# Document search for a B2B accounting platform

URL: https://www.getdynamiq.ai/case-studies/smart-document-search-for-a-b2b-accounting-platform-built-in-one-week-with-dynamiq

> How a B2B accounting platform added natural-language document search on Dynamiq, built by one engineer in a week using Claude and Amazon OpenSearch.

A B2B accounting platform needed natural-language search across contracts, invoices and policies, where keyword search kept coming up empty. One of our engineers built a retrieval pipeline on Dynamiq that answers in plain language with the source documents attached.

Time to launch

A B2B accounting platform shipped document search in one week with one of our engineers.

Model stack

Runs on Anthropic Claude and Amazon Titan Text Embeddings over Amazon Bedrock, with Amazon OpenSearch as the vector store.

Query style

Answers natural-language questions about contracts, invoices and policies, not just keyword matches.

Engineering model

Built and shipped by one Dynamiq engineer working directly with the client's own team.

## The challenge

The client runs a B2B accounting platform that its own customers use to manage contracts, invoices and other financial paperwork, and users kept hitting the same wall: finding one specific document inside a large, unstructured pile of PDFs, Word files, slide decks and images. The platform's existing search was keyword-only, so a question like "which contracts expire next month" or "find invoices sent in March with overdue payments" returned nothing useful. The team wanted search that understood plain language, handled every common document type, and scaled without a pile of new infrastructure to operate. Building that in-house would have meant standing up a machine-learning team and a vector database from scratch, at a cost the roadmap could not absorb.

## What we built

One of our engineers worked directly with the client's team to design a retrieval-augmented search pipeline on Dynamiq: documents are converted and embedded on ingestion, stored in Amazon OpenSearch as the vector store, and queried through Anthropic Claude for the answer itself. A user can now ask "show me all NDAs signed last month" or "which contracts have not been renewed" in plain language and get back the right documents with an answer synthesized from them, directly inside the product they already use. One-click connections to Claude and Titan Text Embeddings over Amazon Bedrock meant the team never had to stand up a separate inference pipeline or manage a model deployment by hand.

## How it runs

A document enters the pipeline as a PDF, a Word file, a slide deck or an image. Dynamiq converts it to text, embeds it, and writes it into Amazon OpenSearch, which serves both vector similarity and keyword search from the same index. When a user asks a question, Dynamiq's retrieval pipeline pulls the most relevant chunks, hands them to Claude, and returns an answer with the source documents attached, so a user can always check where the answer came from. Model access runs through Amazon Bedrock, so the client's existing AWS relationship and controls extend to the new feature without a separate vendor to manage. Because the pipeline is a workflow rather than custom code, the client's own team can see and adjust every step themselves.

## Results

A B2B accounting platform shipped document search in one week with one of our engineers. The feature shipped with no additional infrastructure to operate and no custom machine-learning model to maintain, so the roadmap moved straight to the next problem instead of a long build. The client is now extending the same pipeline to document summarization and structured extraction, such as pulling contract terms and payment details automatically.

## Built with

-   [Knowledge](https://www.getdynamiq.ai/product/knowledge-rag)
-   [Models and AI Gateway](https://www.getdynamiq.ai/product/models)
-   [Agent Builder](https://www.getdynamiq.ai/product/agents)

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