AI Meeting Intelligence Platform

Problem and target customer

Why this exists

Customer problem

Teams already have video-call recordings but leave them unprocessed because transcription, note cleanup, and action extraction take too long. A generic summary is not enough when users cannot verify it against the recording or recover a failed job.

Who pays

Small project and client teams that receive recorded video calls and want a simple post-call processing tool without a calendar-wide knowledge system.

Business model

Subscription with an included pool of processed recording minutes and metered overage.

Editorial note

This product begins with a file, not a calendar. That narrower boundary makes resumable upload, visible stage progress, and retry correctness more important than organization-wide meeting discovery.

A trustworthy call package preserves the path from edited summary or action back to the exact transcript moment. Publishing should capture the human-reviewed version rather than silently replacing it after a regeneration.

The beta succeeds when ordinary coordinators can recover from an interrupted upload or failed provider and still finish a useful result. Calendar sync and cross-meeting memory belong to a different, broader workflow.

Choose your next step

What do you need next?

Evaluate the operating tradeoffs quickly, or inspect how to build the MVP.

Evaluate this ideaSee the cost and deployment preview

Compare the three operating paths at the default workload before opening the full calculator.

See cost preview Build this ideaInspect the build blueprint

Review MVP scope, workflow, states, data rules, architecture, and acceptance criteria.

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Evaluation preview

What would it take to run?

Directional infrastructure estimates for the current 6,000 recording minutes assumption. Open the full calculator when you are ready to change it.

Open full cost and deployment

ManagedSelected

$22-$56/ month

Managed app, database, queue, and workers for upload-first call processing.

Lowest operating effort

Lean self-hosted

$24-$54/ month

One operator-managed server runs the application and staged jobs.

Lowest baseline cost

More control

$57-$131/ month

Separate application, database, and worker capacity for concurrent files.

Most separation and control

Build blueprint

Build the first paid use case

Product goal

Who it serves and what it must change

Target user: A project coordinator or consultant processing a recorded call into a usable follow-up package.
Problem: Large uploads fail, processing is opaque, and generated notes cannot be trusted when claims are detached from transcript timestamps.
Measurable outcome: A user uploads one supported recording, receives a speaker transcript and cited draft, corrects assignments, publishes the result, and exports it without administrator intervention.

MVP scope

What ships now and what waits

Included

Resumable audio and video upload
File and duration validation before billing
Speaker-aware transcription synchronized to playback
Draft overview, topics, decisions, and actions with timestamp citations
Human edit, assignment, publish, and export
Stage-level retry without repeating successful work

Excluded

Calendar connection
Recurring-series library
Meeting bot
Live transcription
Automatic external task creation

UX and user flow

Screens, actions, and states

New Recording

Collect the file, title, language hint, consent confirmation, and optional participant labels.

Select fileConfirm consentResume uploadStart processing

Processing Status

Expose upload, transcription, extraction, and indexing stages independently.

View progressRetry stageCancel job

Call Review

Synchronize recording playback, speaker transcript, and cited draft content.

Jump to timestampRename speakerEdit summaryReject artifact

Action Review

Turn extracted tasks into confirmed assignments before publication.

Assign ownerSet due dateConfirm actionRemove draft

Published Result

Present the reviewed call package and supported exports.

Copy summaryDownload textTrigger approved handoff

Primary flow

User creates a processing job, confirms they may upload the recording, and completes a resumable direct upload.
The server validates format and duration, then a worker produces timestamped speaker segments.
A second worker drafts structured artifacts using segment IDs and rejects unknown citations.
The user corrects speakers and content, confirms action owners, and publishes a locked version.
The published result can be copied, downloaded, or sent through an explicit optional handoff.

Loading, empty, and error states

Draft job
Uploading
Upload paused
Queued
Transcribing
Extracting
Needs review
Published
Failed
Cancelled

Core entity outline

Entities and business rules

ProcessingJob

The ownership, file, usage, and stage boundary for one recording.

Fields: owner_id, title, language_hint, storage_key, duration_seconds, status, consent_at, current_stage, failure_code
Relations: Has one Transcript, Has many ArtifactVersions

Transcript

A provider result normalized into a versioned speaker transcript.

Fields: job_id, provider_job_id, version, status, detected_language
Relations: Has many TranscriptSegments

TranscriptSegment

A timed unit of speech used for playback navigation and citations.

