AI Risk and Model Governance Frameworks
End-to-end model lifecycle controls covering inventory, classification, risk tiering, validation, monitoring, and decommissioning. Tailored to your regulator and aligned with NIST AI RMF and ISO 42001.
Service
Enterprise AI, Built to Pass Risk Review and Deliver Value.
Three integrated pillars under one practice: AI Governance and Compliance, Generative AI and LLM Consulting, and Intelligent Process Automation. Designed for banks, insurers, healthcare, government, and large operators that need AI to stand up to regulators and to the board.
What is Enterprise AI Consulting?
Enterprise AI Consulting, defined
Enterprise AI Consulting is an integrated practice that helps regulated and large enterprises adopt AI safely and profitably. Magnis AI delivers governance frameworks aligned to MAS FEAT, RBI, EU AI Act, GDPR, HIPAA, and PDPA, production-grade generative AI and LLM systems, and intelligent process automation, all under a single operating model that produces audit-ready evidence alongside working code.
Overview
Enterprise AI Consulting, the practice in summary
Most enterprise AI initiatives stall between proof of concept and production because governance, risk, and integration are treated as afterthoughts. Magnis AI inverts the sequence. We start with the regulatory envelope, design controls into the architecture, and ship AI that auditors, customers, and boards can defend. Three connected pillars, one operating model.
Our three pillars
Three integrated practices, one operating model
Pillar 01
AI Governance and Compliance
AI governance is the foundation that allows regulated enterprises to deploy AI without exposing the business to regulatory, reputational, or operational risk. Magnis AI designs and operationalises governance frameworks that satisfy MAS FEAT, RBI model risk guidance, the EU AI Act, GDPR, PDPA, HIPAA, and SOX. Our work spans policy design, model risk management, control libraries, audit evidence, and the engineering primitives needed to enforce governance in production.
Regulatory Compliance Mapping
Crosswalks between your existing controls and MAS FEAT, RBI guidance, EU AI Act Article 9 to 15, GDPR Article 22, PDPA, HIPAA, SOX, and the OECD AI Principles. We hand you the evidence artefacts auditors expect.
Explainability and Interpretability
Model-agnostic explanation pipelines using SHAP, LIME, counterfactuals, and concept-based explanations. Translated for non-technical stakeholders, regulators, and customer-facing disclosures.
AI Audit and Continuous Monitoring
Production observability for drift, fairness, bias, robustness, prompt injection, hallucination, and data leakage. Alerting tied to your incident response runbook.
Responsible AI Policy Design
Enterprise AI policies, acceptable use guidelines, and ethics committee charters that align legal, risk, security, and product teams behind a single operating model.
Third-Party and Vendor AI Risk
Assessment frameworks for SaaS AI tools, foundation model providers, and AI-augmented vendors. Contract clauses, DPIA templates, and exit criteria included.
Framework
The Magnis Governance Operating Model
Four interlocking layers: Strategy (board mandate and risk appetite), Policy (standards, controls, evidence), Operations (model lifecycle, audit, monitoring), and Engineering (guardrails, evaluations, telemetry). Each layer maps to specific roles, artefacts, and KPIs so governance becomes measurable rather than aspirational.
Outcomes
- Audit-ready evidence pack covering every production AI system
- Reduced model risk capital and lower regulatory remediation exposure
- Faster time to deploy by removing approval bottlenecks
- Board-level visibility into AI risk posture, refreshed quarterly
- Defensible position for MAS, RBI, and EU AI Act conformity assessments
- 01
Diagnose
AI inventory, regulatory exposure scan, and gap analysis against MAS FEAT, EU AI Act, and your sector regulator. Output: prioritised governance roadmap.
- 02
Design
Target operating model, RACI, policy library, model risk taxonomy, and control catalogue. Reviewed by your legal, risk, and audit functions.
- 03
Deploy
Implement controls in your MLOps and LLMOps stack: model registry, evaluation gates, drift monitoring, red-teaming, audit logging.
- 04
Sustain
Quarterly assurance reviews, regulator engagement support, and incident response readiness. Handover to your internal AI governance lead.
Pillar 02
Generative AI and LLM Consulting
Most enterprise generative AI initiatives stall between proof of concept and production. The reasons are predictable: unclear use case economics, weak retrieval architecture, no evaluation harness, no governance, and no path to scale. Magnis AI takes generative AI projects from ambiguous boardroom mandates to production systems with documented ROI, regulator-ready controls, and an operating model your engineers can sustain. Vendor agnostic across OpenAI, Anthropic, Google, AWS Bedrock, Azure OpenAI, and open-weight models such as Llama, Mistral, and Qwen.
LLM Strategy and Use Case Portfolio
Identify, prioritise, and size generative AI use cases by value, feasibility, and risk. Output: a costed 12-month portfolio with build versus buy decisions for every initiative.
Retrieval-Augmented Generation (RAG)
Production RAG architecture covering chunking strategy, hybrid search, reranking, caching, and citation. Built for accuracy, latency, and cost at enterprise scale.
Agentic AI Workflows
Multi-step agent systems with tool use, memory, planning, and human-in-the-loop checkpoints. Designed for auditability rather than autonomy theatre.
