MARKET RESEARCH
Agent-authored JTBD research brief in Confluence
On a monthly schedule, an agent reads the full interview corpus, synthesizes the dominant jobs-to-be-done with evidence and gaps.
How it runs
The automated pipeline, trigger to output.
- TriggerMonthly schedule
- ActionLoad full theme corpusAirtable
- ActionAgent synthesizes JTBD briefOpenAI
- LogicHold if corpus too sparse
- OutputPublish brief to ConfluenceConfluence
What it does
Produces a stakeholder-ready research brief from the whole corpus, not a single call. An agent reads every logged interview theme and its quotes, reasons across them to identify the dominant jobs-to-be-done, where the evidence is strong versus thin, and which questions remain open, then writes a structured narrative brief and publishes it to Confluence each month.
When to use it
Use it when leadership needs a periodic, defensible synthesis of what customers are telling you, with cited evidence and explicit gaps, rather than a raw theme list. Best when your corpus is large enough that manual synthesis is the bottleneck.
How it works
- 1A monthly schedule triggers the run.
- 2The agent loads the full set of interview themes and quotes from the Airtable corpus.
- 3The agent synthesizes dominant jobs, ranks evidence strength, and flags open questions, iterating as it reasons over the data.
- 4A logic step holds publication if corpus coverage is too sparse for a credible brief.
- 5The finished narrative brief is published as a Confluence page for stakeholders.
Set it up
What you configure once, before turning it on.
- 1Connect AirtableBases, tables, views, automations.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect ConfluenceSpaces, pages, blueprints.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
More Market Research workflows
Hiring Surge Detector with Slack Alert
Detects when a target account's open-role count jumps above its recent baseline and posts a ranked Slack alert to the GTM channel so reps can act on a company that is clearly…
Tech-Stack Shift Inference from Job Descriptions
Reads new job descriptions for target accounts, uses an LLM to extract named technologies and infer stack changes.
Enrich Inbound Accounts with BigQuery Firmographics and Score Fit
When a new account row lands in Airtable, joins it against BigQuery public business datasets to attach firmographic attributes.
Blend BigQuery TAM with Live Competitor Signals into a Notion Brief
On demand, sizes a chosen segment from BigQuery public data, gathers current competitor signals via Brave Search, and synthesizes a one-page market brief into Notion.
Allocate Sales Territory TAM from BigQuery Geo Data to HubSpot
When triggered by a webhook, queries BigQuery public ZIP-level business data to compute TAM per sales territory.
Discover Adjacent Segments from BigQuery and Rank Expansion Bets
On a schedule, scans BigQuery public industry data for fast-growing NAICS sectors adjacent to your core segments, scores them as expansion bets.
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.