Fields: transcript_id, start_ms, end_ms, speaker_label, text, confidence
Relations: May support many Artifacts

Artifact

One overview point, topic, decision, or action in the current review set.

Fields: job_id, artifact_type, text, review_status, owner_label, due_at, source_segment_ids
Relations: Belongs to one ProcessingJob, References TranscriptSegments

PublishedPackage

An immutable reviewed snapshot used for export.

Fields: job_id, version, published_by, published_at, export_status
Relations: Belongs to one ProcessingJob, Snapshots accepted Artifacts

Business rules

Unsupported format, excessive duration, or missing consent blocks processing before metered services are called.
A stage retry reuses prior successful output and is idempotent by job ID and transcript version.
Generated artifacts must cite segments from the job's current transcript version.
Actions remain Draft until a user confirms their wording and owner.
Publishing creates an immutable package; later edits require a new version rather than rewriting an exported result.

Architecture and data flow

Components, integrations, and controls

Upload and review app

Create jobs, resume uploads, show stages, synchronize playback, and collect review changes.

Job API

Validate files, authorize signed object access, meter accepted duration, and orchestrate stages.

Transcription worker

Submit private media, poll or receive callbacks, and persist normalized segments.

Extraction worker

Produce structured artifacts and validate their segment citations.

PostgreSQL and private storage

Persist job state and reviewed output while keeping recording bytes behind signed URLs.

Integrations

AssemblyAI pre-recorded transcription
LLM API for structured call artifacts
Cloudflare R2-compatible storage
Optional Make webhook for published exports

Data flow

The browser uploads chunks directly to a private object key scoped to one draft job.
After validation, the job reserves processed minutes and queues transcription.
Transcript completion queues extraction with ordered segment IDs and bounded text.
Human edits and confirmations are saved separately from model output; publication snapshots the approved fields.

Failure handling

Resume an interrupted upload from acknowledged chunks rather than restarting the file.
Keep uploaded media after a provider failure and allow retry from the failed stage.
Reject extraction output with missing or cross-job citations while preserving the transcript for manual notes.
Record export failure separately so a Published package remains downloadable.

Security

Require recording consent confirmation and document retention before accepting processing.
Use private storage, expiring media URLs, signed callbacks, and server-side ownership checks.
Remove provider URLs from persisted public results and avoid placing recording content in logs.
Allow owner deletion of source media and derived packages according to retention policy.

Rate limits

Limit file size, duration, concurrent uploads, and active processing minutes per workspace.
Deduplicate provider submission by stage key and cap retry attempts.
Back off on transcription and model rate limits without moving a job backward to Uploading.

Deliverables and acceptance

Definition of done for the MVP

Deliverables

Resumable direct upload and validation
Visible staged processing with idempotent retries
Synchronized transcript, citation, and artifact review
Action confirmation and immutable publication
Text download plus optional approved webhook export

Acceptance criteria

A supported interrupted upload resumes without duplicating the stored object or processing charge.
Every generated artifact opens a valid timestamp in the current transcript.
A user can publish only after all retained actions have confirmed wording and ownership.
Retrying extraction does not retranscribe a successful transcript.
If extraction fails twice, the transcript remains usable for manual summary creation and later publication.

Recommended stack

Enough technology for the first version

Web application

Next.js and Tailwind CSS

Support direct upload, processing status, synchronized review, and export in one product.

Data

PostgreSQL

Keep job stages, transcript versions, citations, review changes, and published snapshots transactional.

Jobs

BullMQ and Redis

Make transcription and extraction independently retryable.

Media

Cloudflare R2

Store large recordings privately and serve temporary playback links.

Processing

AssemblyAI and OpenAI adapters

Separate speech recognition from structured artifact drafting and preserve a replaceable provider boundary.

Why this is sufficient

This version deliberately avoids calendar and recurring-series complexity. Its engineering focus is reliable large-file processing, timestamp provenance, and recovery from partial failures for one recording at a time.

Not required for the MVP

Calendar APIMeeting attendance botVector databaseReal-time transcriptionNative mobile app

Next stepTurn the blueprint into an execution plan

Copy the build prompt, model the operating cost, and choose where to deploy.

Build and ship

Execution

Build, price, and deploy the MVP

Once the blueprint is clear, use the prompt, cost model, and deployment options to start building.

Build prompt

Copy this into a builder

Build a Next.js application with direct private uploads, PostgreSQL, a staged worker queue, transcription and LLM adapters, and timestamp-preserving review screens.

