I help boards trust their AI — by governing the data that feeds it.

Problem

Enterprise AI initiatives were outpacing the controls around them. Delivery teams wanted to ship; risk teams wanted evidence; regulators wanted traceability. Internal teams needed a repeatable way to assess inherent AI risk, define mitigations, and produce assurance evidence without slowing delivery — and without a different answer for every client.

Approach

• Designed an enterprise AI assurance framework aligned to DAMA DMBOK, ISO 27001, and APRA CPS 234 & CPG 235 — with EU AI Act readiness mapped in for forward-compatibility.
• Built a Responsible AI control library: risk taxonomy, model classification, decision-point register, bias / fairness controls, human-in-the-loop checkpoints, and operational risk owners.
• Mapped regulatory obligations to AI lifecycle stages, controls, and accountability models in Collibra — every obligation tied to a control, every control tied to an owner.
• Stood up repeatable AI assessment, AI inventory, and approved-use-case workflows, embedded into ADMP delivery rituals so assurance was a pipeline step, not an afterthought.
• Coordinated third-party AI due diligence — technical docs, control environments, testing evidence, contract obligations, and ongoing vendor risk reviews.
• Trained delivery leads on "assurance-by-design" — what to capture, when, and how to evidence it without blocking sprints.

Outcomes

• Improved regulator and internal-risk confidence through repeatable assurance artefacts that auditors could trace end-to-end.
• Enabled secure, auditable migrations for Australian Retirement Trust and Challenger.
• Reduced data and AI risk during legacy decommissioning and cloud migration.
• Cut time-to-assurance from weeks of bespoke work to a templated 3–5 day flow per use case.
• Created reusable patterns that other Accenture delivery teams now apply across regulated AU clients.

Problem

Ziko was layering AI features onto cloud data without governance scaffolding. Data ownership, sensitivity, lineage, and access patterns were largely tribal knowledge — a regulator and customer trust risk as AI usage grew.

Approach

• Conducted AI and data governance assessment for the Azure migration.
• Established Data Owners, Stewards, governance forums, decision rights, and escalation paths.
• Implemented MS Purview classification, sensitivity labels, and data estate map.
• Aligned Purview policies with Azure security, access management, and audit requirements.

Outcomes

• Delivered a governed, AI-ready cloud platform with scalable compliance and security.
• Strengthened trust in AI-driven outputs through structured governance.
• Improved traceability of data inputs feeding AI models inside Ziko.

Problem

Long-running ETL was blocking same-day reporting; on-prem footprint was expensive; planned migration timelines were aggressive and audit-sensitive.

Approach

• Re-architected ETL with quality gates, lineage capture, and reusable patterns.
• Embedded governance into the Snowflake migration — access, classification, masking, and review cadence.
• Stood up risk-aware delivery rituals across onshore/offshore teams up to 23 developers.

Outcomes

• ETL processing time reduced from 8 hours to 28 minutes — same-day data availability.
• Snowflake migration completed in <25% of planned time, saving approximately $1.5M.
• Improved client confidence in AI-driven outputs through governance traceability.

Problem

Core banking migrations are unforgiving: data quality, secure handling, and audit traceability all need to land cleanly the first time, with members and regulators watching.

Approach

• Implemented data management policies aligned to regulatory and audit expectations.
• Built evidence packs and traceability supporting audit readiness.
• Introduced operating disciplines — punctuality, cadence, escalation paths.

Outcomes

• Digitisation and storage savings exceeded $300k.
• Operational discipline and punctuality lifted from 48.3% to 92.5% in three months.
• Built a collaborative, risk-aware culture across business and technology.

Problem

Selling a regulated wealth division means separating customer, investment, and insurance data across seven core applications under strict regulator and contractual deadlines — with zero tolerance for leakage between buyers. The data that landed with each buyer had to be clean, complete, evidenced, and legally defensible. Failures here mean APRA conversations, broken contractual warranties, and personal-data breaches.

