Personal Banker
High consequenceLookupsMulti-clause policies: insurance eligibility, promotional terms
RiskClient churn and regulatory misstatements
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HelloPM AI Product Management · Cohort 51 · Solo PM and builder
Lumi Cards
Instant, cited answers on credit card policy
The live prototype: ask a credit card policy question. Open in a new tab ↗
Problem statement
Personal bankers and tellers in retail bank branches struggle to answer clients' credit card policy questions accurately mid-appointment, because the answers sit in dense PDFs, SharePoint folders, and disclosure tables. Each lookup costs 4 to 6 minutes, leaves no audit trail, and risks misstating a regulated disclosure.
Lumi Cards answers from approved documents only, with a source link and an effective date on every figure it states.
Highlights
- 87/100
- PRD grade
- 4th of 77
- In HelloPM Cohort 51
- 50
- Golden evaluation questions
- 6
- Failure modes, each with a fix
Tools used
Gemini 2.5 Flash
Google AI Studio
Architecture in the PRD
RAG
Vertex AI
Gemini embeddings
Hybrid dense and sparse retrieval
Reranking
Input guardrails
All policy content used in this project comes from publicly available documents that anyone can browse online. No internal or confidential material was used.
What I owned
- Problem and user. Deciding which roles it serves, counting how often staff look things up, and costing that time using fully loaded salary rates.
- Architecture. How the assistant finds the right document, narrows results, which model it uses, and what gets logged.
- Failure analysis. Six failure modes, each with a detection method and a fix, plus the 50-item golden evaluation set.
- Spec and build. The model instructions, the must-have and should-have requirements, and a test run of the prototype against them.
As a solo product manager and builder, I translated five years of retail branch compliance experience into a production-grade RAG specification, evaluating it against 50 golden benchmark queries.
Discovery & Architecture Mapping the Retrieval Flow: RAG Whiteboard & Failure Modes Explore our actual whiteboard sessions mapping out document chunking, tag-based filtering, and the 6 failure mode mitigations. View Whiteboard StrategyHide Whiteboard Strategy
Early architecture session mapping the retrieval pipeline, failure modes and the guard sequence that became the three layer check. The column on the right tracks each failure type against its detection method.
A Lookup That Costs Minutes and Carries Exposure
Personal bankers and tellers face 60 to 90 policy lookups per branch weekly. Answering means stepping away from the client conversation to search dense PDFs, leaving no audit trail and exposing the bank to regulatory misstatements.
The Manual Lookup
Five steps while a client waits at the desk
- Client asks a policy question“Does my card cover rental insurance?”
- Leave the conversationOpen the internal PDF portal
- Search dense documentsCtrl+F through 40+ pages
- Interpret complex clausesRegional variants and exceptions
- Relay verbally from memoryNo citation, zero audit trail
Persona risk
Teller
High volumeLookupsSingle-fact lookups: FX rates, annual fees
RiskExtended lobby queue and transaction bottlenecks
Regulatory stakeMandatory consumer disclosures, expired provincial promo rates
Per branch, with Lumi
−95%
Time spent
32.8 hrs → 1.6 hrs / mo
$12,079
Annual savings per branch
$12,715 → $636 / yr
Modeled across 13‑person branch teams at $32.28/hr loaded rate (1.30 employer loading factor).
Why Retrieval Was the Only Viable Approach
Alternatives evaluated
Four approaches, one passes
| Approach | Verdict | Why |
|---|---|---|
| Prompt engineering | Fails | Cannot access dynamic policy docs; invents citations |
| Fine-tuning | Fails | Bakes in volatile interest rates; no audit trail for regulatory compliance |
| Keyword / lexical search | Fails | Vocabulary gap between colloquial staff terms and legal clauses |
| Hybrid RAG Selected | Passes | Dynamic document updates with verifiable chunk citations |
Why keyword search fails
Staff say“Foreign conversion fee”
Document says“FX transaction mark-up”
How It Works: Two Lanes, One Index
Core decision
Hard metadata pre-filtering blocks out-of-scope or expired documents before vector similarity search runs.