Build prompt

Your build prompt is ready

Open the prompt pack whenever you are ready to take this blueprint into your builder.

Based on the blueprintReady for your builderFollow-up steps included

Cost calculator

Model the MVP operating cost

A technical run-cost estimate for the first production version. Team, acquisition, payment fees, and business COGS are excluded.

Estimated monthly total$22-$56

$3.61-$9.29 per 1,000 recording minutes

Recording minutes processed / month6,000 recording minutes

Selected pathEasiest

Pricing checkedAug 2, 2026

Cost breakdown

$22-$56 per month

Low and high values allow for usage variance and plan headroom.

Managed media processor

Managed app, database, queue, and workers for upload-first call processing.

20K recording minutes included, then $5-$15 per 20K recording minutes

$5-$25

Recording transcription

Pre-recorded speech-to-text with speaker diarization, billed here per 60 uploaded minutes.

0 included, then $0.15-$0.23 per 60 recording minutes

$15-$23

Call artifact generation (GPT-5.4 mini)

Transcript input and structured overview, decision, and action output per processed minute.

6K recording minutes x 250 input + 40 output tokens

$1.65-$2.76

Recording storage

Private source recording retention and object operations during beta.

Monthly allowance from this idea's operating profile

$0-$5

Included

Application, database, queue, and worker hosting
Transcription
Artifact generation
Private recording storage
Backups

Not included

Calendar integration
Task-tool subscriptions
Historical migration
Consent or legal review
Human editing labor

Pricing basis

The estimate combines the selected infrastructure path, required operating modules, selected optional modules, and usage above included monthly allowances. Taxes and regional uplifts are excluded.

Railway pricing AssemblyAI pricing OpenAI model pricing Cloudflare R2 pricing

Deployment options

Pick the operational tradeoff

Choose based on operating preference, not only the headline price.

EasiestRecommended

Railway

Run the upload API, database, queue, and separate stage workers with managed deployment.

$5-$25/month plus processing and storage

Good fit

Upload-first beta
Background jobs
Managed operations

Limitation

Worker activity and media transfer can increase resource usage.

Deploy the processor

Cheapest

Vultr

Run the app, worker, and database on one small Vultr VPS with Docker Compose and explicit backups.

$7-$23/month plus APIs

Good fit

Cost-sensitive pilot
Hands-on maintenance
Moderate concurrency

Limitation

Upload resilience, queue durability, backups, and monitoring are self-managed.

Deploy on Vultr View the lean VPS guide

More control

DigitalOcean

Separate application, worker, data, storage, and backup responsibilities as the workload grows.

$40-$100/month plus APIs

Good fit

Separated workers
Predictable infrastructure
More processing headroom

Limitation

Additional services increase operations and do not replace usage-based transcription.

Explore DigitalOcean View the separated cloud guide

Start building

Prototype, automate, then deploy

Recommended route

01Vibe-coding builderPrototype upload-to-summary

Use Lovable to test resumable upload, stage progress, transcript playback, summary editing, action assignment, and export for one recorded call.

Open Lovable 02AutomationExport reviewed call outcomes

Use Make as optional glue to send a Published summary or confirmed actions to a document or task system after users approve them.

Open Make 03DeployShip the media-processing API

Deploy the application, database, queue, and stateless processing workers on Railway while recordings remain in private object storage.

Open Railway

AI Meeting Intelligence Platform

Is this worth validating?

Build this if

Avoid this if

Validate first

Why this exists

Customer problem

Who pays

Business model

What do you need next?

What would it take to run?

Build the first paid use case

Who it serves and what it must change

What ships now and what waits

Included

Excluded

Screens, actions, and states

Primary flow

Loading, empty, and error states

Entities and business rules

Business rules

Components, integrations, and controls

Integrations

Data flow

Failure handling

Security

Rate limits

Definition of done for the MVP

Deliverables

Acceptance criteria

Enough technology for the first version

Next.js and Tailwind CSS

PostgreSQL

BullMQ and Redis

Cloudflare R2

AssemblyAI and OpenAI adapters

Why this is sufficient

Not required for the MVP

Build, price, and deploy the MVP

Copy this into a builder

Your build prompt is ready

Model the MVP operating cost

Usage assumptions

$22-$56 per month

Included

Not included

Pricing basis

Pick the operational tradeoff

Railway

Good fit

Limitation

Vultr

Good fit

Limitation

DigitalOcean

Good fit

Limitation

Prototype, automate, then deploy

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