Your customers' compliance teams, ops analysts, and underwriters are searching through PDFs that took someone a week to generate. Generic RAG implementations fail on financial documents in ways that aren't visible until a regulator asks why a citation pointed to the wrong line of a loan covenant.
Five specific failure modes we've seen across fintech RAG engagements. If you're trying to ship RAG on financial documents and any of these sounds familiar, you're not alone.
Default chunkers split documents on character count or paragraph breaks. Financial documents — balance sheets, transaction schedules, regulatory filings — have tables where context lives across multiple rows and columns. A naive chunk that contains rows 12–18 of a 50-row table is retrieved confidently, cited correctly, and is nearly always wrong.
The LLM cites a clause from page 12. The clause says what the user wanted to hear. But the clause that legally applies is on page 47, with a 'except where' qualifier the retrieval system never saw. Worse: there's no automatic way to know retrieval missed it.
All documents indexed in one vector store. The model retrieves whatever's most similar to the query — including documents the asking user shouldn't have access to. The compliance officer's reaction is predictable.
Loan covenants get amended. Transaction policies get versioned. Compliance memos get superseded. If the retrieval system isn't aware of version state, it confidently returns last quarter's policy to this quarter's question.
Without a golden-set eval framework, the system 'feels right' in QA and silently fails in production. By the time the customer reports a hallucinated citation, the team has shipped 47 more queries with the same root cause.
A production RAG pipeline designed for financial documents — chunking, embedding, retrieval, grounding, and evaluation, with audit trails at every layer.
Strategies tuned to the document type — tables stay intact, clauses keep their conditional context, multi-page contracts retain cross-references. We use a combination of structural parsing (PDF table detection, section hierarchy) and semantic boundary detection rather than naive character chunking.
Every document tracks its source version. When source documents update, embeddings update — or get marked stale, depending on policy. Retrieval queries respect version state: ask about Q4 policy, get Q4's embeddings; ask about current policy, get current.
Access controls applied at the retrieval layer, not after. Users only retrieve documents they have permission to read — enforced at vector store query time, with audit logs of every retrieval event for SOC 2 review.
Every generated response is grounded to specific document spans. A separate verification pass checks whether the cited spans actually support the claim — flags hallucinations before they reach the user. Citations are clickable, jumpable, and visible to the user.
Golden-set evaluation suite built from your real document corpus. CI integration catches retrieval regressions before deploy. Production monitoring tracks citation accuracy, retrieval coverage, and query latency in real time — so you know what the system is doing on real traffic, not what it did in QA.
The concrete deliverables of an engagement. Everything ships to your repo, in your stack, under your control.
Four to eight weeks, broken into four phases. Predictable rhythm, transparent progress, no surprise deliveries.
Week 1. We work with you to audit a representative sample of the documents the system will need to handle. We identify failure-prone patterns specific to your corpus, scope the chunking and embedding strategies, and finalize the architecture. You get a written scope document at the end of the week.
Weeks 2–4 typically. Production RAG pipeline shipped to your stack behind a feature flag. Eval suite built from your real document corpus in parallel — so we know the system's failure rate before it touches a user. Weekly demos, code in your repo from week 2.
Weeks 4–6 typically. RBAC integration with your identity provider. Audit log infrastructure. Citation verification layer. Monitoring dashboards. By end of phase, the system is ready for compliance review — and we'll walk through it with your reviewer if useful.
Final week of engagement plus 30 days after. Documentation finalized, two handoff training sessions with your team, direct Slack access for 30 days post-launch. After day 30, optional retainer available. No lock-in, no platform fees, no surprise renewals.
Things buyers commonly ask about RAG engagements. If your question isn't here, the call is the easiest way to get an answer.
Yes. We build provider-agnostic. The pipeline works with OpenAI, Anthropic, Bedrock, Vertex, or self-hosted models like Llama or Mistral. The choice is yours — and so is the bill. We'll recommend based on your latency, cost, and compliance constraints, but the decision is yours.
Three things. Retrieval accuracy (did we retrieve the right document spans for this query?), citation faithfulness (does the generated answer actually match what the cited spans say?), and regression behavior (when we change a parameter or upgrade a model, does anything we already had working break?). The suite runs in CI and produces a numerical accuracy score per release.
Almost always. We've built on Pinecone, Weaviate, pgvector, Qdrant, and Chroma. If you have a strong preference or an existing deployment, we work with it. If you don't have one yet, we'll recommend based on your scale, latency, and operational preferences.
Under your access controls, in your environment, with whatever data residency requirements you have. We don't take copies of your documents out of your cloud. If you're in a regulated jurisdiction with specific data handling rules — UK FCA, EU MiCA, US bank-grade — we work within them. NDAs signed before any document access.
Common case. We start with an audit of what you have and figure out whether to fix it or replace it — usually a mix. We'll tell you honestly. Sometimes the answer is 'your retrieval logic is fine, replace the chunking strategy and rebuild the eval suite.' Sometimes it's 'this needs to start over.' Either way, you get a written assessment within the first two weeks.
First call is 30 minutes. You describe what you're trying to ship and what's in the way. We ask technical questions about your documents, your stack, and your compliance constraints. By the end of the call, we'll both know whether this is something we should build together.