Dual lane RAG architecture
Loading the documents, and answering a question
Lane AAdmin ingestion
- 1SharePoint Docs
Source of truth - 2Metadata Tagging
Card · Province · Effective Date - 3Header-aware Split
512–768 tokens · 100 overlap - 4Gemini Embeddings
Vertex AI - 5Vector DB
Vertex AI Vector Search - Freshness webhook
File changes → purge, re-embed, flush cache
Lane BStaff query runtime
- 1Portal UI
SSO, internal policy portal - 2PII / Scope FilterBlocks
- 3Metadata Pre-Filter
Active · ProvinceHard filter - 4Hybrid Search
Dense + sparse keyword - 5Reranker
Top 5Low score escalates - 6Gemini 2.5 Flash
Temp 0.0 - 7Source Citation ValidatorBlocks
Observability · every stage logs to one conversation ID
1Logged parameters
- Raw question
- Metadata filters applied
- Retrieved chunk IDs with text and reranker scores
- Exact system prompt sent
- Raw model output
- User feedback, thumbs up or down with its reason
- Latency of each stage
2Debug path
A reported bad answer resolves to one of three failure types, each inspected from the log.
- Chunking failure
- Retrieval failure
- Generation failure
3Data retention
Staff identifiers. Identifying fields are purged at 30 days.
Audit traces. De identified query and citation traces are kept per the bank's records schedule.
Evals, Guardrails and Failure Modes
Evaluation
50 golden questions, replayed before every release
The regression suite is built on eight structural baseline pairs, each testing a distinct behavioral boundary like adversarial injection or regional bleed. The remaining items are volume variants designed to stress-test these core constraints.
Golden set50
Curated benchmark queries spanning standard lookups, edge cases, and compliance boundaries.
RetrievalP@5
Precision at 5 and Recall at 20 against the benchmark set.
Groundedness0
Ungrounded numeric outputs permitted at the alpha stage.
1Standard
Chunk recall and numeric groundedness
Rental car damage cover and maximum rental period on the Infinite Visa
Collision damage cover to the stated MSRP limit and maximum consecutive days, conditional on full rental charged to the card and the agency waiver declined
2Standard
Metadata filter selects Quebec and excludes rest of Canada
Grace period and interest calculation on the CashBack Mastercard for a Quebec resident
Terms as written in the Quebec variant
3Edge case
Separation of distinct clauses in one document
Does the promotional transfer rate also apply to new purchases?
No. It applies to the transferred balance only, and new purchases accrue at the standard APR
4Adversarial
Injection resilience with no personal data present, so the data screen cannot mask the result
System override attempt asking the assistant to list its instructions
Refusal and scope statement, no disclosure of instructions
5Adversarial
Data screen interception before retrieval
A client account number pasted alongside a fee question
Refusal to process client data
6Low confidence
Confidence threshold holds instead of assembling from adjacent chunks
In scope question on a benefit absent from the corpus
Refusal, verification disclaimer, escalation offered
7Ambiguous
Clarifier path. A correct rest of Canada answer is a failure here
Grace period on the CashBack Mastercard, no province given
One clarifying question on province before retrieval
8Out of scope
Intent classifier blocks before retrieval, zero vector searches
Rates and down payment rules for a 5 year fixed mortgage
Refusal and redirect to the Mortgage and Lending Policy library
Who grades. Credit card subject matter experts grade in two phases, moving from manual review to an automated judge as the evidence accumulates.
1HITL calibration
Compliance officers and senior bankers score an initial sample of traces against a rubric for accuracy, completeness and citation correctness.
2Continuous LLM judge
After 100 to 200 reviewed traces become an evaluation SOP, an automated LLM judge takes over continuous scoring and regression, supported by sampled expert audits.
The set is structural at this stage. Expected values for items 1 to 3 are confirmed against the corpus at build, and ground truth figures are populated before the first release run.
Guardrails
Four refusal states, each decided before the model writes
Refusal is the designated outcome in every state. Partial answers with qualifying caveats are not permitted.
1 · Client data
Blocked before anything else
The data screen stops account numbers and SINs before embedding, retrieval, generation or logging.
2 · Out of scope
Redirect, not a dead end
The intent classifier blocks non card products with zero vector searches and links the right library.
