Can it be used for regulated customer-facing advice?

It is built as an internal tool for qualified staff. Retrieval and citation make it a fast route to source material, but the regulated judgment stays with the adviser and the system is scoped so it cannot be pointed at customers.

Can it run entirely in our environment?

Yes. Where data residency or vendor restrictions require it, the whole stack — embeddings, vector store, and open-weight models — deploys inside your infrastructure with nothing leaving the network.

Why not just fine-tune a model on our documents?

Fine-tuning teaches style and format well but is a poor way to store facts — it cannot cite sources, and updating a single document means retraining. RAG updates the moment you re-index and can always show its working. Most projects that think they need fine-tuning need better retrieval.

How accurate can we expect it to be?

That depends on your corpus, and we will measure it rather than promise a number. The evaluation harness gives you a retrieval accuracy figure on your own documents before anything reaches users — if it is not good enough, we tune until it is.

RAG Systems · Financial Services

RAG Systems for Financial Services

Regulatory answers need a source, a date, and a version.

Compliance manuals, product terms, regulatory guidance, and internal policy change constantly and carry real consequences when misquoted. Retrieval over that corpus gives staff the current passage with its effective date, rather than an answer assembled from memory.

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Every claim: GroundedEvery claim
Search strategy: HybridSearch strategy
Retrieval accuracy: MeasuredRetrieval accuracy

Typical stack

LlamaIndex
LangChain
Pinecone
pgvector
Cohere Rerank

Use cases

What this handles for financial services firms

Search compliance manuals and regulatory guidance by question
Retrieve product terms and conditions with effective dates attached
Answer internal policy questions with a citation staff can forward to compliance
Track which version of a policy was in force at a given date for review purposes

Policy answers that arrive with a source, a version, and a date attached as standard.

Context

The financial services problem underneath it

Financial services work is document-heavy, deadline-driven, and unforgiving of errors — which is exactly the profile automation suits, provided everything it does is logged and reversible. We build systems where every extraction, calculation, and posting leaves an inspectable trail.

Statements, invoices, and receipts arrive in every format and get keyed by hand
Reconciliation is monotonous, high-volume, and expensive when it goes wrong
Client onboarding and KYC involve chasing documents for weeks
Reporting packs are assembled manually against a hard deadline every period

What we build

Inside a RAG system

Most failing RAG deployments fail at retrieval, not generation. We build the evaluation harness first: a set of real questions with known-correct source passages, so retrieval accuracy is a number we can improve rather than a feeling. Chunking strategy, hybrid search, and reranking then get tuned against that number.

Document parsing that preserves tables, headings, and layout structure
Chunking strategies tuned per document type rather than a fixed token count
Hybrid dense and sparse retrieval with a cross-encoder reranking pass
Citation grounding that ties every claim to a specific retrieved passage
Retrieval evaluation harness with recall and precision tracked per release
Incremental re-indexing so updated documents are searchable immediately

Deliverables

What ships with the engagement

Indexed corpus with a documented chunking and embedding strategy
Golden evaluation set of questions with verified source passages
Retrieval quality report benchmarked before and after tuning
Query API or chat surface, depending on how you will consume it
Runbook for adding new document types to the pipeline

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