Case Study: AI Research Agent for 1,000+ Project Documents
Why classic RAG systems fail – and how an agentic RAG approach with LangChain, Qdrant, and Claude cuts research time by over 90%.
Up to 1,000+ documents per project – contracts, project files, internal policies. All of it existed, yet none of it was truly accessible: manual research took 30 to 60 minutes per query, critical details were missed, and off-the-shelf AI tools produced unreliable answers with no sources.
For a mid-sized company, we solved this knowledge problem with a custom AI research agent: complex questions are now answered in 1 to 2 minutes – precise, verifiable, and with significantly reduced hallucinations. That’s over 90% time savings per research task.
Case study · PDF · 9 pages · German
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The challenge
Starting point:
- 1,000+ documents per project – scattered, unstructured, hard to search
- 30–60 minutes of manual searching per complex query – a massive productivity drain
- Missed details: nobody could be sure they had really found every relevant passage
And the pain points with standard AI tools:
- Hallucinations: invented content is an absolute no-go in a business context
- No traceability: answers without sources are neither trustworthy nor audit-proof
- Scalability: the effort kept growing with the document base
Why classic RAG systems fail here
Retrieval-Augmented Generation (RAG) is the standard approach for letting AI models work with your own documents. At this scale, however, classic RAG hits hard limits:
| Classic RAG | Agentic RAG – the difference |
|---|---|
| Rigid chunking: documents are blindly split into equal pieces, context gets lost | Intelligent chunking: semantically meaningful segmentation, context is preserved |
| No understanding of tables & images: only text is processed | Multimodality: text, tables, charts, and images are understood together |
| Single-shot retrieval: one search query, no matter how complex the question | Iterative tool calling: the agent keeps issuing targeted queries until the answer is complete |
| No self-correction: irrelevant hits go unnoticed | Self-reflection: the model evaluates result quality and corrects course on its own |
The solution: an agentic RAG architecture
Instead of a rigid pipeline, the system acts as an autonomous research agent. Through advanced tool calling, the model decides on its own which search tools to use and how often. If the agent finds a reference to another contract inside a document, it fetches that contract next to verify the linked information – just like a human analyst would. A vision LLM also unlocks tables, charts, and scanned pages that classic text pipelines simply skip.
The flow: user question → LangChain orchestrates the agent → the agent develops a search strategy → semantic search in the vector database → a complete answer is synthesized.
The source feature: trust through transparency
The heart of the solution is end-to-end traceability for every answer – from the question to the exact passage in the original document:
- Ask a question – e.g. “What notice periods apply in contract X?”
- Semantic search – the agent retrieves the most relevant text segments from the 1,000 documents via Qdrant
- Answer generation – Claude answers exclusively based on those passages (grounded generation)
- Source linking – every statement is tied to a file name and page number
- One-click verification – a click opens the exact passage in the original
Question: “What notice periods apply in contract X?”
AI answer: “According to section 8.2, a notice period of 3 months to the end of the quarter applies…”
[Source: Vertrag_Firma_B.pdf, page 14]
Results & ROI
- > 90% time savings – from an average of 45 minutes down to 2 minutes per research task
- Significantly reduced hallucinations – grounded generation based exclusively on the company’s own documents; every statement is backed by a source rather than invented
- 100% source attribution – every answer comes with a verifiable citation
The technology stack at a glance
| Component | Role | Benefit |
|---|---|---|
| LangChain | Orchestrator | Flexible chunking & retrieval |
| Qdrant | Vector database | Fast semantic search |
| Claude (Anthropic) | LLM / analyst | Large context window, demonstrably low hallucination rate |
| RAG | Architecture pattern | Grounded generation, minimizes fabrications |
LangChain and Qdrant are battle-tested open source, Claude runs under an enterprise agreement for data sovereignty – all behind a custom interface in the client’s corporate design.
Your next step
What we can do for you:
- Intelligent assistants & RAG – next-generation document and knowledge management
- Process automation (agents) – autonomous AI agents that take over recurring workflows and administrative tasks
- Custom LLM & vision integration – connecting and fine-tuning modern language and image models for your core processes
- AI strategy & prototyping – from initial opportunity analysis to a ready-to-use proof of concept in a few weeks
Whether you want to tame a huge document base, automate workflows, or bring an entirely new AI idea to life – in a free 30-minute demo call we’ll show you live, on a real document set, what’s possible. From first conversation to a running proof of concept in less than two months.
Learn more about our AI services on the AI & Digitalization page.
Your contact
Let's talk about your project.
You'll talk directly to the consultant who designs and builds systems like this one – no strings attached.
Joachim Havloujian · Consultant