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🛠️ Software Development automation

Intelligent automation for fast, scalable delivery

AUTOMAIT connects your issue tracker, repo, CI/CD and monitoring stack so engineers can build instead of babysitting pipelines.

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The problem

DevOps tools automate pieces — AI connects the lifecycle

Feature requests, bug fixes, code reviews, testing and deployments — all while keeping systems stable and customers happy.

Traditional DevOps tooling automates parts of the puzzle, but it falls short on reducing repetitive work, predicting risk and connecting workflows across the whole lifecycle.

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Tickets written from scratch every time

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Test coverage that lags the code

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Risky manual deployments

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Incidents found by users, not monitors

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Post-mortems that never get written

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Recurring CI failures nobody analyses

Plan & Build → Deploy & Run → Learn & Improve

How it works, stage by stage

1

Plan & Build

Requirements, backlog management and boilerplate coding create bottlenecks. AI speeds up planning and accelerates delivery by drafting tickets, generating boilerplate and creating automated tests.

Result

Engineers start from a draft instead of a blank page.

Workflow examples

Request triageFeature request intake → AI (summarise & prioritise) → Jira (create ticket)
Test generationPull request trigger → AI (generate unit tests) → CI (run tests)
Shift-left securityCommit trigger → AI (scan for bugs/security issues) → Slack (alert dev)

Live example — Patch summary to Slack

Manual trigger or webhook→Fetch software titles from Jamf Pro→Filter target software→Retrieve patch summary (latest version, up-to-date, out-of-date)→Format summary using Slack Block Kit→Post to Slack channel for IT / security teams
2

Deploy & Run

Deployment and monitoring can be risky and manual. AI predicts issues before they reach production, automates deployment and rolls back the moment anomalies appear.

Result

Releases stop being an event.

Workflow examples

Validated deploysMerge to main → AI (validate build) → Kubernetes (deploy)
Auto rollbackMonitoring trigger → AI (detect anomaly) → GitOps (rollback)
Faster incident responseAPI spike → AI (root cause analysis) → PagerDuty (alert team)
3

Learn & Improve

Post-mortems and retrospectives get skipped when everyone is busy. AI analyses past performance, generates summaries and suggests improvements — turning every sprint into a smarter one.

Result

Institutional knowledge gets written down automatically.

Workflow examples

Automatic RCAIncident closed → AI (generate root cause analysis) → Confluence (publish report)
Sprint insightSprint end → AI (summarise blockers & recommendations) → Jira (add insights)
Pipeline healingCI/CD history → AI (analyse failures) → GitHub (open config fix PR)
Keep exploring

Other departments we automate

🎯

Sales

Prospect to closed-won, on autopilot

📣

Marketing

Plan, produce, publish and prove ROI

💬

Customer Support

Faster answers on every channel

👥

HR & People

Hire, onboard and pay without the admin

🧾

Accounting & Finance

Zero-touch invoices and reconciliation

📦

Operations

Plan, execute and optimise the supply chain

🔗

Cross-System

Make every tool speak the same language

Ready to automate software development?

Book a 30-minute discovery call. We'll map your current workflow and show you the fastest quick win.

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