Approach

• Planned, mapped, and resolved data issues across seven core applications spanning customer, policy, investment, and claims domains.
• Designed BI and data architecture supporting the split and downstream compliance for both buyers.
• Coordinated onshore/offshore teams across multiple time zones and embedded risk management and governance rituals.
• Built field-level mappings with provenance, ownership, and SFT-compliant evidence trails.
• Worked through politically sensitive divestment dynamics — keeping technical teams focused while the commercial deal moved.
• Defined cut-over and run-back procedures so a failed transfer never left customers without a system of record.

Outcomes

• Successful Successor Fund Transfer (SFT) and Project EDISON delivery — on time, on regulator schedule.
• Clean data separation across seven applications under tight, audited deadlines.
• Stakeholder engagement maintained throughout a politically sensitive divestment.
• Both buyers (Zurich and Insignia) onboarded data with documented integrity and no post-handover material defects.
• Established a divestment-data playbook reused inside ANZ’s wider transformation programs.

Problem

Each client had different risk frameworks, regulators, and AI maturity — but all needed structured governance to defend AI-driven outputs to internal audit, board, and external regulators.

Approach

• Mapped each client’s obligations to data assets, analytics outputs, and operational controls in Collibra.
• Delivered data quality remediation, metadata management, lineage, and secure ETL pipelines.
• Built audit-ready governance artefacts supporting data quality, AI usage, and risk controls.

Outcomes

• Improved client confidence in AI-driven solutions through compliance traceability.
• Embedded structure, accountability, and governance across teams of up to 23 developers.
• Built a reusable assurance pattern across predictive analytics, BI, and automation programs.

Problem

Reporting was unreliable, data standards were inconsistent across domains, and ETL pipelines were hard to monitor — limiting trust in enterprise reporting and downstream digital initiatives.

Approach

• Implemented structured data quality, metadata, and secure handling processes across business units.
• Improved ETL pipelines and monitoring for stable business reporting operations.
• Worked across stakeholders to support Toyota’s digital transformation.

Outcomes

• Enhanced governance maturity and consistent data standards across finance, sales, and customer domains.
• Stabilised business reporting and improved data transparency.
• Enabled cohesive, high-performing teams across onshore and offshore contributors.

Problem

The client had policies, an audit committee, and a growing AI inventory — but no single place to answer the question regulators actually ask: "Show me the obligation, the control that implements it, the owner who runs it, and the evidence it works." Compliance was a quarterly hunt across SharePoint, JIRA, and a dozen control owners.

Approach

• Modelled the regulatory obligation library inside Collibra — APRA CPS 234 & CPG 235, ISO 27001, internal risk taxonomy — at the clause level, not just the framework level.
• Mapped each obligation to controls, owners, evidence sources, and the AI / data assets it governs — with Collibra communities aligned to business domains, not IT silos.
• Built lineage and asset relationships so an AI use case shows the obligations it must satisfy, the data assets it consumes, and the control evidence supporting it.
• Defined a quarterly attestation cycle inside Collibra workflows so control owners certify evidence rather than email it.
• Trained domain stewards in how to maintain their own governance artefacts — moving from central admin overhead to federated ownership.

Outcomes

• Single defensible source of truth for AI and data governance evidence — auditors and regulators can self-serve.
• Eliminated quarterly evidence-hunt overhead — replaced with attestation workflows.
• Shortened time-to-answer on regulator queries from weeks to days.
• Created a federated stewardship model that scales with the AI inventory.

Problem

The insurer was adopting AI in claims and underwriting fast, but its data estate was a patchwork: Azure SQL, on-prem SQL, Snowflake, Power BI semantic models. Nobody could answer "where is PII in our AI training data, and who owns it?" in under a week. APRA expected better.

Approach

• Designed the Purview rollout sequence — Azure native first, then Snowflake via the multi-cloud connector, then Power BI semantic-model registration.
• Built the classification taxonomy: PII, sensitive PII, health, financial, customer-identifiable, model training, model output — mapped to APRA CPS 234 & Privacy Act obligations.
• Implemented automated sensitivity labelling on Azure-hosted data with policies that propagate to downstream BI assets.
• Established a Data Owner / Steward operating model aligned to insurance business domains (claims, underwriting, customer, finance).
• Integrated Purview outputs into the AI assurance workflow — so model documentation references real-time classifications of training inputs.

