AI AGENTS
On-Demand Company Dossier from a Slack Request
Mention a company name in Slack and an agent researches its primary sources across the web, drafts a structured dossier.
How it runs
The automated pipeline, trigger to output.
- TriggerSlack command with company nameSlack
- ActionSearch company sourcesExa
- ActionCrawl site + top resultsFirecrawl
- LogicCheck source coverage threshold
- ActionSynthesize cited dossierOpenAI
- OutputReply in Slack threadSlack
What it does
An analyst types a company name in a Slack channel; the agent returns a one-page dossier — what the company does, recent moves, funding or financials it could verify, and open questions — every claim backed by a primary source it actually fetched and read.
When to use it
For deal teams, partnerships, and sales engineers who need a fast, sourced read on a company before a call. Replaces 30 minutes of tab-hopping with a single Slack message and a cited answer.
How it works
- 1A Slack slash command or mention with a company name triggers the run.
- 2Exa searches for the company's site, recent news, and official posts, returning candidate URLs.
- 3Firecrawl crawls the company's homepage and the top sources into readable text.
- 4The agent (OpenAI) synthesizes a dossier with sections for overview, recent developments, signals, and unknowns, attaching a citation to each claim.
- 5A logic step confirms at least the minimum sources were read; if too thin, it asks for more search depth.
- 6The dossier is posted back as a threaded Slack reply with collapsible source links.
Set it up
What you configure once, before turning it on.
- 1Connect SlackChannels, DMs, threads, mentions.
- 2Connect ExaNeural search across the web.
- 3Connect FirecrawlCrawl, scrape, structured extract.
- 4Connect OpenAIModels, embeddings, files.
- 5Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 6Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 7Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More AI Agents workflows
Custom Metrics Cardinality Spike Pager
A webhook from a Datadog monitor fires when custom-metric cardinality jumps; an agent pinpoints the offending metric and tag, estimates the added cost.
Sentry-to-Confluence Runbook Updater
When a Sentry issue is resolved, the agent finds the matching Confluence runbook page and proposes an inline update with the verified fix.
Stale Doc-PR Chaser for Runbook Gaps
On a daily schedule the agent finds runbook doc PRs that were opened from resolved incidents but never reviewed, summarizes what each one fixes.
Resolved Incident to Public Troubleshooting Doc
For customer-facing errors resolved in Sentry, the agent drafts a sanitized troubleshooting entry and opens a PR to your ReadMe documentation.
On-Call Runbook Gap Closer: Resolved Sentry Issues to Doc PRs
An agent reads each newly resolved Sentry issue, compares the actual fix against your existing runbook, and opens a GitHub PR adding the missing remediation steps.
Weekly On-Call Doc-Gap Digest
Each week the agent reviews every Sentry issue resolved in the last 7 days, ranks the ones whose runbook coverage is missing or thin.
Run it inside a business
This workflow drops into a full company template. Import the org, and this is one of the playbooks its agents run.
Run this workflow in your colony.
14-day trial. No DevOps. No Sales call. Provisioned in under a minute.