I’m building a search layer on top of Confluence, Slack, and Jira. What’s the simplest approach?

The simplest approach is:

1) Don’t build a “federated search engine” first

Instead, build a single index that ingests content from Confluence, Slack, and Jira, then search that index.

Why this is simplest:

• one query path
• one ranking model
• one permissions model to enforce
• one place to add filters like source, author, date, project, channel, space

A federated approach (query each system live) is usually harder because you must merge results, normalize ranking, handle latency, and deal with inconsistent APIs.

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2) Use the sources’ APIs to pull data incrementally

Create small connectors:

• Confluence: pages, blogs, comments, title, body, space, labels, updated_at, permissions
• Slack: messages from selected channels, thread replies, channel metadata, timestamps, users, permissions
• Jira: issues, comments, summary, description, status, project, labels, assignee, updated_at, permissions

Store each item as a normalized document like:

{
  "id": "slack:channel:ts",
  "source": "slack",
  "title": "thread starter or derived title",
  "body": "plain text content",
  "url": "...",
  "updated_at": "...",
  "authors": ["..."],
  "tags": ["..."],
  "acl": ["user/group ids allowed"],
  "metadata": {
    "space": "...",
    "channel": "...",
    "project": "...",
    "issue_key": "..."
  }
}

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3) Pick a search backend that gives you full-text search + optional vector search

For the simplest production setup, choose one of:

• Elasticsearch / OpenSearch if you want classic search, filters, highlighting, and decent operational maturity
• Postgres + pgvector + full-text search if the corpus is small and you want to keep infra minimal
• Typesense / Meilisearch if you want a very lightweight keyword-first experience

If you want “best simple choice” for enterprise search across these tools, I’d usually pick:

• OpenSearch if scale and search relevance matter
• Postgres if you’re prototyping or corpus is modest

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4) Enforce permissions at index time or query time

This is critical.

Simplest workable method:

• store ACL info on every document
• when a user searches, filter results by their allowed user/group IDs

Do not ignore permissions and try to patch later.

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5) Start with keyword search, then add semantic search later

The minimum useful product is:

• query string search
• filters by source/date/project/space/channel
• highlight snippets
• sort by relevance and recency

Then later:

• embeddings for semantic search
• reranking
• query expansion
• “best answer” synthesis

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6) Recommended MVP architecture

A simple architecture:

1. Connectors

   • fetch via API/webhooks
   • incremental sync on a schedule

2. Normalizer

   • convert all content into one document schema

3. Index

   • OpenSearch / Postgres / Typesense

4. Search API

   • handles auth
   • adds ACL filters
   • supports filters and pagination

5. UI

   • single search box
   • source filter chips
   • result previews

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7) The easiest implementation path

If you want the fastest path with lowest complexity:

Option A: Fastest MVP

• Postgres
• tsvector for full-text search
• store ACL arrays in JSONB
• simple background sync jobs

Best if you have:

• small-to-medium data volume
• need to ship fast
• limited ops team

Option B: Best long-term simple choice

• OpenSearch
• API connectors
• ACL-based filtering
• later add embeddings

Best if you have:

• lots of docs/messages
• need relevance tuning
• want room to grow

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8) What not to do

Avoid these early:

• live-querying Slack/Confluence/Jira on every search
• building custom ranking before you have usage data
• trying to unify every object type perfectly
• skipping ACLs
• ingesting everything without incremental sync or deduping

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Bottom line

Simplest approach:
Build a central search index with small connectors for Confluence, Slack, and Jira, normalize content into one schema, and enforce permissions in the search layer.

If you want, I can sketch:

1. a minimal data model,
2. the sync architecture, or
3. a recommended stack for your expected scale.