Outcomes

• "Where is PII in our AI training data?" became a 30-second query.
• Audit-ready evidence pack for APRA CPS 234 and Privacy Act obligations — auto-refreshed.
• Surfaced previously undetected sensitive-data flows into Power BI dashboards — closed before regulator review.
• Reduced AI assurance turnaround time by integrating live classifications into model documentation.

Problem

Small Sydney businesses needed real websites — not template builders — but couldn’t justify $25k–$60k agency fees. Most ended up with generic Wix sites that didn’t convert. They needed bespoke design, governed copy, working forms, and SEO — without the agency price tag.

Approach

• Used AI design and code generation tools end-to-end — discovery, sitemap, copy, layout, code — with a human-in-the-loop for brand voice, accessibility, and accuracy.
• Applied a governance-by-design lens even at SMB scale: privacy notice, accessibility checks, form-data handling, basic SEO, and analytics consent.
• Built reusable templates and component libraries so subsequent client builds got faster without losing bespoke feel.
• Embedded contact forms, booking flows, and Stripe / payments where relevant — wired to authenticated email so leads landed reliably.

Outcomes

• Delivered consultancy-grade websites at small-business price points and turnaround times.
• Multiple Sydney SMB clients launched within weeks rather than months.
• Demonstrated, in production, what AI-assisted delivery actually looks like — the same patterns I bring to enterprise clients.
• Proof point: governance-by-design scales down, not just up.

Problem

Enterprises adopting LLMs face a real-time control gap: prompts flow through to AI vendors carrying PII, source code, financial data, and regulated content — with no enforcement layer in between. Existing tooling either blocks LLMs entirely or trusts vendor-side controls that no auditor can defend.

Approach

• Engineered risk controls to dynamically intercept, audit, and redact PII (data privacy protection) and block restricted enterprise topics prior to LLM processing.
• Built a three-layer architecture: regex / pattern scanner for PII masking, prohibited-topic evaluator for sensitive business content, and a secured-API call layer with rate limiting and logging.
• Established a compliance-logging framework producing real-time, tamper-evident audit trails detailing security classifications and programmatic intervention records.
• Mapped each control to NIST AI RMF functions and ISO 42001 clauses so the gateway is regulator-defensible out of the box.
• Published as open-source so other governance teams can fork, adapt, and audit the controls themselves.

Outcomes

• Working reference implementation regulators and engineering teams can both read — not a slide deck, code.
• PII never leaves the boundary unmasked: SSN, credit cards, emails are redacted before any vendor call.
• Restricted topics (source code, financial models, customer databases) blocked at ingestion — no audit-after-the-fact required.
• Every prompt logged with timestamp, user, data classification — full audit trail for regulator review.

Problem

Small and medium businesses see Big-4 AI consulting prices and assume AI transformation isn’t for them. Off-the-shelf SaaS tools cost more than the staff they’re meant to augment. The market needed a defensible, governance-aware delivery framework that scales down — not up.

Approach

• Built a structured catalog of AI automation solutions spanning document extraction, customer service, intelligent email, data quality, workflow orchestration, accounts receivable, and natural-language task capture.
• Engineered zero-enterprise-fee tech stacks — n8n Community Edition self-hosted, open-source LLMs, free-tier APIs, low-cost VPS — with monthly running cost typically under USD $35.
• Embedded local data sovereignty, automated compliance auditing, and proactive risk mitigation into every reference architecture.
• Productised five industry verticals (Retail/Ecommerce, Manufacturing, Wholesale/Distribution, Property Management, Taxation/Accounting) with vertical-specific use cases and ROI patterns.
• Defined four delivery models — consulting, implementation, SaaS/white-label, marketplace — with transparent pricing bands.

Outcomes

• Scalable AI transformation pattern delivering 30–60% reduction in manual labour time, 40–70% cost savings vs. additional hires, faster process cycles (invoice processing 3 days → 2 hours, customer enquiries 24h → 5min).
• 60–80% deflection of standard support tickets via intelligent chatbots.
• 95%+ data completeness through dedup and enrichment patterns (from ~60% typical SMB baseline).
• Governance-by-design preserved at SMB scale — privacy notices, accessibility, audit trails, role-based access — not stripped out for cost.

