AI for Software Engineering

AI Tools and Systems for Engineering Teams

AI systems that operate alongside your engineering workflow — reviewing code, generating tests, maintaining documentation and analysing incidents — so your team ships faster with fewer quality escapes.

Engineering Friction

Engineering Time Lost to Tasks AI Can Handle

Engineering teams spend significant cycles on work that is important but not uniquely human: reviewing code for obvious patterns, writing tests for new functions, keeping documentation current and digging through logs during incidents.

AI does not replace engineering judgment — it handles the systematic, pattern-matching work that consumes time without requiring creativity. The right AI tools accelerate cycle time and raise the quality floor without adding management overhead.

Code review bottlenecks slow PR merge time
AI pre-review on every PR, human review for judgment calls
Tests written by the same person who wrote the code
AI generates tests from a different perspective to the author
Documentation falls behind with every sprint
Automated documentation generation on merge
Sprint planning is inconsistent across teams
Estimation assistance with historical pattern matching
Incident response is slow and undocumented
AI triage surfaces likely causes from logs in minutes
AI Systems

What We Build

Each system integrates with your existing engineering toolchain — GitHub, GitLab, Jira, your CI pipeline and your documentation platform.

AI Code Review

Automated code review that catches logic errors, security anti-patterns, performance issues and deviations from your coding standards. Runs on every pull request. Configured against your specific guidelines, not generic best practices.

PRsSecurityPerformanceStandards

Automated Test Generation

Test case generation for new code paths, edge cases and regression scenarios. AI analyses the function signature, implementation and existing test patterns to generate tests that match your codebase style.

Unit TestsIntegrationEdge Cases

Documentation Generation

Keep documentation current with code changes. AI generates and updates function documentation, API references, architecture decision records and internal wikis from the codebase — on commit or on schedule.

ADRsAPI DocsWikiInline Docs

Sprint Planning Assistant

An AI assistant that helps engineering leads scope, estimate and prioritise sprint work. Reviews ticket descriptions, identifies dependencies, flags underspecified requirements and suggests story-point estimates based on historical patterns.

EstimationDependenciesPrioritisation

Incident Analysis

AI-assisted incident triage and post-mortem analysis. Correlates logs, metrics, deployment events and previous incidents to surface likely root causes and draft post-mortem documents from structured incident data.

Root CausePost-MortemLog Analysis
Integration

Works with Your Engineering Stack

We integrate with your existing toolchain. No platform migration, no new interfaces for your team to learn.

GitHub
GitLab
Jira
Linear
Confluence
Notion
PagerDuty
DataDog
Slack
CI/CD Pipelines
VS Code
Custom APIs
Get Started

Where Does Your Engineering Team Lose the Most Time?

Tell us about your stack, your team size and the specific friction points. We will scope an AI system that fits your engineering workflow.