding of publicly available competitor positioning. Buyers should verify directly with each vendor.
Proof
Real Systems, Real Outcomes
In regulated industries, the cost of getting AI wrong is concrete. These are the systems we built to make sure they didn’t.
Insurance
HSA Trustee — Customer Service AI
Customer-service AI deployed in a regulated insurance environment with full audit trail, escalation governance, and compliance documentation.
+40% CSAT
increase in customer satisfaction
Legal / Real Estate
Commercial Real Estate Contract Analysis
AI-powered contract review applied to high-stakes commercial documents, with accuracy controls, audit trails, and documented review standards.
70 / 40 / 30
time reduction | accuracy gain | cost reduction
Legal
Preferred Legal: AI-Powered Drafting
GenAI-powered legal document drafting with output controls, review checkpoints, and audit-ready documentation standards.
document prep · 120 min → 10 min
Why RTS Labs
Engineering Depth, Governance Discipline
We know what we’re governing — we’ve already built it for Finance, Insurance, Logistics, and Real Estate clients.
01
14+ Years Building AI in Regulated Industries
Our governance practice is grounded in what we actually build. We know which controls hold up because we’ve seen what fails in production.
02
Engineering + Governance, Not Policy-Only
Controls built into the systems we ship. Frameworks that survive contact with production.
03
U.S.-Based, Compliance-Aware Delivery
100+ U.S.-based experts. Compliance considerations from day one of every engagement.
04
Defined Timelines, Outcome-Tied Engagements
Sprint methodology with specific week ranges — not open-ended advisory retainers.
leadership
Jyot Singh
Founder & CEO
Founded RTS Labs 14+ years ago. Has led AI and data-engineering engagements across Finance, Logistics, Insurance, and Healthcare for mid-market and enterprise clients.
Alex Hogancamp
Director of AI
Leads AI strategy and LLM engineering at RTS. Designed the Production Sprint methodology and oversees model architecture, evaluation, and governance practices.
Sandhya Ramamurthy
Director of Data Engineering
Leads data platform and pipeline architecture. Specializes in making enterprise data production-ready for AI — the step most projects skip and most projects fail at.
Practitioner Credibility
Tools and Platforms We Work With
Tool-agnostic, platform-experienced. We work with the monitoring infrastructure your AI systems run on.
Governance Platforms
Monitoring & Observability
AI / LLM Frameworks
Cloud Platforms
Data & MLOps
Compliance & GRC
Original Research
Inside the RTS AI Lab
We don’t just consult — we test, benchmark, and publish. Original research your team can act on.
[ Research ]
Document Extraction Across 9 LLMs
Governance frame: model-selection risk. Which models are reliable for compliance-sensitive extraction — and which fail silently in high-stakes document workflows.
[ Research ]
Bias & Fairness Patterns in Enterprise AI
Governance frame: real-world bias signals from RTS engagements. Where models drift, how to catch it before a regulator does, and what remediation looks like in practice.
[ Research ]
AI in Contract Review — Risk Profile
Governance frame: when GenAI contract review is defensible and when it isn’t. Practitioner lessons from the commercial real estate engagement.
After Go-Live
What Happens After We Ship
of organizations state that robust AI governance frameworks directly increase consumer trust and accelerate executive buy-in for new AI initiatives.
Continuous Monitoring
Drift, bias, and performance alerts wired into your observability stack.
Regulatory Update Tracking
We monitor EU AI Act, NIST, and state-law changes — and notify you of what affects your AI portfolio.
Annual Policy Refresh
Policies revisited yearly against your current AI inventory and regulatory landscape.
Audit-Readiness Support
When the regulator calls, you have documented evidence ready in hours, not weeks.
Optional Managed Governance
For teams that need ongoing governance ops without growing headcount.
Client Voices
What Clients Say
“RTS Labs has been an invaluable partner in our journey. Their expertise and ability to understand our vision allowed them to deliver innovative, AI-powered solutions that align perfectly with our goals. Their collaborative approach and commitment to excellence have transformed how we operate, driving efficiency and unlocking new opportunities. We couldn’t have asked for a better partner.”
Jason Herzog
Founder, Holon Health
“RTS Labs became an extension of our team, delivering AI solutions that transformed how we operate. Their understanding of our needs and collaborative approach unlocked opportunities we hadn’t imagined. Truly a game changer for our business.”
Gemma Brooks
COO, BlueOceanBrain.com (an HSI Company)
FAQ
Common Questions
What is AI governance and why does it matter?
