AI-Powered Document Processing & Knowledge System for a Major Consulting Firm
A global consulting firm cut time to find critical information by 65% and document preparation time by 50% with citation-backed RAG search over SharePoint and AI drafting tools.
- Industry:
- Professional Services & Consulting
- Tech stack:
- Python, LangChain, Azure OpenAI +5 more
- Updated:
- Less time to find critical information
- 65%
- Less document preparation time
- 50%
- Citation-backed answers
- RAG
- Research cycles with talk-to-docs
- Days → hours
Case study summary
Perimattic built an AI-powered document processing and knowledge system for a global consulting firm with thousands of consultants and analysts. Connected to the firm's SharePoint repository, it answers natural-language questions with citations using retrieval-augmented generation (RAG), drafts proposals and statements of work, and automates due diligence and competitive benchmarking. Time to find critical information fell 65% and document preparation time fell 50%.
About the client
The client is a global consulting firm with thousands of consultants and analysts. It needed an AI-powered platform to transform internal document processing, knowledge discovery and content workflows by integrating with its existing SharePoint repository and digital tools. Its knowledge management relied on keyword search and manual document handling.
The challenge: keyword search and manual content workflows
Inefficient search and discovery
Consultants struggled to find relevant deliverables, reports and proposals because search was keyword-based.
Manual content workflows
Writing proposals, statements of work (SoWs) and client reports took significant time and manual effort.
Competitive benchmarking and due diligence
Analysing competitor moves, financial reports and contracts by hand was slow and error-prone.
How Perimattic built the AI knowledge system
Perimattic sped up knowledge discovery and automated content workflows using retrieval-augmented generation (RAG), LLM-powered assistants and analytics dashboards, all connected to the firm's existing SharePoint content.
Intelligent document understanding
- Integrated AI-powered search with the SharePoint repository for contextual discovery
- Enabled prompt-based search (for example, “What are Q1 trends for Client X?”) with direct citations
- Built summarisation and extraction tools for project plans, invoices and regulatory filings
- Implemented RAG for accurate, citation-backed responses
AI-powered content creation and automation
- Developed a digital assistant for drafting proposals, SoWs and pitch decks
- Built auto-generated newsletters tailored by industry, theme and geography
- Automated resume parsing and matching for hiring workflows
- Created an onboarding assistant that guides new hires through policies, documents and videos
Predictive analytics and competitive insights
- Used LLMs with reasoning for predictive analysis and trend forecasting
- Automated due diligence of financials, contracts and annual reports
- Brought survey data (SurveyMonkey and internal feedback) into dashboards
- Built benchmarking tools that track bids, wins, hiring and technology adoption
Results: 65% faster knowledge discovery
65% faster knowledge discovery
Time to find critical information fell by 65%, giving consultants faster access to insights.
50% less document preparation time
Automated summarisation and drafting halved document preparation time.
Data-driven insights
Predictive forecasting and competitor benchmarking give consultants a strategic edge.
Seamless workflows
Automated ingestion and indexing eliminated manual document-handling errors.
Faster decision-making
Talk-to-docs Q&A cut research cycles from days to hours.
Services behind this project
- RAG Development ServicesRetrieval-augmented generation over your own documents, with citations.
- AI Development ServicesProduction-grade AI systems built to your workflow, from scoping to deployment.
- AI Agent DevelopmentConversational and autonomous agents that act across your business systems.
- RAG evaluation (Perimattic AI Suite)Faithfulness and retrieval metrics scored on live RAG traffic.
Technologies behind the knowledge system
The languages, frameworks, platforms and integrations used to deliver this project.
- Python
- LangChain
- Azure OpenAI
- RAG pipelines
- Microsoft SharePoint APIs
- Kubernetes
- Vector databases
- Power BI
Questions about this project
What is a RAG knowledge system?
Retrieval-augmented generation (RAG) retrieves the most relevant passages from your own documents and passes them to a large language model, so answers are grounded in, and cite, your content rather than the model's general training data. Here, the source content is the firm's SharePoint repository.
How does the system work with SharePoint?
It connects through Microsoft SharePoint APIs. Documents are ingested and indexed automatically, and consultants search them with natural-language prompts that return answers with direct citations.
What results did the consulting firm achieve?
Time to find critical information fell 65%, document preparation time fell 50%, and talk-to-docs Q&A cut research cycles from days to hours.
How can you keep RAG answers accurate in production?
Citations let users check every answer against its source. For ongoing quality, we recommend measuring retrieval and faithfulness on live traffic; see RAG evaluation and our guide to evaluating an LLM before deployment.
