Transforming contract workflows with Claude for financial services
Three days to answer a contract question. Under five seconds with Claude. For a $7.4B financial services company fielding 250 inquiries a month through five employees, that gap represented compounding compliance risk and a workload growing 40% a year with no path to scale. phData built a production Claude for financial services RAG AI assistant on Amazon Bedrock to close it.
At a glance
| Industry | Financial Services |
| Scale | 5,600 employees, $7.4B revenue |
| Challenge | Five FTEs three day average response time per contract inquiry with inquiry volume growing 40% annually |
| Technology | Anthropic Claude, Amazon Bedrock, Amazon Textract, Amazon Titan, LanceDB, AWS Lambda, AWS S3, AWS EKS, Streamlit |
| Timeline | Production deployment |
| Result | Contract inquiry response time reduced from 3 days to under 5 seconds; 40%+ inquiry growth absorbed without added headcount |
| Phdata Service | Generative AI, AI & Machine Learning, Anthropic, AWS |
The problem
For a $7.4B financial services company fielding 250 contract inquiries a month, answering a single question meant manual search, email chains, and three days of back-and-forth across a team of five full-time employees. With volume growing 40% annually, headcount would need to grow every year just to maintain a deteriorating experience where responses missed key details and left customers waiting.
Retrieval-augmented generation (RAG) is an AI architecture that combines a document search pipeline with a large language model. The system retrieves relevant source documents first, then passes them to the LLM to generate an answer grounded in that specific content.
Vector embeddings is numerical representations of text that allow a search system to find semantically similar content, so a question about “early termination fees” surfaces the right contract clause even if the exact phrase doesn’t appear.
Amazon Bedrock is AWS’s managed service for running foundation models inside a customer’s existing AWS security perimeter, without routing data through external APIs.
What phData did
Four decisions shaped the delivery.
phData chose Anthropic's Claude over other LLMs for citation-quality output
phData evaluated multiple foundation models before selecting Claude, which delivered the highest accuracy and fewest hallucinations across the test set. It also uniquely generated near-final draft responses that cited the specific contract language behind each answer, rather than generic summaries.
Anthropic’s Claude via Amazon Bedrock gave the team both the output quality they needed and the compliance posture the firm required.
phData built the entire stack inside AWS to satisfy compliance requirements
The contract retrieval pipeline runs on AWS Lambda, converts PDFs using Amazon Textract, creates vector embeddings with Amazon Titan, and stores them in a LanceDB vector database on AWS S3.
Hosting everything within the firm’s existing AWS environment meant sensitive customer contract data never left their security perimeter, a non-negotiable in financial services.
phData used RAG rather than fine-tuning to keep answers current as contracts change
Fine-tuning an LLM on contract content creates a model frozen at training time; phData used RAG instead, retrieving from the live vector database at query time so answers always reflect the current contract library. No retraining is required when contracts change.
phData added metadata filters to improve retrieval precision
Rather than relying on free-text search alone, the AI assistant interface gives associates dropdown menus to filter by contract metadata before querying.
This narrows the retrieval set to the most relevant contracts before Claude generates a response, reducing irrelevant results and improving answer precision in a library with hundreds of contracts.
The result
Contract questions that once averaged a three-day turnaround now get answered in under five seconds, with five full-time contract research roles transitioned to a part-time prompting workflow.
Adoption followed a pattern common in enterprise AI: initial skepticism, then enthusiastic uptake. Contract research associates were initially concerned that automation meant their roles were at risk. Once they began using the assistant, that concern disappeared. The system surfaces relevant contract clauses and summarizes them in plain language. Associates quickly realized it eliminated the tedious document search while leaving the judgment work to them. They loved it.Â
Contract research associates receive citation-backed draft responses in seconds, replacing days of manual document review.
40%+ year-over-year inquiry growth is absorbed without adding headcount.
The Case Underwriting team now has access to the same capability, extending contract research across the organization.
Every response cites the specific contract clause it draws from, enabling immediate verification and action.
contract inquiry response time, down from a three-day average.
inquiry growth handled without adding headcount.
Ready to see what a similar engagement could look like for your organization?
Why this matters beyond this project
Every financial services company with complex product documentation faces this same contract inquiry bottleneck. What this engagement proves is that Claude for financial services isn’t a future aspiration. phData is deploying production systems that deliver measurable ROI today, at regulated firms with compliance constraints. Any organization sitting on a library of PDFs, whether that’s contracts, policies, underwriting guidelines, or product disclosures, has the raw material to replicate this outcome. phData’s AI & Machine Learning services exist to move from that raw material to a production-grade solution in weeks.
Frequently asked questions
How does Claude for financial services reduce contract inquiry response times?
Claude reduces contract inquiry response times by powering a financial services contract AI assistant that replaces manual document search with a RAG pipeline: an associate asks a question, the system retrieves the most relevant contract chunks from a vector database, and Claude generates a citation-backed response in under five seconds. The process that previously required email exchanges and days of manual review is reduced to a single prompt.
What is retrieval-augmented generation (RAG) and how does it work for contract research?
RAG contract management works by retrieving relevant source documents first, then passing them to a language model to generate an answer grounded in that content. For contract research, this means the LLM answers based on actual contract language in your library, not general training data, reducing hallucination and making every response auditable.
Is it safe to use Claude with sensitive financial services documents?
Yes, when architected correctly. phData deployed this solution entirely within the firm’s AWS environment using Amazon Bedrock to run Claude. Customer contract data never passed through external APIs or Anthropic’s systems directly. The regulated financial services context was a core design constraint from the start.
What does it cost to build a Claude-powered contract AI assistant?
Cost depends on contract library size, query volume, and integration requirements. phData’s 3-2-1 GO accelerator is designed to get AI solutions like this into production quickly, typically in a matter of weeks. Contact phData for a scoped estimate.
What AWS services does phData use to build RAG AI assistants for financial services?
This deployment uses Amazon Bedrock (to run Claude), Amazon Textract (PDF-to-text extraction), Amazon Titan (vector embeddings), AWS Lambda, AWS S3, LanceDB for the vector database, and AWS EKS.
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