AI governance is the system of policies, controls, oversight processes, and accountability structures that ensure AI systems operate within legal, regulatory, and organizational boundaries. It matters because AI systems make consequential decisions — credit approvals, insurance underwriting, hiring, medical triage. Without governance, those decisions expose organizations to regulatory penalties, litigation, and reputational damage. For regulated industries, governance is increasingly a compliance requirement — the Federal Reserve (SR 11-7), state insurance commissioners (NAIC AI Model Bulletin), and the EU AI Act are issuing specific guidance organizations are expected to operationalize.
How is AI governance different from data governance?
Data governance manages how data is collected, stored, accessed, and maintained. AI governance governs what AI systems do with that data: the decisions they make, the models driving those decisions, and the controls that ensure models behave as intended over time. Data governance focuses on quality, lineage, and access. AI governance focuses on model risk: bias, drift, hallucination, unexplainability, and the gap between tested and production behavior.
Which AI regulations apply to my business?
It depends on your industry, geography, and how you use AI. Financial-services firms under Federal Reserve or OCC oversight should know SR 11-7. Insurers should review the NAIC AI Model Bulletin. Companies with EU operations or EU customer data face EU AI Act obligations. Horizontal frameworks like NIST AI RMF and ISO/IEC 42001 are widely applicable and increasingly referenced by regulators. State laws (NYC Local Law 144, Colorado SB21-169) apply in specific contexts.
How much does AI governance consulting cost?
Engagement cost varies by scope: the number of AI systems in scope, the frameworks that apply, and whether the engagement is a one-time program build or an ongoing managed arrangement. A focused readiness assessment runs differently from a full framework implementation with continuous monitoring. RTS provides defined-scope engagements with milestone-based pricing rather than open-ended retainers.
How long does it take to implement an AI governance framework?
A focused readiness assessment takes 2–4 weeks. A full framework design and implementation — including policy development and controls embedding — typically runs 12–16 weeks, depending on the number of AI systems in scope and regulatory complexity. Ongoing governance is structured as a defined sprint with a sustained monitoring phase rather than an open-ended engagement.
Do we need an AI governance platform, or is consulting enough?
For organizations with a small number of AI systems and a clear regulatory scope, consulting to build the framework, policies, and controls is often sufficient. For organizations running many AI systems in production requiring continuous monitoring at scale, a governance platform (Credo AI, OneTrust, IBM watsonx.governance) can provide the operational infrastructure. RTS implements governance programs that work with or without these platforms, and advises whether a platform investment is warranted.
What is the difference between AI governance and Responsible AI?
Responsible AI is a principles-based framework addressing the ethical dimensions of AI: fairness, transparency, accountability, and societal impact. AI governance is the operational and regulatory layer — controls, documentation, oversight processes, and accountability structures that make responsible-AI principles auditable and enforceable. You can have responsible-AI principles without governance, but you cannot demonstrate regulatory compliance without governance.
How do you measure bias and drift in AI models?
Bias measurement evaluates model outputs across demographic groups and protected classes to identify statistically significant disparities — for credit, insurance, and hiring applications this includes disparate-impact analysis and fairness-metric testing. Drift measurement tracks whether a model’s performance degrades over time as real-world inputs change. RTS uses tools including Arize AI and Fiddler AI to establish baselines and alert thresholds, with defined procedures for investigation and retraining.
What if we already have data governance in place?
Existing data governance is a strong foundation — but not a substitute for AI governance. Data governance addresses lineage, quality, and access. AI governance adds the model layer: risk classification, bias testing, explainability, performance monitoring, and regulatory documentation specific to AI decision systems. RTS integrates AI governance into your existing GRC environment rather than replacing it, so your data-governance investments remain in place.
Why choose RTS Labs over a Big-3 consultancy or a governance platform?
Big-3 consultancies deliver governance at global scale for enterprises with complex, multi-jurisdictional programs and the budget to match. For mid-market organizations ($100M–$4B), the cost structure and engagement model are often misaligned. Governance platforms provide excellent continuous-monitoring infrastructure but require implementation the platform alone doesn’t provide. RTS is the team that builds AI and governs it — controls embedded in systems we ship, not bolted on after.
Let's Talk
Govern AI Like You Built It
If your AI program is outpacing your governance, or a regulatory deadline is driving urgency, the first step is a conversation. RTS works with mid-market and enterprise organizations in Finance, Insurance, Logistics, and Real Estate to design and implement AI governance that withstands scrutiny.
Mid-market & enterprise · NIST AI RMF, EU AI Act, ISO 42001 supported · U.S.-based team. We respond within one business day. No-pressure conversation.