01
Market Data Parity Review
Find mismatched coverage, definitions, sessions and multi-leg records before a migration becomes a false equivalence.
Explore the reviewQuant Data & AI Reliability Consultant
Make market data and AI research defensible.
Independent technical review for market-data pipelines, backtests and AI-assisted financial research.
Source
Checks
Decision
Evidence, boundaries and decisions remain traceable.
What changes
Before important outputs reach production, clients or investment decisions.
Small fintech, quant and trading-analytics teams often have a working system and an unresolved question: can its data, history and automated reasoning be trusted under pressure? The practice makes the answer inspectable.
Focused reviews
Three places silent failures hide.
02
Backtest & Research Integrity Review
Test whether history, assumptions and execution rules were available at the moment a result claims they were.
Explore the review03
Financial AI Evaluation Sprint
Test whether an AI-assisted workflow remains numerically correct, time-aware and supported by a traceable evidence record across real edge cases.
Explore the reviewWorking principle
A defect map, not a confidence ritual.
Every engagement begins with a written scope, required inputs, deliverables and a fixed quote. The goal is a usable decision record: what is sound, what is uncertain, and what must change before the next release.
Common failure modes
Problems that survive a quick demo.
Correct rows, wrong population
Coverage gaps and changing definitions can create a clean-looking but incomparable dataset.
History that knew the future
Revised data, roll logic and timing mistakes can create backtest results that were never tradable.
Useful language, unsupported answer
AI systems can sound precise while missing the source, date, table or exception that changes the decision.
Evidence-led examples
Methods that hold uncertainty in view.
Independent research case
Vendor-feed parity
A comparison design that distinguishes agreement, unpaired records and unresolved semantics.
Read the methodTechnical research note
Multi-leg volume integrity
A control model for strategy records where missing legs remain visible rather than becoming invented flow.
Read the methodStart with the decision at risk
Bring the discrepancy, the data sources and the deadline.
The initial scope defines what can be tested, what evidence is needed and what a useful answer should contain.