Case Studies

What the Methodology Looks Like in Production

Three anonymized engagements where the Slavin AI methodology was applied end-to-end. Each case study reports what was built, what worked, what we would change, and the metrics that proved it.

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A Note on Anonymization

Why These Cases Are Anonymized

Each engagement below is a composite of real work - the architecture, controls, metrics and timeline are accurate; the client identifiers and some industry-specific details are anonymized to respect confidentiality. Named references are available under NDA at the Architecture Review stage.

Three Engagements

Healthcare, Financial Services, Manufacturing

Different industries, different regulatory regimes, same methodology. Each case study links to a detailed write-up.

Healthcare

HIPAA-compliant clinical knowledge RAG

A regional healthcare provider deployed a retrieval-augmented assistant for clinicians to query internal clinical guidelines, drug-interaction references and institutional protocols. The challenge was producing a system that clinicians would trust and use, under HIPAA constraints, without surfacing patient data into the LLM.

Outcome highlights: 42% reduction in time-to-answer for protocol queries, zero PHI leakage incidents, HIPAA audit passed on first attempt.

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Financial Services

Compliance Officer AI Assistant

A mid-tier asset manager deployed an AI assistant to support compliance officers reviewing client communications against an evolving regulatory framework. The challenge was high-stakes correctness, full audit trail per query, and tolerance for compliance officers who explicitly do not trust AI outputs by default.

Outcome highlights: review throughput up 3.1x, source attribution on every answer, escalation rate to senior compliance matching pre-AI baseline (i.e., the AI did not increase escalations - proof of consistency).

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Manufacturing

Document AI for Quality Assurance

A European industrial manufacturer deployed document-AI to extract structured data from supplier quality certificates, incoming-inspection reports and corrective-action documents. The challenge was extreme document heterogeneity (30+ supplier templates), tight ERP integration, and an in-house engineering team building the system itself.

Outcome highlights: 78% of supplier QA documents auto-processed end-to-end, manual review queue reduced to exceptions only, 8-month payback against the engagement cost.

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Common Threads

What the Three Engagements Share

Different industries; same recurring patterns in what determined success.

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Baseline Metric Before Build

Every engagement defined the baseline metric before architecture work began. No baseline = no ROI claim later.

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Governance In Design, Not Post-Hoc

Each system was risk-classified during Discovery and governance was designed in Phase 3 - not after a near-miss incident.

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RAG-First, Fine-Tuning Later (or Never)

All three started with retrieval-augmented generation grounded in client data. None of them needed fine-tuning to ship.

Underlying Framework

Methodology

The four-phase engagement model these case studies followed - Discovery, Architecture, Governance Design, Implementation Oversight.

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AI Governance

The maturity model, twelve-control baseline and risk taxonomy applied in every engagement.

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Enterprise AI FAQ

Fifteen decision-maker questions about enterprise AI - strategy, governance, risk, implementation, ROI.

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Your Engagement Will Become the Fourth

An Architecture Review takes two hours and clarifies whether and how your initiative becomes a future case study - or stops at Discovery, with budget intact.

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