Fine-Tuning and Model Adaptation
LoRA, QLoRA, full fine-tuning, and continued pretraining decisions based on cost, latency, and data sensitivity. Includes training data curation and evaluation.
Evaluation and Guardrails
Offline and online evaluation harnesses, golden datasets, LLM-as-judge protocols, and runtime guardrails for safety, PII, and prompt injection.
LLMOps Platform Design
Reference architectures for prompt management, version control, A/B testing, observability, cost tracking, and incident response on Bedrock, Vertex, Azure, or open-source stacks.
Framework
The Magnis Generative AI Build Method
Five stages: Discover (use case shaping), Design (architecture and evaluation), Build (RAG, agents, fine-tunes), Govern (guardrails, monitoring, policy), and Scale (cost optimisation, multi-tenant rollout). Every stage produces artefacts your CIO, CISO, and CFO can review independently.
Outcomes
- Generative AI use cases shipped with documented business value
- Reduced hallucination through evaluation-driven engineering
- Lower inference cost via model routing and semantic caching
- Audit-ready governance evidence for every LLM in production
- Internal capability uplift, your engineers own the platform after handover
- 01
Use Case Shaping
Workshops with business owners to define problem, decision context, success metrics, and risk profile. Output: a prioritised backlog with measurable hypotheses.
- 02
Architecture and Evaluation
Reference architecture, model choice, retrieval design, latency budget, and an evaluation harness with golden datasets before a single line of production code.
- 03
Build and Govern
Iterative delivery in two-week sprints with guardrails, logging, and red-team review built in. Governance artefacts produced alongside code.
- 04
Scale
Cost and latency optimisation, prompt caching, semantic caching, model routing, multi-tenant isolation, and handover to your platform team.
Pillar 03
Intelligent Process Automation
Traditional RPA solved repetitive screen-based work but plateaued because it could not handle ambiguity. Intelligent automation closes that gap by combining RPA with intelligent document processing, conversational AI, computer vision, and agentic LLM workflows. Magnis AI helps enterprises identify the right automation opportunities, design end-to-end automated processes, and operate them with the governance regulated industries demand. We deliver outcomes across finance, operations, HR, customer service, and back-office functions.
Robotic Process Automation (RPA)
Attended and unattended bots on UiPath, Automation Anywhere, Power Automate, and open-source alternatives. Designed for resilience to UI changes and instrumented for governance.
Intelligent Document Processing (IDP)
Extraction from contracts, invoices, KYC documents, claims forms, and clinical notes using OCR, layout-aware models, and LLM verification. Accuracy SLAs you can defend in audit.
Conversational AI and Virtual Agents
Customer-facing and employee-facing assistants on Azure Bot, Google Dialogflow, Amazon Lex, or open-source LLM stacks. Designed for compliance, escalation, and measurement.
Process Mining and Discovery
Data-driven discovery of automation opportunities using Celonis, UiPath Process Mining, or open-source tools. Output: ranked backlog with sized business case for each candidate.
Agentic Automation Workflows
Multi-step LLM agents that orchestrate APIs, RPA bots, and human approvals to handle exceptions traditional automation cannot. Designed for auditability and rollback.
Automation Operating Model
Centre of Excellence design, governance, demand intake, prioritisation, and operations. Built so automation scales without becoming a cost centre.
Framework
The Magnis Automation Value Model
Three lenses applied to every automation candidate: Cost (FTE hours released, error reduction), Cycle Time (latency removed from the value stream), and Experience (customer and employee satisfaction lift). Initiatives that do not move at least two lenses are deprioritised.
Outcomes
- 30 to 70 percent cost reduction on automated processes
- Cycle time reduction from days to minutes for document-heavy workflows
- Error rate reduction with audit-trail evidence
- Higher customer and employee satisfaction scores
- Reusable automation components that compound value across the enterprise
- 01
Discover
Process mining, task mining, and stakeholder interviews to build a ranked automation portfolio. Output: 24-month roadmap with business case per initiative.
- 02
Design
End-to-end process redesign before automation. We automate the right process, not the broken one. Includes exception handling and audit design.
- 03
Build
RPA, IDP, conversational AI, and agentic components delivered in two-week sprints with governance artefacts produced alongside code.
- 04
Operate
Bot operations, monitoring, change management, and continuous improvement. Handover to your CoE or managed by Magnis as a service.
Regulatory coverage
The frameworks we align your AI to
We translate each framework into engineering controls, audit evidence, and operating model artefacts. No abstract principles, only implementable expectations.
MAS FEAT and Veritas
Fairness, Ethics, Accountability, Transparency for AI in financial services.
EU AI Act
High-risk classification, Articles 9 to 15 controls, conformity assessment.
NIST AI RMF
Govern, Map, Measure, Manage applied across the AI lifecycle.
ISO 42001
AI Management System certification and continuous improvement.
HIPAA and HITECH
PHI controls applied to training data, inference, and analytics.
GDPR and PDPA
Data protection, automated decision-making, and cross-border transfer.
RBI and SEBI
Reserve Bank of India model risk and SEBI algorithmic trading rules.