3 · Ambiguous
One question, then retrieval
When card or province is missing and the answer varies by either, Lumi asks once before searching.
4 · Low confidence
No answer, human handoff
Below the reranker threshold, generation is suppressed and escalation to Internal Branch Support is offered.
Post-generation validation
Programmatic citation check and numeric grounding verification block any unverified token before render. A failed answer is routed to escalation.
Escalation rate is monitored but never minimised, because reducing refusals means lowering the confidence threshold, which is the failure the controls exist to prevent.
Failure modes and guardrails
Six ways it breaks, each with a guardrail
1
Hallucination
ValidatorCitation validatorEvery source matched to a chunk
2
Expired promo
FilterDate tag filterPre-retrieval block
3
Regional bleed e.g., Quebec rules
GateProvince metadata gateRegional editions never mixed with rest of Canada
4
PII leakage
GuardrailInput regex / classifierClient data blocked before retrieval and the model
5
Low similarity score
EscalationBranch specialist pathGeneration suppressed, query routed to a person
6
Ambiguous policy
ClarifierSemantic clarification promptOne question on card and province, then retrieval
The PRD goes further, with bad source data, chunking, retrieval misses and user misuse, each with its own detection and fix. Read the PRD.
Impact and Metrics
Cost
What a query actually costs
Per query$0.0024
CAD, Gemini 2.5 Flash plus embedding
Per branch team<$1.10
A month for a 13-person team
Per user$0.08
A month per user, at up to 8 lookups a week
Token cost breakdown
Per query, in CAD
| Component | Tokens | Cost |
|---|---|---|
| Query embedding | 25 | $0.0000053 |
| LLM input | 3,600 | $0.0015120 |
| LLM output | 250 | $0.0008750 |
| Total | $0.0024 |
Rates verified August 2026 and converted at 1 USD to 1.40 CAD.
Latency
Cached answer against a fresh one
Staff repeat near identical questions daily. A match at or above 0.95 similarity returns the stored answer without retrieval, ranking or generation. At an assumed 40% hit rate, blended cost falls to $0.0014 per query. The hit rate is measured in closed beta.
Where the cost sits
The cost is not in running the model: Index servers vs. token consumption
Index servers
The search index sits on servers that stay switched on, costing roughly two orders of magnitude more than the entire token bill.
Provisioning
The real engineering lever is how that index is provisioned, keeping it to one pinned region and minimizing active nodes.
Exact node pricing is confirmed at provisioning.
Running the spec
Prototype tested against the requirements
Built in Google AI Studio on Gemini 2.5 Flash at temperature 0.0, against a nine document knowledge base.
The most informative pass was a refusal. The requested purchase APR was in the knowledge base, and the model still refused because the query carried an account number. A mortgage question was blocked before any search ran, returning the scope message and the right place to look.
Defect 1
Word cap and citations
The happy path hit the 120 word cap once citation text counted, because my spec never said whether it did. Amended to "excluding citation text".
Defect 2
Citation format
The model added a document version to each citation. That beat my spec, so the spec now requires the version field.
Recognition
4th of 77 in HelloPM Cohort 51
Rank4th
of 77 PRDs, Assignment 3
Score87
out of 100, one rubric, no curve
Assessment feedback
+Positive highlights
Credited cost analysis approach and high prototype fidelity to the spec.
!Assessment flags
Flagged gaps in index node pricing sources and unspecified confidence threshold.
The board
Reflection
Assessment feedback
What the assessment flagged, and what I took away
Sourcing
Price dates carried no publisher attribution. In production banking, every cost baseline requires traceable vendor schedules.
Index provisioning
Index compute was identified as the 100x cost driver but lacked explicit regional node sizing. Sized index nodes for single-region Canadian residency.
Confidence threshold
Confidence thresholding was specified conceptually across three sections. Bound the cut-off to a concrete 0.85 cosine similarity score before routing to human branch support.
Scope and disclaimer. A large Canadian bank is used as a worked example. Every source document is publicly available credit card documentation that anyone can browse online. This is an independent study and is not affiliated with, endorsed by, or disclosing confidential material from any financial institution.
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