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Rivac AI Labs
Data Intelligence, Document AI & Applied Analytics

Document Question-Answering Systems

When the answer to a question lives somewhere inside thousands of internal documents, the honest cost isn't the document, it's the time spent hunting for it, or the wrong answer given because nobody found the right one. This service builds a retrieval-based question-answering system over your internal document corpus (policies, manuals, contracts, past reports) so anyone on your team can ask a plain-language question and get an answer grounded in your actual documents, with citations back to the source so it can be verified rather than trusted blindly. We build the retrieval and grounding carefully so the system says "I don't know" rather than confidently inventing an answer when the documents don't contain one. It solves the real cost of tribal knowledge walking out the door and new hires or support staff burning hours searching for answers that already exist somewhere in the archive.

This is a capability we deliver for you as a service, not a role we’re hiring for.

How We’d Approach This

A clear, staged plan, not a black box

These are the service-specific steps inside our six-step process.

  1. 1

    Inventory the document corpus and interview frequent searchers to learn the real questions people ask and how they judge a good answer

  2. 2

    Pilot a retrieval-and-answer system against a subset of the corpus, measured against a hand-built set of known question-answer pairs

  3. 3

    Review pilot answers with subject-matter experts to catch ungrounded or incomplete responses before expanding coverage

  4. 4

    Build out full-corpus coverage with source citations, confidence signals, and a refresh process for keeping the index current as documents change

What You Get

Deliverables from this engagement

  • Production document Q&A system with source citations on every answer
  • Evaluation set of known question-answer pairs and accuracy results
  • Refresh/reindexing process for keeping the corpus current
  • Access layer (chat interface, Slack bot, or API) fitted to how your team already works

Six Ways We Could Architect This

Different engagement, different build, pick the shape that fits

There’s more than one way to deliver on this service. Browse a few of the ways we’d structure the work, depending on your speed, budget, and integration needs.

Read all 6 architectures as text

Lean Fast-Start Architecture

Document Ingest

  • Internal Document Corpus
  • Simple Text Extractor
  • Chunking Script

Index Core

  • Embedding Model Call
  • Local Vector Store

Query Loop

  • User Question
  • Retrieve Top Chunks
  • LLM Answer Generation
  • Answer with Source Citation

Delivery

  • Simple Chat Interface
  • User Reads Answer and Source
  • Feedback Note Captured

Cloud-Native / Managed Services Architecture

Managed Storage Layer

  • Document Blob Storage
  • Managed Text Extraction Service
  • Chunking Function

Managed Index Layer

  • Managed Embedding API
  • Managed Vector Database
  • Index Refresh Schedule

Serving Layer

  • User Question via API
  • Managed Retrieval Service
  • Managed LLM Endpoint
  • Grounded Answer with Citations

Access Layer

  • Chat Interface or Slack Bot
  • Usage and Latency Monitoring
  • Managed Autoscaling Backend

Event-Driven & Automation-First Architecture

Triggers

  • New Document Uploaded
  • Ingestion Webhook
  • Document Updated or Deleted
  • Reindex Webhook

Automated Pipeline

  • Auto Extract and Chunk Job
  • Auto Embedding Job
  • Vector Index Auto Updated

Query Automation

  • User Question Event
  • Auto Retrieval Workflow
  • Confidence Above Threshold
  • Auto Answer with Citations
  • Auto Fallback I Do Not Know Response

Notification Layer

  • Answer Delivered to User
  • Flag Sent to Content Owner

Enterprise-Grade & Compliance-Ready Architecture

Governance Layer

  • Document Access Policy
  • PII and Sensitive Content Scan
  • Role Based Permission Mapping

Secure Index Layer

  • Encrypted Document Store
  • Access Controlled Vector Index
  • Redundant Index Replica

Auditable Question Answering

  • Authenticated User Question
  • Permission Filtered Retrieval
  • Grounded Answer Generation
  • Citation and Confidence Log

Compliance Layer

  • Full Query and Answer Audit Trail
  • Retention and Deletion Schedule
  • Quarterly Compliance Review

AI-Augmented, Human-in-the-Loop Architecture

Corpus Ingestion

  • Policies Manuals and Contracts
  • Chunked and Embedded Index

AI Answer Layer

  • User Question
  • AI Retrieval Engine
  • AI Drafted Answer with Sources
  • Confidence Score Check

Human in the Loop

  • Subject Matter Expert Review Queue
  • Answer Grounded and Accurate
  • Correction Sent Back to Index
  • Human Approved Answer

Delivery

  • Answer Shown with Citations
  • Usage Logged for Model Improvement

Integration-Heavy / Ecosystem-Connected Architecture

Client Systems

  • SharePoint and Confluence Docs
  • Contract Management System
  • Support Ticketing System

Question Answering Core

  • Unified Document Connector
  • Retrieval and Grounding Engine
  • Answer with Source Citations

Two Way Sync Layer

  • Slack Bot Interface
  • Microsoft Teams Interface
  • CRM Case Notes Sync
  • Helpdesk Ticket Auto Reply

Shared Governance

  • SSO and Access Control
  • Published Answer Analytics

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Ready to get started?

Tell us what you’re trying to get done and we’ll help you find the highest-leverage place to start, scoped small enough to prove itself before you commit to anything bigger.

Talk to us about Document Question-Answering Systems

Most engagements like this start as a $500–$2,500 pilot, see full pricing.

Questions? Book a free call