Action Item Automator

For

Team leads at consulting firms and project-based service companies

Solves

Action items from meetings get lost in the hand-off from conversation to execution.

Delivers

Assigned tasks with due dates, meeting summary, and automated reminders

Built in

about 2 weeks of creation time, MVP in 2 days

Investment

$5,500 for the MVP, $17,000 for the full product

Run it

Inside your business, or as part of your offer to clients

For team leads at consulting firms and project-based service companies, turn meeting recordings and transcripts into assigned tasks, due dates, and follow-up reminders in existing project tools.

1. Upload or record meeting audio.
2. Transcribe and parse for action items, decisions, and owners.
3. Review and edit extracted tasks in a queue.
4. Sync approved tasks to project tools with due dates.
5. Post summaries to team channels.
6. Send reminders for overdue tasks.

What goes in, what comes out

The workflow

1. In
   Start with

   Meeting recordings or audio files

2. 1

   Upload recording

3. 2

   Transcribe

4. 3

   Parse for actions

5. 4

   Review tasks in queue

6. 5

   Approve and sync to project tool

7. 6

   Post summary to channel

8. 7

   Then send reminders for overdue items

9. Out
   Finish with

   Assigned tasks with due dates, meeting summary, and automated reminders

AI does the heavy lifting, people stay in charge

Use language models to transcribe and extract action items, decisions, and named owners. A human reviewer checks and edits the extracted tasks before they sync, especially for client-facing work, to avoid misinterpretation of ambiguous names or jargon.

What your team sees

Key screens: Meeting list, transcript review, task creation queue, reminder dashboard. Use a meeting list for recent recordings, a transcript view with highlighted action items, a queue for reviewing and confirming tasks before they sync, and a dashboard showing open tasks and reminders. The main screen is the task creation queue, where users approve or edit extracted actions before they go live.

Accounts and administration

User roles, meeting ownership, task approval states, sync logs, reminder settings, and an audit trail of all extracted and edited items.

Integrations and data access

Zoom, Teams, Google Meet for recordings; Jira, Asana, Notion for task creation; Slack and Teams for summaries and reminders.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   2 days

   One buyer segment, one recurring use case; first modules: transcription and task extraction, plus manual review queue. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   3 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   6 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will team leads at consulting firms and project-based service companies use it to solve "action items from meetings get lost in the hand-off from conversation to execution"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. A paid pilot with one consulting firm processes 20 client meetings.
4. Measure, then decide. Track time saved per meeting on task creation and follow-up. Percentage of tasks correctly extracted without human edits. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. One buyer: team leads at a small consulting firm. One use case: client-facing project meetings. First two modules: transcription and task extraction, plus manual review queue. Sync to one project tool (e.g., Jira) with manual approval before tasks go live.

After the MVP. After the paid pilot, automate the reminder nudges, add Slack/Teams posting, and expand to multiple project tools and meeting types without manual review for internal meetings.

What the build depends on. The build needs reliable speech-to-text APIs, a language model for parsing, and API access to project management and communication tools.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test pricing at USD 99 per user per month, with a setup fee of USD 500 for integration and training.

Message to test

Turn every meeting into assigned work without lifting a finger.

Where to find buyers

LinkedIn, industry conferences for consulting firms, and partnerships with project management tool vendors.

Lead magnet

A free demo where we process one recorded meeting and show the extracted tasks versus manual notes.

The first 30 days

1. Week 1: Set up transcription and parsing pipeline.
2. Week 2: Build review queue and task sync to one tool.
3. Week 3: Add summary posting and reminder logic.
4. Week 4: Test with two client meetings and refine extraction accuracy.

Paid pilot

A paid pilot with one consulting firm processes 20 client meetings. Baseline is the time spent on manual note-taking and task creation. Success is a 50% reduction in that time with at least 90% of tasks correctly extracted. Decision to continue based on accuracy and time saved.

Success metrics

Time saved per meeting on task creation and follow-up. Percentage of tasks correctly extracted without human edits.

Retention and expansion

It keeps earning through monthly subscription, with value increasing as the model learns the client's jargon and meeting patterns, reducing review time further.

Why clients would pick it

Each correction and approval trains the model on the client's specific jargon and meeting patterns, creating a customised extraction engine that is hard to replicate without the same data.

Alternatives and positioning

People use manual note-taking, transcription tools like Otter.ai, or project management templates. This differs by automating the entire loop from recording to task creation and follow-up, not just transcription.

Main delivery costs

Main delivery costs are AI transcription and language model API usage, plus integration development and ongoing support.

Limit access to approved users only. Require human approval before tasks sync to external tools. Never send reminders without a task being approved. Must not auto-assign tasks without confirmation of the owner. Must not share meeting content outside the client's workspace.