FDA GMLP
Good Machine Learning Practice for AI medical devices.
India DPDP Act 2023
Consent, purpose limitation, and significant data fiduciary obligations.
Methodology
One delivery method across every pillar
Diagnose, Design, Build and Govern, Sustain. Each phase produces specific artefacts for your CIO, CISO, CRO, and head of internal audit. No phase ends without independent review.
- 01
Diagnose
Regulatory exposure scan, AI inventory, capability gap analysis, and business outcome target. Output: prioritised roadmap with sized business case per initiative.
- 02
Design
Target operating model, reference architecture, controls catalogue, evaluation plan. Reviewed by your legal, risk, audit, and architecture functions.
- 03
Build and Govern
Iterative two-week sprints with guardrails, logging, and red-team review built in. Governance artefacts produced alongside code.
- 04
Sustain
Quarterly assurance reviews, regulator engagement support, incident response readiness, and capability transfer to your AI Centre of Excellence.
Outcomes
What you get from the integrated practice
Measurable outputs that pass internal review and external audit.
- Audit-ready evidence pack for every production AI system
- Generative AI use cases shipped with documented business value
- 30 to 70 percent cost reduction on automated processes
- Reduced model risk capital and lower regulatory remediation exposure
- Defensible position for MAS, RBI, EU AI Act, and sector regulator reviews
- Internal capability uplift, your team owns the platform after handover
Why Magnis AI
Built for the AI risk landscape regulators are creating right now
CISM and TOGAF certified leadership
Architects with deep security, enterprise architecture, and regulatory backgrounds. We speak the language of your CISO, CRO, and chief architect.
Governance-first delivery
Every engagement produces governance evidence alongside working code. No retrofit, no surprises at the model risk gate.
Vendor agnostic by design
We do not resell platforms. Recommendations cover hyperscalers, open source, and best-of-breed governance tools chosen on merit.
Outcomes you can measure
Every engagement carries a defined business outcome (cost, revenue, cycle time, risk reduction) and is reviewed quarterly against it.
Sector depth
Practitioners with hands-on experience in banking, healthcare, government, manufacturing, and retail. Recommendations are economically realistic.
Regulator-grade evidence
Documentation, validation studies, and audit packs prepared to the standard MAS, RBI, EU regulators, and FDA expect.
Industries we serve in this practice
- Banking and Financial Services
- Insurance
- Healthcare and Life Sciences
- Government and Public Sector
- Manufacturing
- Telecommunications
- Retail and Consumer Brands
Common questions about Enterprise AI Consulting
What does Magnis AI Enterprise AI Consulting actually cover?
Three connected pillars under one practice: AI Governance and Compliance, Generative AI and LLM Consulting, and Intelligent Process Automation. Each is a standalone engagement, and they sequence into a complete enterprise AI operating model. We also deliver enterprise AI architecture, independent risk and compliance assurance, and Centre of Excellence build-out.
What is governance-first AI?
Governance-first AI means treating risk, compliance, fairness, transparency, and accountability as design constraints from day one rather than as remediation after a proof of concept. Every Magnis AI engagement produces governance artefacts (model risk documentation, evaluation evidence, monitoring runbooks) alongside the working system so deployment is not blocked by audit.
Is Magnis AI vendor agnostic across foundation models and platforms?
Yes. We are vendor agnostic across OpenAI, Anthropic, Google Gemini, AWS Bedrock, Azure OpenAI, and open-weight models such as Llama, Mistral, and Qwen. Architecture choices are driven by your data sensitivity, latency, cost at scale, and regulator expectations, not by vendor relationships.
How does Magnis AI engage with enterprise clients?
A typical engagement starts with a 45-minute AI Risk Consultation followed by a four to six week diagnostic. From there we deliver in three to nine month phases with measurable outcomes per quarter, working alongside your internal teams to build long-term capability. Managed-service operation is available for organisations that prefer to outsource sustained governance and platform operations.
Can you train and uplift our internal team?
Yes. Capability transfer is a deliverable in every engagement. We pair with your engineers, data scientists, risk officers, and product owners so they can sustain governance, operate the platform, and ship new use cases after handover. We also run executive workshops and board briefings on AI risk and opportunity.
Is generative AI safe to deploy in regulated industries?
Yes, with the right controls. Generative AI in regulated industries requires explicit governance covering data lineage, prompt and output logging, evaluation, hallucination mitigation, guardrails, human oversight, and incident response. Magnis AI delivers all of this as part of every LLM build, with reference architectures designed to support MAS, RBI, EU AI Act, HIPAA, and GDPR conformity.
What is the difference between AI strategy and AI governance?
AI strategy defines what your organisation will do with AI: which use cases, in what order, with what business outcomes. AI governance defines how you will do it safely: policies, controls, evidence, and accountability. Both are needed. Strategy without governance produces unshippable proofs of concept. Governance without strategy produces overheads with no payoff.
Do you deliver work or just produce reports?
We deliver work. Architecture, code, evaluation harnesses, monitoring dashboards, policy documents, and audit evidence. Reports are by-products of delivery, not deliverables in themselves.
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