Learn how Tribe built an AI-powered document engine for a global consulting firm that reduced document review time from 3-5 days to under 6 hours.
The Challenge
At a global consulting firm specializing in financial and operational due diligence, consultants faced delays and bottlenecks when reviewing unstructured documents (financial statements, contracts, reports, etc.). Manually extracting key data from these documents was time-intensive and prone to errors, limiting how many documents could be covered per project. The firm sought an AI solution that could automate extraction and enable consultants to query documents directly.
The Solution
Tribe AI collaborated with the firm to implement an AI-powered document extraction and Q&A engine within its proprietary due diligence platform. The system combined Optical Character Recognition (OCR) with large language models to unlock data from unstructured documents, enabling consultants to ask natural language questions and receive direct, cited answers without line-by-line reading.
Key Features
The document extraction system offered a suite of capabilities tailored to handle diverse, complex document types:
- OCR-based ingestion of PDFs, scanned images, and other non-native formats using Microsoft AI Document Intelligence.
- Automated extraction of structured data fields like financial figures, contract dates, vendor names, and key terms.
- Relationship extraction to establish links between various contracts such as MSAs and SoWs, contracts and their addendums etc.
- Natural language Q&A interface, allowing consultants to query multiple documents in plain English.
- Source citation and answer traceability, linking answers back to their exact location in the original document.
These features streamlined document-heavy workflows while maintaining auditability.
How It Works
The document extraction and Q&A engine follows a multi-step process:
- AI Document Intelligence converts scanned and image-based files into machine-readable text.
- Parsing models detect tables, figures, clauses, and metadata within document structures.
- LLM-based Q&A models process user queries, retrieving answers from parsed content.
- Citations are embedded with answers, allowing consultants to review the source snippet directly.
This process allows consultants to ask, “What’s the EBITDA for 2022?” or “When does the contract expire?” and receive immediate, validated responses.
Tech Stack
Document Extractor
- Cloud: Azure
- LLM: Azure OpenAI - GPT 4o (with structured outputs)
- Other cloud services: Azure app services for hosting, MSSQL Server for storing results
- Languages used: Streamlit
File Q&A
- Cloud: Azure
- LLM: Azure OpenAI - GPT 4o (with tool calling)
- Other cloud services: Azure app services for hosting, Vector DB in Azure search
- Languages used: Streamlit
Impact
The AI-powered document engine dramatically reduced time spent on manual document review, enabling consultants to cover more documents with greater speed and accuracy. By unlocking data previously trapped in PDFs and scans, the platform supported faster insights and reduced operational friction.
Key results included:
- Reduced document review time from 3-5 days to under 6 hours.
- Increased number of documents reviewed per project by 60%.
- Eliminated 90% of manual data entry previously required for extracting key figures.
- Reduced errors by providing auditable, source-cited answers.
The Future
This work has paved the way for broader AI applications across the firm’s document-heavy workflows. With AI-powered document extraction deployed, the firm is exploring ways to extend the platform’s capabilities into broader contract analytics, obligation tracking, and intelligent summarization. Future enhancements aim to integrate directly with contract lifecycle management systems and leverage AI to flag risks, discrepancies, and opportunities in real time.
