From fragmented tools to unified search: Building cross-tool indexing

Building a search interface that queries multiple tools simultaneously sounds straightforward: call each API, aggregate results, rank them. In practice, the challenges compound quickly. Each tool has a different data model, different authentication flows, different rate limits, and different notions of what “relevance” means.

A GitHub issue titled “Auth timeout” is related to a Jira ticket titled “Production: Authentication Service Failure” which was discussed in a Slack thread titled “incident-2026-03-14”. None of these share a key. None link to each other. But they’re all describing the same event — and when you search for it, you want all three.

Semantic linking without explicit keys

The core challenge in cross-tool search isn’t querying — it’s correlation. We use a combination of embedding-based semantic similarity and structural signals (PR numbers in ticket descriptions, ticket IDs in Slack threads, date windows of concurrent activity) to build a link graph across your connected tools.

When you search for “auth incident March”, the search doesn’t just find documents containing those words. It finds the cluster of related artifacts across your tools, ranks them by relevance and recency, and surfaces them as a unified result set.

Handling different data shapes

GitHub returns PRs and issues with structured metadata. Slack returns messages with thread hierarchy. Notion returns page trees with block content. Jira returns tickets with custom field schemas that vary by team.

We normalize these into a common artifact model — title, body, actors, timestamps, status, and tool-specific metadata — that the search layer can rank uniformly. Tool-specific context is preserved but presented consistently.

The result is that a single query can surface a PR, the ticket it implements, the design doc that specified it, and the Slack conversation where the tradeoff was debated — in one place, ranked by how closely each artifact matches your intent.