Engineering

Document AI for engineering and software teams

In short

RootNia gives engineering and software teams one way to query specifications, manuals, drawings, Confluence pages and Jira issues in plain language. It sorts documentation into private, public and technical tiers, answers with RAG citing the exact source and lets you query databases with NL-to-SQL — without anyone needing to know where each document lives.

Last updated · 2 October 2026

The problem: technical documentation lives in too many places

Specifications in SharePoint, architecture decisions in Confluence, the context of a bug in a Jira issue, vendor manuals as PDFs and operational data in a database. Answering “why was this parameter changed in version 3?” requires knowing where to look and whom to ask.

How RootNia solves it

Connected sources: SharePoint, OneDrive, databases and technical documentation (Jira and Confluence) come in through the same orchestrator.
Access tiers: documentation is separated into private, public and technical from ingestion.
Mixed formats: MIKA interprets tables, figures and scans.
Answers with sources: RAG retrieves the context and every answer links to the source document.
Data without SQL: NL-to-SQL turns questions into database queries.

Common use cases

Onboarding new hires with cited answers over internal documentation.
Second-line support: finding previous issues and fixes.
Product engineering: traceability between requirements, specifications and changes.
API integration into internal portals or support tools.

Example: reconstructing why something changed

Illustrative example of how it works, not a specific customer.

An engineer asks: “Why was the connection timeout changed in version 3, and which issue triggered it?”

RootNia searches the technical tier: Confluence pages, Jira issues and specifications in SharePoint.
RAG retrieves the architecture decision, the related issue and the updated specification.
The answer explains the reason for the change and links to each document in chronological order.
If the question involves operational data, NL-to-SQL queries the relevant database.

Getting started

Fast: Starter connects up to 3 document sources (up to 50,000 documents) and is running in 48–72 h.
Growing teams: Business expands to 10 sources, governed document tiers and SSO SAML 2.0, in 1–2 weeks.
Integration: API mode brings answers into your portals and internal tools.

One pipeline, your model of choice

RootNia is model-agnostic: connect ChatGPT, Claude, Kimi or others and switch models without rebuilding the pipeline. See how it differs from using ChatGPT or Claude directly.

Frequently asked questions

Does it integrate with Jira and Confluence?

Yes. Technical documentation, including Jira and Confluence, is handled as the technical tier from ingestion, with its own access control, alongside SharePoint, OneDrive and databases.

Can it read drawings and technical tables?

MIKA interprets text, tables, images and scanned pages together, so specification tables and documents with figures become part of the same knowledge layer.

Is there an API to embed it in our tools?

Yes. API mode lets you call RootNia's orchestration, extraction and RAG engine directly from your own products and internal tools.