Technology
Your engineers copy-paste ticket data into AI 200 times a day.
Technology companies were early AI adopters, but most teams use it in isolation. Engineers copy Jira tickets into a chat window, paste Datadog alerts, and manually summarise support conversations. The AI can access some of these tools natively, but making it genuinely useful requires configuring workspace permissions, building integrations for internal tooling, enabling teams on effective workflows, and standardising how AI is used across departments. We build that platform model-agnostic, so you own it and can swap the underlying model as the market moves.
Where it breaks down
The problems we solve.
Each engineer uses AI differently
Some engineers are highly productive with AI. Most are not. Nobody has built standardised workflows for incident response, code review, or ticket triage. No enablement exists for effective AI usage specific to your stack and workflows. Individual skill varies wildly and so does the value delivered.
Internal tooling has no off-the-shelf connector
Modern AI platforms connect to Jira and GitHub natively. But your internal deployment dashboard, your custom monitoring stack, your proprietary CI/CD tooling? Those need custom integrations. The tools unique to your company are often the ones where AI would add the most value. We build them on open standards like MCP so they stay yours.
Support teams lack engineering context
Your support team uses Zendesk. Your engineering team uses Jira and GitHub. When a customer reports a bug, the support agent cannot see the related Jira ticket or the PR that caused it. We build the integrations that bridge these systems through the AI so support resolves issues without escalating to engineering.
What we build
From pilot to production.
Engineering assistant with full system access
Native and custom integrations connecting Jira, GitHub, and Confluence. Engineers ask about a ticket and see linked PRs, deployment history, related documentation, and past incidents. No copy-pasting. No tab switching.
Incident response with operational context
Custom integrations connecting PagerDuty, Datadog, and your runbook repository. On-call engineers ask the AI for service health, recent deployments, known issues, and resolution steps. Time to context drops from minutes to seconds.
Support-to-engineering bridge
Integrations connecting Zendesk to Jira and GitHub. Support agents ask for the status of a related bug, see the PR that fixes it, and get an estimated resolution timeline without escalating to engineering.
Systems we connect
We integrate with the tools you already run.
Start here
Book a call. We will show you what production AI looks like for your business.
Tell us where you are: stalled pilots, a platform you are evaluating, or teams you want building. We will map the highest-value use cases and show you a realistic path to production. No obligation, no slides.