A brainstorm produces more ideas than anyone can act on. The useful next step is a shortlist that preserves the good distinctions and shows why each idea deserves a test.

OGAD (Off Grid AI Desktop) can transcribe saved brainstorm audio and help organise the checked text with a local model. You supply the selection criteria, review the candidates, and choose what to try. After model setup, the speech and text processing can run on your own computer.

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For a small creative team, this gives a busy session a usable next step. You can return to an idea that was mentioned briefly, compare it with the others, and explain the shortlist without relying only on what people remember most clearly.

What should the shortlist preserve?

Keep the original idea, the problem it addresses, and any stated conditions. Group duplicates carefully. Two ideas that share a keyword may solve different problems, while the same idea may appear in several forms.

Suppose a studio brainstorms ways to help clients prepare for a recording session. Suggestions include a preparation checklist, a short example recording, and a call with the producer. All three concern preparation, but they ask different things of the client and team.

A useful review table keeps those differences:

Candidate	What you need to know
Preparation checklist	Which recurring mistakes would it prevent?
Example recording	What would the client learn by hearing it?
Producer call	Which questions require a conversation?

Do not let a summary collapse all three into “Improve preparation.” That removes the choice you need to make.

What do you need to process the session?

Use a saved audio file, a local speech model, and a local text model. The saved-file route is part of the free core app. It does not require Pro live meeting recording.

The model-setting steps below use an Apple Silicon Mac. Core audio attachments are also available on Windows, where speech runtime setup can differ. 0.0.54-beta.108 adds Linux beta packages.

Complete app, runtime, and model downloads before working offline. Keep the original recording and any whiteboard notes available. Spoken audio cannot capture an idea written silently or a diagram that participants only pointed at.

For a video recording, export its audio for this procedure. The normal video attachment path analyses sampled visual frames rather than transcribing its full soundtrack, as shown in the file-processing code.

How do you make a usable idea inventory?

Transcribe the audio first, then check it before asking for a shortlist. The first model task should be to organise what was proposed. Selection comes after you have a reasonable inventory.

1. Choose local transcription and text models in Models.
2. In model settings, open Transcription, choose Current model, and set Spoken language.
3. In chat, use + > Attach files to choose the saved audio.
4. Wait for processing and open the attachment’s text preview.
5. Check unusual terms and idea names. Copy the text into an editor for corrections, then attach the checked version.

Ask:

Extract proposed ideas from this brainstorm transcript. For each idea, give a short name, the problem it aims to solve, the proposed approach, and a supporting passage. Group clear duplicates but preserve meaningful alternatives. Keep objections and conditions beside the idea. Do not add new ideas yet.

Review the inventory against the recording. A short contribution near the end may be important even if nobody repeated it. Work section by section when the transcript is too long for the model’s available context.

How should you choose the selection criteria?

Choose criteria that match the test you can actually run. For a small team, useful questions often concern the reader or customer problem, the effort required, and the evidence you could collect. The AI should apply your criteria, not quietly invent the team’s priorities.

For the studio example, you might choose:

• Does this address a problem clients already report?
• Can the team test it with the next few sessions?
• What does the team need to prepare?
• What would show whether it helped?

These are questions for discussion, not measured scores. If the recording contains no estimate of production time, mark effort as unknown instead of allowing the model to produce a precise number.

Add any real constraints yourself, such as the available staff member or the format you can produce this week.

How do you create the shortlist?

Give the model the checked inventory and your criteria. Ask for reasons and uncertainties beside each recommendation. This makes the shortlist easier to challenge before the team invests time.

Propose a shortlist of three ideas from this reviewed inventory using the criteria below. Explain why each fits, what remains unknown, and the smallest useful test. Do not invent costs, audience demand, or performance estimates. Also identify one excluded idea worth keeping for later and explain the tradeoff.

For the preparation example, the model might recommend a checklist as a small first test. That recommendation still needs your judgement. Perhaps the repeated problem requires a conversation, in which case a checklist would not address it.

Ask a follow-up that tests the recommendation:

What evidence in the inventory would argue against this shortlist? Show the objections already raised and identify assumptions we should check before choosing.

Keep the final choice separate from the generated recommendation. The decision belongs to the team.

What should each selected idea contain?

Turn each selected idea into a short test brief. Include the intended user, the problem, the first version, and the observation you will make after trying it.

Test-brief field	Example question
User	Who will use this first?
Problem	What should become easier?
First version	What is the smallest useful thing to make?
Owner	Who has agreed to do the work?
Review	What will the team inspect after the test?

Only include an owner or deadline after someone accepts it. The brainstorm mentioning a person’s name is not enough to establish a commitment.

Save the shortlist with the reviewed inventory and transcript. If you later revisit a rejected idea, you can see the conditions that shaped the original decision.

What can go wrong in the summary?

Problem	How to correct it
Distinct ideas are merged	Ask for the user problem and approach of each alternative
A popular idea always ranks first	Apply explicit criteria and review the supporting evidence
New ideas appear as original suggestions	Separate extracted ideas from later model suggestions
Precise costs appear without evidence	Replace them with unknowns or your own estimates
The transcript misses quiet speech	Check the original audio before excluding the idea

Do not treat the number of mentions as a vote. A facilitator may repeat an idea to explain it, while another idea may be understood after one sentence.

Choose one test from the last brainstorm

Download OGAD and process one short session. Build a checked inventory, choose your criteria, and finish with one test brief the team can review.

Keep speech and text models local for the processing described here. Remote model choices and later sharing have separate network requirements. The useful result is a reasoned next experiment, not a claim that AI can predict which idea will succeed.