Build Production-Grade AI
That Scales
With controls, compliance, and security by design
Akshaya.io helps enterprises move from pilots to outcomes with an end-to-end approach: Core AI engineering + AI governance + GRC integration. We design and build AI platforms, copilots, agentic workflows, and decision intelligence—while implementing the policies, guardrails, monitoring, and auditability needed for regulated, high-stakes environments.
Build AI You Can Trust—At Enterprise Scale
Most organizations don't fail at AI because models are weak—they fail because production controls are missing: unclear ownership, unmanaged risk, security gaps, inconsistent data practices, and no audit-ready evidence.
Reliable
Measurable performance and quality
Secure
Protected against prompt injection, data leakage, and supply-chain risk
Compliant
Risk-managed, documented, auditable
Operational
Monitored, cost-controlled, continuously improved
We align programs to globally recognized frameworks such as NIST AI Risk Management Framework (AI RMF) and ISO/IEC 42001 AI management systems, and help enterprises operationalize compliance obligations (including EU AI Act requirements where applicable).
What We Deliver
Enterprise AI
Core AI + Platform Engineering
Production AI, not prototypes. We build and modernize AI capabilities that integrate with your architecture, data estate, and security model.
Enterprise AI Platform & Architecture
- AI reference architecture (cloud, hybrid, on-prem)
- LLMOps/MLOps foundations: model registry, evaluation pipelines, deployment patterns
- Observability: drift detection, quality monitoring, incident workflows
- FinOps for AI: usage controls, cost attribution, scaling strategy
GenAI and Agentic AI Solutions
- Enterprise copilots (internal knowledge, customer ops, developer productivity)
- Agentic workflows with human-in-the-loop approvals
- RAG/GraphRAG architectures with policy-aware retrieval and permissioning
- Multimodal pipelines (documents, images, audio) where business-relevant
Model Evaluation & Assurance
- Automated evaluation harnesses (accuracy, relevance, robustness, toxicity)
- Testing for regression, bias, and failure modes
- Red teaming and safety testing for real-world misuse scenarios
Governance AI
AI Governance + GRC + Controls
Governance AI means more than policy documents—it's a system that creates continuous compliance, control evidence, and accountable operations.
AI Governance Operating Model
- AI governance council design (roles, RACI, approval gates)
- AI policy framework: acceptable use, data handling, third-party/vendor standards
- Model lifecycle governance (build/buy, change mgmt, retirement)
AI Risk Management (NIST AI RMF)
- Risk taxonomy: harm, privacy, security, legal, operational, reputational
- Controls mapped to NIST AI RMF functions (Govern, Map, Measure, Manage)
- Model risk tiering (low/medium/high risk) and required evidence per tier
ISO/IEC 42001 Readiness
- Establish and implement an AI Management System (AIMS) approach
- Governance policies, risk assessment, lifecycle controls, supplier oversight
- Documentation packages and operating procedures for internal audits
EU AI Act Alignment
- Classification support (use-case risk profiling)
- Programmatic compliance workflows and evidence collection
- Timelines and obligations planning (GPAI + high-risk systems)
GRC Integration
- Integrate AI controls into existing GRC workflows (risk registers, control testing, audit management)
- Model inventory and "AI system cards" for traceability
- Third-party AI/vendor due diligence playbooks
Security for GenAI & LLM Applications
GenAI introduces new security risks—prompt injection, insecure output handling, data poisoning, model theft, sensitive info disclosure, and more.
Prompt Injection Defense
Input validation and prompt hardening techniques
Output Filtering
Secure tool-use boundaries and least-privilege for agents
RAG Security
Access control, data provenance, retrieval constraints
Threat Monitoring
Detect prompt attacks, leakage attempts, and suspicious behavior
Common Enterprise Outcomes
Faster Time-to-Value
Repeatable patterns for safe scaling beyond pilots
Reduced Risk Exposure
Defensible controls and measurable assurance
Audit Readiness
Consistent evidence, model documentation, decision logs
Lower Operational Cost
AI usage governance + FinOps controls
Higher Adoption
Policies + training that enable teams (not block them)
Deliverables You Can Expect
Strategy & Roadmap
- Enterprise AI roadmap (platform, use-cases, talent, operating model)
- Governance AI roadmap (policy, controls, GRC integration, assurance)
Build & Implement
- AI platform accelerators (LLMOps/MLOps templates, evaluation harnesses)
- Governance artifacts: policies, procedures, model cards, risk assessments
- Control mapping and evidence automation
Operate
- Monitoring dashboards and alerting
- AI incident response runbooks
- Continuous compliance reporting
Engagement Options
Enterprise AI + Governance Assessment
Capabilities, gaps, prioritized plan
Secure Copilot / Agentic AI Launchpad
Architecture + MVP + controls
AI Governance & GRC Implementation
Controls, evidence, workflows
ISO/IEC 42001 Readiness Program
AIMS design + operationalization
Frequently Asked Questions
What is AI governance and how is it different from Responsible AI?
AI governance is the operational framework of policies, controls, roles, and processes that ensure AI systems are developed and deployed responsibly at scale. Responsible AI is the set of ethical principles (fairness, transparency, accountability). Governance AI operationalizes those principles through measurable controls, audit evidence, and continuous compliance workflows.
How do you operationalize NIST AI RMF in an enterprise?
We help enterprises implement NIST AI RMF through its four core functions: Govern (establish accountability and policies), Map (identify AI risks in context), Measure (assess and track risks), and Manage (prioritize and respond to risks). This includes creating risk taxonomies, control mappings, model tiering frameworks, and continuous monitoring dashboards.
What is ISO/IEC 42001 and do we need it?
ISO/IEC 42001 is the international standard for AI Management Systems (AIMS). It provides a framework for organizations to manage AI responsibly throughout the lifecycle. If you operate in regulated industries, work with EU customers, or need third-party assurance of your AI practices, ISO 42001 certification demonstrates mature AI governance.
How does AI governance apply to GenAI copilots and agentic workflows?
GenAI copilots and agentic AI require additional governance controls: prompt injection defenses, output filtering, human-in-the-loop approvals for high-risk actions, RAG security with access controls, monitoring for misuse patterns, and clear escalation paths. We design governance frameworks specifically for these autonomous and semi-autonomous AI patterns.
What are the top security risks for LLM applications?
Key risks include prompt injection attacks, sensitive information disclosure, insecure output handling, data poisoning, model theft, excessive agency in agentic systems, and supply chain vulnerabilities. We implement defenses aligned with OWASP Top 10 for LLM Applications including input validation, output filtering, least-privilege architectures, and continuous monitoring.
How should we prepare for EU AI Act obligations?
Preparation involves: classifying your AI systems by risk tier, implementing required documentation and transparency measures, establishing conformity assessment processes for high-risk systems, ensuring GPAI model compliance if applicable, and building evidence collection workflows. We help organizations create compliance roadmaps aligned with EU AI Act timelines.
We deliver advanced AI engineering and AI governance—so your AI is secure, compliant, and scalable from day one. As a leading enterprise AI consulting and AI governance consulting partner, we specialize in governance AI, AI GRC, LLM security, and comprehensive LLMOps consulting and MLOps consulting services. Our solutions are aligned with NIST AI RMF, ISO 42001 AI management system standards, and EU AI Act compliance requirements. We help enterprises implement AI TRiSM frameworks, establish model registry and model inventory systems, achieve audit readiness, and deploy defenses including prompt injection prevention, RAG security, and agentic AI governance. Our expertise extends to drift detection, AI incident response, and sovereign AI implementations—aligned with leading GenAI security guidance including OWASP Top 10 for LLM applications.