Your policy library, answerable.

• 01

  Frontline policy assistant

  Branch and support staff ask in plain language and get the current clause with its effective date — plus an explicit warning when the retrieved revision has been superseded.

• 02

  Product terms across a changing catalogue

  Fee schedules and eligibility rules change per product, region and vintage. Retrieval is scoped by those dimensions so a 2024 answer can't leak into a 2026 question.

• 03

  Regulatory change triage

  New circulars are diffed against your existing procedure set, and the assistant drafts the list of documents that now contradict it for a compliance officer to confirm.

• 01

  Every answer needs a receipt

  We ground responses in your own documents and return the citation with the answer. If the retrieval finds nothing, the system says so instead of improvising — that refusal path is tested like any other feature.

• 02

  Models never move money

  The LLM classifies, drafts and retrieves. Balance changes, limit increases and refunds run through your existing deterministic services behind an approval step, with the full prompt and decision written to an audit log.

• 03

  Data residency is a build constraint, not a setting

  For RBI, PCI-DSS and SOC 2 scopes we deploy inside your VPC or account with zero data egress, and pick models — including open-weights — that can legally sit where your data sits.

1. 1

   Data inventory

   What docs / databases / APIs / feeds matter? What's PII vs public? What changes daily vs quarterly? We map it before writing code.

2. 2

   Retrieval architecture

   Hybrid (BM25 + vector) retrieval, chunking strategy, reranking, citations. Tuned for your domain not a generic benchmark.

3. 3

   Eval suite

   Real-world question set with golden answers. Faithfulness, context precision, latency. So you know when changes ship a regression.

4. 4

   Guardrails

   Prompt-injection defense, PII scrubbing, refusal patterns, output schemas. Especially critical for customer-facing deployments.

5. 5

   Production + iterate

   Cost-effective inference (caching, fallback models), monitoring, weekly eval reports. Fine-tune when the data justifies it.

• Stripe
• Razorpay
• Plaid
• Salesforce Financial Services Cloud
• Zendesk & Intercom
• Snowflake / BigQuery
• Postgres with row-level security
• Twilio
• Internal core-banking APIs

FAQ

RAG for banking and financial services: your questions.

• Our documents contradict each other. What then?

  The system surfaces the conflict rather than picking a winner: both passages, both dates, and a flag. That surfacing is usually worth more than the answering — most libraries we index have contradictions nobody had noticed.

• Can we keep customer data out of the model entirely?

  Yes, and for most policy use cases you should. The index holds documents, not customer records; where an answer needs account context we inject the specific fields at query time and drop them after, rather than embedding anything customer-identifiable.

• Should I use RAG or fine-tuning?

  RAG for facts that change. Fine-tuning for style, format, or domain reasoning. Most production systems use both — and we'll tell you which mix is right after a 1-week discovery.

• Can I use my own LLM (open-weights)?

  Yes. We work with Llama, Mistral, Qwen, and similar — useful for data residency, cost predictability, and air-gapped environments.

• AI agents for fintechAgents wired into your ledger, KYC provider and helpdesk — resolving the repetitive half of the queue end to end, and escalating the rest with the context already gathered.Read
• AI governance for financial servicesModel inventories, risk classification, evaluation policy and human-oversight design — written to survive an internal audit rather than a conference talk.Read
• Fintech prototype to productionWe take a working fintech prototype and add the things that were never in scope: real auth, migrations, secrets handling, audit logging, and a test suite that survives the next regeneration.Read

• RAG for healthcare and clinical documentsRetrieval over clinical guidelines, payer policy and internal SOPs — with the citation, the version, and an explicit refusal when the library doesn't cover the question.Read
• AI product search and catalogue RAGSemantic search and grounded product Q&A over your catalogue, reviews and size guides — with stock and price read live rather than remembered.Read
• RAG for SaaS productsRetrieval over your docs and your customers' workspace content, with isolation enforced in the index and tested adversarially.Read