AI AGENTS

Weekly RFP Answer-Library Coverage Gap Report

On a weekly schedule, an agent reviews recent RFP questions that scored low confidence, clusters the recurring gaps, and posts a prioritized content-to-write list to Slack.

CategoryAI Agents
Enginesim
Difficultyintermediate
Triggerschedule
Steps6
Setup~15 min

How it runs

The automated pipeline, trigger to output.

  • TriggerWeekly schedule fires
  • ActionRead past week of RFP questions and scoresPostgreSQLPostgres
  • LogicFilter to low-confidence and no-source questions
  • ActionCluster gaps and rank by frequency via OpenAIOpenAI
  • ActionConfirm no covering page exists in ConfluenceConfluenceConfluence
  • OutputPost prioritized content backlog to SlackSlack

What it does

Keeps your RFP knowledge library honest. Each week the agent looks back at questions the autoresponder answered with low confidence or no good source, clusters them into recurring themes, and reports which answer-bank entries are missing or stale so your enablement team knows exactly what to write next.

When to use it

Use it once you have an RFP autoresponder running and want a feedback loop that continuously improves the Confluence library instead of letting the same unanswerable questions resurface every quarter.

How it works

  1. 1A weekly schedule triggers the run.
  2. 2The agent reads the past week of logged RFP questions and their confidence scores from the answer store.
  3. 3It filters to low-confidence and no-source questions only.
  4. 4OpenAI clusters the filtered questions into themed coverage gaps and ranks them by frequency.
  5. 5The agent cross-checks the Confluence library to confirm no covering page already exists.
  6. 6It posts a prioritized 'content to write' list to the enablement Slack channel.

Set it up

What you configure once, before turning it on.

  1. 1
    Connect PostgresAny Postgres URL — query, write, migrate.
  2. 2
    Connect ConfluenceSpaces, pages, blueprints.
  3. 3
    Connect OpenAIModels, embeddings, files.
  4. 4
    Connect SlackChannels, DMs, threads, mentions.
  5. 5
    Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
  6. 6
    Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
  7. 7
    Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.

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