Guides
Step-by-step playbooks for the moments that actually trip up fintech teams — a zero-PII demo environment, a rebalancing engine under stress, fair-lending compliance before an examiner asks.
Generating Reg BI Test Data: A Methodology for Compliance Teams
The fact patterns Reg BI examiners actually cite — concentrated senior holdings, cognitive-decline markers, recent inheritances into illiquid products — barely exist in most firms' books. Build a corpus that does.
How to Evaluate AI Agent Memory: A Step-by-Step Method
Four steps turn 'the agent seems to remember things' into a number: a corpus with a real answer key, an adapter that never sees the answers, blind QA, and two-axis scoring against a baseline instead of in isolation.
Why AI Agents Forget Decisions — and How to Measure Exactly What's Lost
An agent's memory system doesn't lose decisions because the information vanished — it loses them because extraction, summarization, and graphing are compression, and compression is lossy. The gap to a verbatim baseline is exactly how much, and it's measurable.
Cold-Starting an Advisor AI: Giving a New Agent a Memory of Decisions It Never Made
A new advisor agent starts with an empty memory, and its first real questions are exactly the ones an empty memory can't answer: what have we recommended before, and why. Seeding closes that gap before day one — if the seed content is shaped like the questions that will be asked.
How to Test an AI Advisor Without Touching Production Client Data
Every test fixture built from real client records inherits that record's compliance obligations — which means most advisor-AI test environments are quietly carrying the same governance burden as production. Testing against a synthetic household-plus-decision corpus removes that burden by construction.
Measuring Memory Compression Loss: A Practical Method
Compression loss isn't a vibe — it's a number you get by running a verbatim baseline and your real system over the same corpus, diffing per task type, and checking whether each gap traces to retrieval or to what got extracted.
Retrieval Granularity: Why Passages, Facts, and Graph Edges Answer Different Questions
A memory system's retrieval unit — a whole passage, a single fact, or one graph edge — isn't an implementation detail. It's the reason the same architecture can win decisively on one question type and lose badly on the next.
Validating LLM Answers Without Ground Truth: Cross-Model Disagreement as a Health Signal
In production, there's no answer key to check against. DecisionSynth Bench's cross-provider validator has a different model family audit every answer against only what the system actually retrieved — and its disagreement rate tracks real exact-match scores closely enough to trust as an unsupervised health check.
The Adapter Pattern for Memory Evaluation: One Contract, Any Vendor
Comparing memory vendors fairly means every system answers through the identical narrow contract — ingest once, then answer blind, with the runner stripping ground truth before a task ever reaches the code under test. Here's how to build one.
Known-Answer Evaluation: Why Generated Ground Truth Beats Labeled Ground Truth
Hand-labeling ground truth means a person decides, after the fact, what the correct answer is. Generating it means the correct answer is whatever a deterministic process computed — removing an entire error-prone step, in the specific domains where that's possible.
Decision Audit Trails for AI Advisors: What Reg BI-Era Compliance Will Ask Your Agent
If an AI agent acts on a remembered decision, the firm has to be able to reconstruct why — the same standard a human advisor's recommendation is already held to. An agent whose memory can't produce that reconstruction has a compliance gap, not just a UX one.
How to Backtest a Tax-Loss Harvesting Algorithm with Synthetic Households
TLH algorithms that skip lot-level cost basis, wash-sale conflicts, and QSBS holding periods look fine in aggregate and wrong underneath. Backtest against a deterministic corpus so comparisons are reproducible.
Building a Retirement Income Sequencing Engine: Design Principles and Test Methodology
Optimize Social Security, then Roth conversions, then IRMAA in sequence, and the conversions quietly push the household across a tier already locked in. Solve all six decumulation decisions as one integrated problem.
Playbook: Migrating Non-Production Environments from Production Data to Synthetic
Retiring sanitized prod copies from dev and staging isn't a flip — it's parallel validation in a shadow environment, then cutover with a rollback gate for tests that quietly grew to depend on real-data idiosyncrasies.
Playbook: Building a No-PII Demo Environment for Sales Engineering
A sales demo has to look production-ready without ever touching production data. Here's the architecture — isolated environment, per-prospect personalization, nightly resets — that makes 'fully synthetic, zero leak risk' true by construction, not by policy.
Playbook: Stress-Testing a Rebalancing Engine Without Real Client Data
'Produces a reasonable trade list for one household' is too low a bar — per-account logic that passes it still produces undoing trades the moment a household has multiple accounts. Stress-test before you ship, not after.
Playbook: Load-Testing Wealth-Tech APIs at 10,000 Synthetic Households
A load test that stays flat against a small fixture at modest concurrency doesn't prove production-readiness — it misses hot-account contention, query-plan degradation, and the burst traffic that causes incidents.
Playbook: Fair-Lending (Reg B / ECOA) Model Validation Using Synthetic Households
Fair-lending exams test adverse-action explanations and four-fifths-rule disparities case by case. Synthetic households make matched-pair testing possible — same profile, different protected class — real data can't.
Playbook: Executing SR 11-7 Model Validation Using Synthetic Data
If your validation team tests against the same data the development team trained on, validation only catches what dev's data already missed. Run SR 11-7's three pillars on an independent synthetic corpus instead.
Playbook: Onboarding Your First Fintech Customer on a Synthetic-Data Corpus
Buying the corpus is the easy part — skip onboarding and it drifts to 'installed but unused.' Schema mapping, integration tests, security prep, and a named champion per team make it a working asset instead of shelfware.
Playbook: Quarterly Compliance Dry-Run on a Synthetic Customer Population
The cheapest place to find a compliance program gap is your own dry-run against a synthetic population, not a regulator exam or a customer complaint. Run one against your supervisory engine, on your timing.