Self-Hosted AI Knowledge Assistant for Document-Heavy Teams
A self-hostable RAG platform designed to cut document lookup from 15 minutes to under a minute
Executive Summary
Projected lookup time per query, down from 10-15 minutes
Projected daily staff time on search, down from 4-7 staff-hours
Executive, Legal, Financial, and Operational summarization
Data stays inside the organization's own infrastructure
The Challenge
Document-heavy teams lose hours each week searching for answers buried across contracts, reports, SOPs, policies, scanned files, email threads, and shared drives. For legal, finance, operations, HR, and compliance teams, the issue is not just search speed — critical information lives in unstructured documents, scanned files, or tribal knowledge. The typical workflow: search shared drives and email, open multiple candidate PDFs, manually skim for the relevant section, ask colleagues when the answer is not obvious, summarize findings manually, and repeat the whole process when a similar question comes up again. For a typical 100-500 person document-heavy company, this means 20-30 lookup queries per day at roughly 10-15 minutes each — 4-7 staff-hours daily spent on search. For regulated or confidentiality-sensitive teams the problem is harder still: they often cannot send internal documents to public AI SaaS tools at all, ruling out the obvious off-the-shelf answers.
Key Pain Points
- ✕20-30 daily lookup queries at 10-15 minutes of manual search each
- ✕Critical answers trapped in scanned files invisible to text search
- ✕Knowledge dependent on asking colleagues and tribal memory
- ✕Sensitive documents unsuitable for public AI SaaS tools
Our Solution
Amasa built a self-hostable enterprise knowledge assistant for internal document intelligence — a capability case study with modeled impact. Users upload documents; scanned files are processed through a dedicated FastAPI + PaddleOCR microservice with API-key authentication, PP-OCRv4 model and language selection, and confidence-scored output. Documents are chunked, embedded, and stored in a MongoDB vector index. Users then ask questions in chat: intent detection routes each request to RAG-based Q&A or to summarization, with Executive, Legal, Financial, and Operational summarization modes, plus automatic FAQ generation. Answers come grounded in the uploaded corpus for users to validate before acting. The backend uses Express, Inversify, TypeORM, MongoDB vector search, LangChain, and OpenAI GPT models. Amasa also built the operating layers a real deployment needs: policy enforcement, AI-powered content validation, rate limiting, trial and lead management, credit tracking, access codes, automated data-lifecycle cleanup, and structured Winston logging. Critically, the whole platform is deployable inside the buyer's own infrastructure, so sensitive documents never leave the organization.
Implementation Approach
Ingestion & OCR Pipeline
Built document upload with a dedicated PaddleOCR microservice for scanned material, with confidence-scored extraction
RAG Core
Implemented chunking, embedding, and MongoDB vector search with LangChain and OpenAI GPT models for grounded Q&A
Intent Routing & Summarization
Added intent detection routing between chat and four summarization modes, plus automatic FAQ generation
Enterprise Operating Layers
Built policy enforcement, content validation, rate limiting, credit tracking, data-lifecycle cleanup, and structured logging for self-hosted deployment
The Results
The implementation delivered transformative results across all key metrics, with immediate impact on operational efficiency, accuracy, and customer satisfaction.
Impact Metrics
| Metric | Before | After | Improvement |
|---|---|---|---|
| Search time per query | 10-15 minutes | Under 1 minute (projected) | ~90% faster |
| Daily search effort | 4-7 staff-hours/day | 30-90 minutes/day (projected) | ~80% reduction |
| Scanned documents | Often invisible to search | OCR ingestion with text extraction | Fully searchable |
| Data handling | Unsuitable for public SaaS tools | Deployable inside buyer infrastructure | Self-hosted |
Key Takeaways
- Self-hostable RAG unlocks AI document intelligence for regulated teams that cannot use public SaaS tools
- OCR ingestion makes scanned material — often the blindest spot in enterprise search — fully queryable
- Intent detection that routes between Q&A and summarization matches how people actually ask questions
- Production readiness lives in the operating layers: policy enforcement, rate limiting, data lifecycle, and logging
Inside the Capability
The Workflow Before
- Search shared drives, email threads, or folders.
- Open multiple candidate PDFs, reports, contracts, or SOPs.
- Manually skim documents for the relevant section.
- Ask colleagues when the answer is not obvious.
- Summarize findings manually for another stakeholder.
- Repeat the process when a similar question comes up again.
For a typical 100–500 person document-heavy company, this means 20–30 lookup queries per day at roughly 10–15 minutes of manual search each – 4–7 staff-hours daily spent on search. And for regulated or confidentiality-sensitive teams, sending internal documents to public AI SaaS tools is often not an option at all.
The Workflow After
- A user uploads documents into the system.
- Scanned files are processed through OCR.
- Documents are chunked, embedded, and stored in a MongoDB vector index.
- The user asks a question in chat.
- Intent detection routes the request to RAG Q&A or summarization.
- The system returns a grounded answer from the uploaded document corpus.
- Users validate the answer before using it in business decisions.
Beyond Q&A, the platform generates FAQs automatically and offers Executive, Legal, Financial, and Operational summarization modes – matched to how different stakeholders actually consume documents.
Modeled Impact
This is a capability case study: the impact below is modeled for a typical document-heavy team, using projected figures.
| Metric | Before | After |
|---|---|---|
| Search time per query | 10–15 minutes | Under 1 minute |
| Daily query volume | 20–30 queries/day | Same volume handled through self-serve chat |
| Staff time spent on search | 4–7 staff-hours/day | 30–90 minutes/day |
| Knowledge access | Shared drives, email, and colleagues | Self-serve document Q&A |
| Scanned documents | Often invisible to search | OCR ingestion with text extraction |
| Data handling | Sensitive docs unsuitable for public SaaS tools | Deployable inside buyer infrastructure |
Enterprise Operating Layers
Production readiness lives in the layers around the AI core. Amasa built policy enforcement, AI-powered content validation, rate limiting, trial and lead management, credit tracking, access codes, automated data-lifecycle cleanup, and structured Winston logging. The OCR path runs as a dedicated FastAPI + PaddleOCR microservice with API-key authentication, PP-OCRv4 model and language selection, and confidence-scored output.
The Takeaway
Amasa built a self-hostable enterprise knowledge assistant that turns internal document repositories into searchable, AI-powered knowledge systems. For document-heavy teams, the platform is designed to reduce manual document search from 10–15 minutes per query to under a minute – while keeping sensitive data inside the organization’s own infrastructure.
Quick Facts
Industry
Enterprise
Solution Type
Generative AI
Published
August 24, 2026
Technologies Used
Related Resources
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