Get AI-matched speaker recommendations for your event

Ask for session recommendations and AI matches speakers to your audience using past ratings and preferences.

How the work actually flows

A straight line.

Pattern: Sequence (1)

flowchart TD trig>"request for recommendations received"]:::trig s0[("pull speaker ratings and preferences")]:::store s1["AI builds optimized session lineup"]:::task s2["return recommendation summary"]:::task s3[("log recommendation to spreadsheet")]:::store trig --> s0 s0 --> s1 s1 --> s2 s2 --> s3 out[/"recommended speaker lineup delivered"/]:::out pay{{"better audience fit less guesswork"}}:::pay s3 --> out out --> pay classDef task fill:#e7f6fe,stroke:#34b8f0,color:#2c2a29 classDef svc fill:#f6f8fa,stroke:#7c8795,color:#2c2a29 classDef mi fill:#e7f6fe,stroke:#0079a8,color:#2c2a29,stroke-width:2px classDef human fill:#fff,stroke:#0079a8,color:#0079a8 classDef store fill:#f6f8fa,stroke:#0079a8,color:#2c2a29 classDef trig fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigtime fill:#00a4eb,stroke:#0079a8,color:#fff,font-weight:bold classDef trigdata fill:#8ad4f5,stroke:#0079a8,color:#06314c,font-weight:bold classDef gate fill:#fff,stroke:#e8a23d,color:#6b4708,font-weight:bold classDef out fill:#1f9d6b,stroke:#167a53,color:#fff,font-weight:bold classDef pay fill:#06314c,stroke:#021f33,color:#fff
Starts itA stepA record or sheetResultPayoff
Build size
Advanced

A larger build with multiple systems, AI reasoning, and custom rules.

Business functions
AI Agents & Autonomous SystemsSpreadsheet & Database OpsAPI & Webhook Integration
Connects
Google SheetsClaude

The problem it solves

Putting together a session lineup that actually fits your audience means digging through past speaker ratings, feedback, and preferences by hand. It is easy to end up guessing instead of matching the right speaker to the right crowd.

Who it fits

Event organizers and conference planners building a speaker lineup.

How it works

  1. A request comes in asking for session recommendations
  2. The automation pulls speaker ratings, past sessions, and audience preferences from a spreadsheet
  3. AI analyzes the fit between speakers and audience and builds an optimized lineup
  4. A recommendation is returned with a plain-language summary
  5. The recommendation and its details are logged to the spreadsheet for future reference
What you get

Speaker lineups matched to your audience

You get a ready-made speaker lineup matched to your audience's interests and past ratings, with a plain-language summary you can act on.

What you get

A recommended session lineup with a written summary, plus a saved record for tracking.

What you need

A Google Sheets account and an Anthropic Claude API key.

We can build this. But should you?

The hard question is not how to build it. It is whether this is the right thing to build first.

That is what a Fractional Chief AI Officer figures out with you, before anyone writes a line of code.

Let's Talk Strategy

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