AI-Powered Document Summarizer
Document summarization and point-of-interest extraction across large multi-document sets, built on an agentic graph RAG architecture that holds context together where chunking normally breaks it.
- Architecture
- Agentic Graph RAG
- Scope
- Multi-document
- Sectors
- Insurance · Manufacturing · Biomedicine
The problem
High-growth companies accumulate documents faster than anyone can read them. The bottleneck was never storage — establishing what a set of documents actually said meant a person working through it one file at a time, which made document-heavy steps the slowest part of delivery.
The approach
Three distinct jobs, because “summarize this” turns out to be several different problems:
- Document summarization — condensing one document without losing the detail that made it worth reading.
- Point-of-interest extraction — pulling out the items that matter to a given workflow, rather than producing prose someone still has to mine.
- Multi-document summarization — synthesizing across a set, so the output reflects the collection rather than a pile of per-file summaries.
All three share one failure mode: chunking. Splitting documents into fixed windows to fit a context limit is convenient for retrieval and destructive for meaning — a clause separated from the condition qualifying it, a figure from its table. Flat vector search compounds it, because similarity rewards redundancy: the same point restated across five documents, and the one contradicting it missed.
So retrieval is an agentic graph RAG architecture rather than a single-shot lookup. Chunks are nodes in a graph preserving their relationships — to the section and document they came from, and to the entities they share with chunks elsewhere. Retrieval traverses those edges instead of ranking by distance alone, so a passage arrives carrying the context that makes it mean what it means, and the agentic layer decides what to do next: expand along a relationship, re-query, or stop. That is what makes multi-document summarization something other than concatenation.
It runs serverless on AWS — S3, Step Functions, AWS SAM — and multi-tenant from the start, serving organisations across insurance, manufacturing, and biomedical work, each with its own document conventions.
The outcome
Document-heavy stages stopped being the constraint on delivery. Reviewers start from extracted points of interest and a synthesis across the whole set instead of page one of file one — and because extraction is systematic, what surfaces no longer depends on who happened to be reading.