AI-first GCC
Fairness frameworks, explainability, EU AI Act and DPDPA alignment, risk classification, monitoring, and policy design to ensure your AI systems are trusted, auditable, and regulator-ready.
EU AI Act
obligations mapped
DPDPA
India aligned
4 risk tiers
use-case classification
100%
models documented
The regulatory landscape changed in 2024-25. The EU AI Act phased into force, India operationalised DPDPA, sector regulators issued AI-specific guidance, and major enterprises faced public failures of biased or unsafe AI systems. The cost of getting AI governance wrong is now reputational, regulatory, and financial.
Responsible AI is not just compliance. It is the operating layer that makes AI deployable. Without fairness testing, explainability, monitoring, and human oversight, business units will not greenlight launches and legal will not sign off. With them, AI moves faster, not slower.
NeoIntelli helps enterprises design and operate responsible AI as a system - use-case classification, approval workflows, model documentation, evaluation, monitoring, and audit - integrated into the AI delivery lifecycle, not bolted on at the end.
Deliverables
01
Define and measure fairness criteria across models, datasets, and outcomes - with statistical metrics, disaggregated evaluation, and mitigation playbooks.
02
Implement interpretability tools, model cards, and stakeholder-appropriate explanation interfaces for both classical ML and LLM-based systems.
03
Map AI systems to EU AI Act risk tiers, DPDPA obligations, sectoral regulation (BFSI, healthcare), and ISO/IEC 42001 AI management standard.
04
Tier AI systems by risk, define approval requirements, controls, and oversight depth proportionate to each tier.
05
Create AI policies, review boards, escalation paths, incident response procedures, and the governance operating cadence.
06
Automate model documentation, evaluation evidence, approval trails, and monitoring artefacts to make every AI system audit-ready.
01
Inventory AI systems, map regulatory exposure, audit current controls, and benchmark against EU AI Act, DPDPA, and ISO/IEC 42001.
02
Build the risk classification, control catalog, policy library, approval workflows, and governance operating model.
03
Integrate controls into the AI delivery lifecycle - use-case intake, model registry, MLOps pipelines, and monitoring.
04
Run continuous governance - approvals, monitoring, incident response, regulatory horizon scanning, and board reporting on cadence.
Quarterly review boards cannot govern weekly model releases. Governance has to live in the pipeline.
Bias appears as data drifts. Fairness has to be measured continuously, not at launch only.
Third-party AI inside enterprise systems carries the same risk. Vendor AI inventory and assessment are mandatory.
Hallucination, prompt injection, and data leakage need GenAI-specific controls beyond classical model governance.
Model cards written once and never updated fail audits and undermine trust.
AI failures will happen. Without a tested response plan, they escalate into reputational events.
Every production AI system risk-classified and documented
Fairness and quality evaluation enforced in every release
EU AI Act obligations met for high-risk systems
DPDPA and sector regulation continuously aligned
Vendor AI inventory current and risk-scored
Zero unmitigated incidents involving regulated outcomes
No. It covers fairness, transparency, safety, robustness, and trust - all essential for adoption and long-term value. Compliance is one outcome of doing the others well.
Yes. We classify AI systems by risk tier (prohibited, high-risk, limited-risk, minimal-risk), map obligations, implement required documentation, conformity assessment readiness, and post-market monitoring.
India's DPDPA 2023 governs how personal data is processed, including by AI systems. We help design consent, notice, purpose limitation, rights management, and cross-border transfer controls that align AI use cases with DPDPA.
Using statistical fairness metrics (demographic parity, equal opportunity, calibration), disaggregated evaluation across subgroups, and domain-specific criteria defined with business and legal stakeholders.
Not when embedded into the lifecycle. Tier-appropriate controls actually speed up launches by removing late-stage legal blockers and rework. Late governance is what slows delivery.
Hallucination, prompt injection, data leakage, copyright exposure, and toxic output need controls beyond classical model governance - structured outputs, guardrails, evaluation, red-teaming, and monitoring.
Yes. We run workshops on responsible AI practices, bias awareness, EU AI Act obligations, DPDPA, and governance processes for technical, product, legal, and executive audiences.
Yes. For enterprises with limited internal capacity, we can operate the governance layer - reviews, documentation, monitoring, reporting - while transferring capability over time.
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