Problem

The business owner was losing 25–30 hours per week to inbox triage, drafting replies, daily agenda assembly, task delegation across the 45-person team, customer follow-ups, and report generation. Lapsed customers were going un-followed. Growth was bottlenecked on the owner’s personal bandwidth, not on demand.

Approach

• Designed a three-layer stack — Claude Pro (Microsoft 365 connector for read-only Outlook/SharePoint/Teams access) + self-hosted n8n on the existing Hostinger VPS + Make.com Core + Raycast — at a total running cost of ₹2,618 / ~USD $28 per month. No Microsoft Copilot, no Power Automate, no Big-4 lock-in.
• Built seven n8n workflows: daily morning briefing, VIP-customer email categoriser, auto-archive (SharePoint, replacing PST), email-to-task delegation with assignee parsing, weekly manufacturing report, 5-day customer follow-up reminder, and product-offer broadcast.
• Integrated ERPNext via REST API for customer-email-to-CRM-Lead, manufacturing-log-to-Work-Order, and invoice-email-to-Sales-Invoice flows — all data resident on the owner’s Indian VPS.
• Engineered Microsoft 365 email triage and task delegation workflows — recovering 50% of the owner’s operational time and shifting leadership focus toward strategic growth.
• Drove a 15% increase in first-month sales by designing and deploying automated re-engagement pipelines that reactivated lapsed and single-purchase B2B customer segments via merged ERPNext + Outlook intelligence.
• Framed headcount restructure per India DPDP / labour-law guidance as workforce reallocation to higher-value customer-facing roles — defensible at audit and HR review.

Outcomes

• Workforce restructured from 45 to 28 staff cleanly, with documentation/reporting roles absorbed by automation; remaining team reallocated to growth-facing work.
• Owner’s operational time recovered ~50% — from 25–30 hrs/week of inbox + delegation work to under 15 minutes of morning triage.
• First-month sales lifted 15% via lapsed-customer re-engagement automation.
• Annual stack cost ~₹31,418 vs. ~₹52L annual value recovered (owner time + reallocated FTEs) — payback in days, not months.
• 12-week phased rollout with documented risk register and DPDP-aware data classification rules — production-ready, audit-defensible.

Problem

Boards are asking for AI governance evidence. Risk teams want a defensible control library. Engineering wants pragmatic LLM gateway specs. Each audience needs a different artefact — but they all need to plug into the same regulatory backbone. No existing framework spans all three audiences.

Approach

• Authored a production-grade Enterprise AI Governance Playbook covering AI System Registry (to eliminate shadow AI), multi-tier risk classification (Prohibited / High / Limited / Minimal), 3-layer LLM gateway architecture (PII redaction → topic blocking → secured API), audit trails, vendor monitoring, retention rules, and incident response.
• Designed a multi-sector AI Governance Audit Matrix featuring 110+ scored controls across six core sectors — Banking (APRA CPS 234), Insurance (Solvency II), Manufacturing (ISO 9001), Automobile (SOTIF), Telecom (GDPR), Utility (NERC) — enabling 2–4 hour governance maturity assessments.
• Mapped every control to international regulations: APRA CPS 234, Solvency II, GDPR, SOTIF, NIST AI RMF, ISO 42001 — so the same assessment defends multiple regulator conversations.
• Bridged technical engineering and corporate compliance with automated data lineage controls, metadata mapping, and auditable metrics.
• Published the framework on GitHub under MIT (assessment) + reserved (playbook) so other organisations can adopt, fork, and extend.

Outcomes

• Production-grade framework deployable in 30 days (registry + risk tiers) / 90 days (LLM gateway + audit trails) / 6 months (board dashboard).
• Single dual-layer governance solution serving CIOs, security & compliance, engineering, and audit/risk — eliminates the artefact-per-audience tax.
• Audit-ready evidence package for APRA, GDPR, NIST AI RMF, ISO 42001 examinations.
• Open-source matrix already covering six sectors — extensible to additional verticals by community contribution.