Autonomous
Engineering Intelligence

An autonomous AI agent that plugs into your Jira, Azure DevOps, GitHub and codebase — investigates tickets, writes code, ships pull requests and resolves defects end-to-end. Development, ticket triage, bug fixing, feature implementation — handled.

0%
Time Saved
0%
Cost Reduction
24/7
Availability
2–15min
Per Investigation

Autonomous engineering across every industry · click a domain to explore

From Ticket to Pull Request

Induktiv AI doesn't just fix bugs. It investigates, writes code, implements features and ships — autonomously.

🧠

Autonomous Investigation

Reads tickets, fetches logs of any type, analyzes code and correlates across Jira, Confluence, DOORS and Git — without a human in the loop.

Writes Real Code

Implements fixes, new features and refactors inside your repo. Creates branches, writes tests, opens pull requests with full reasoning and evidence.

🔗

Full-Stack Integrations

Jira, Azure DevOps, ServiceNow, GitHub, GitLab, Confluence, SharePoint, DOORS, Polarion, CodeBeamer, Grafana, Jenkins. Plugs into what you already run.

🎯

Root-Cause Reasoning

Multi-turn agentic loop with hypothesis testing, evidence accumulation and confidence scoring. 5–30 tool calls per ticket until the real root cause is locked down.

📊

Structured Reporting

17+ structured sections per investigation. Evidence timelines, recommendations, traceability — exported to Jira, Word or PDF for compliance.

🛡️

Enterprise-Grade Security

TLS 1.3 + AES-256. TISAX / ISO-ready. Zero-trust local AI option on-prem, SSO/LDAP/RBAC, full audit trail. Built for regulated industries.

How It Works

An event-driven pipeline built on six modular layers — from ticket trigger to merged pull request.

1

Trigger

Ticket created, label added, @mention or scheduled scan. Induktiv listens on Jira, ADO, ServiceNow, GitHub.

2

Orchestrate

Router pre-screens, fetches metadata, attachments, logs, linked tickets and code changes from every source.

3

Reason

Agentic loop: builds context, dispatches tools, evaluates evidence, iterates with confidence scoring.

4

Implement

Writes code, generates tests, creates a branch and commits the change directly inside your repository.

5

Report

Structured markdown with 17+ sections, evidence timeline, root cause, confidence score — in Jira, Word, PDF.

6

Deliver

Opens a pull request. Your team reviews and merges. 2–15 min end-to-end vs. 8–40h manual work.

Built For The World's
Most Demanding Codebases

From hypercar firmware to heavy-industry control systems — Induktiv AI is designed for regulated, mission-critical software where reliability is non-negotiable.

The Real Cost — Per Ticket

Every engineering defect costs ~€510 in manual investigation (6h × €85/h fully loaded). Induktiv AI takes it down to €84 — a saving of €426 per ticket.

Without Induktiv AI
€510 / ticket
6h avg investigation × €85/h fully loaded
  • Root-cause analysis2.5 h
  • Data collection1.5 h
  • Report writing1.0 h
  • Escalation & rework1.0 h
With Induktiv AI
€84 / ticket
45 min review × €85/h + €20 AI compute
  • AI investigation (auto)0 min
  • Engineer review30 min
  • Final validation15 min
  • Saving per ticket€426

Ready to Automate Engineering?

Book a 30-min discovery call and see Induktiv AI resolve a real ticket in your environment.