Case Studies
Real engagement patterns.
Illustrative case studies drawn from real project categories and recurring problem types. No fabricated client names, no invented ROI figures.
From scattered AI experiments to unified agent platform
Context
Mid-size SaaS company. Engineering team of 12. Six separate AI POCs running in parallel, none connected, none shipped.
Problem
Each team had built their own AI integration independently. Different models, different prompt strategies, different data access patterns. No shared knowledge layer, no agent schema, no way to compose across use cases.
Approach
- →Audited all six POCs for architectural patterns and failure modes
- →Designed unified agent schema and tool registry
- →Defined shared knowledge model with topic ontology
- →Built one reference implementation of the new architecture
- →Documented migration path for each existing POC
Outcome
Architecture specification and reference implementation delivered. Team used the reference to consolidate remaining POCs over 6 weeks.
This describes a pattern across multiple engagements, not a single client.
Quantitative model audit: backtest that didn't survive walk-forward validation
Context
Proprietary trading research team. Python-based backtest showing strong theoretical returns. Ready to move to paper trading.
Problem
The backtest had been developed iteratively with parameter optimization. Suspected lookahead bias in the feature engineering step. Cost assumptions hadn't been validated against real execution constraints.
Approach
- →Full audit of feature engineering pipeline for lookahead contamination
- →Identified three instances of forward-looking calculation in indicator construction
- →Rebuilt cost model with realistic transaction cost assumptions
- →Ran walk-forward validation across three non-overlapping periods
- →Documented which regime the strategy actually works in
Outcome
Walk-forward validation confirmed the strategy was profitable in one of three regimes. Lookahead fixes reduced theoretical Sharpe but match paper results. Team continued with paper testing.
This describes a common pattern in quant model reviews. No client data referenced.
AI readiness assessment before a $2M vendor commitment
Context
Professional services firm, 200 employees. Leadership evaluating an enterprise AI platform contract. Pre-purchase assessment requested.
Problem
Vendor promised significant productivity improvements. The firm's internal team lacked the architecture context to evaluate whether the claims were realistic given their data infrastructure.
Approach
- →Data readiness audit (structure, accessibility, governance)
- →Technical integration feasibility review
- →Realistic outcome range estimate based on actual data state
- →Build vs buy analysis for the core use cases
- →Go/no-go recommendation with reasoning
Outcome
Recommendation: proceed with a 3-month pilot of one use case rather than full platform commitment. Specific integration constraints documented.
Pattern drawn from multiple advisory engagements. No client names used.
Founder needed a working AI prototype for investor demo — 72 hours
Context
Solo technical founder. Pre-seed. Investor meeting in 10 days. Had a concept but no architecture and no implementation.
Problem
Founder had been spending nights building disconnected pieces. No coherent architecture, no working demo, no way to show the system as a whole.
Approach
- →Day 1: Architecture session — defined agent schema, knowledge model, user flow
- →Day 2: Core build — working prototype with real data, not mock data
- →Day 3: Documentation, repository cleanup, investor-facing README
- →Handoff: live walkthrough + full source code transfer
Outcome
Working prototype delivered by Monday. Git repository transferred. Founder ran the investor demo without assistance.
This is the standard Weekend Sprint engagement pattern.
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