TL;DR: Most AI note-taker pipelines fail in production because teams treat the LLM as both the transcription layer and the extraction layer. The fix is modular: use async transcription with speaker diarization and word-level timestamps as your structured foundation, then run separate LLM stages for summarization, action item extraction, and decision logging. Enforce JSON schemas at every stage, track confidence signals, and route validated outputs to downstream APIs via idempotent webhooks. Transcript quality sets the ceiling for every downstream output.
Most engineering teams try to build AI note-takers with a single massive LLM prompt. It works in staging and fails spectacularly in production. A 90-minute multilingual sales call lands in your pipeline, the LLM hallucinates three action items that were never discussed, misattributes two decisions to the wrong speaker, and your CRM (Customer Relationship Management) now contains garbage that account executives are filing as bugs. The problem is rarely the LLM. The problem is the transcript it was given.
Building reliable meeting intelligence requires moving from monolithic prompts to modular, multi-stage extraction. This guide breaks down the architecture for turning raw audio into structured, verifiable JSON outputs, starting with clean diarization and ending with reliable CRM webhooks.
Common pipeline failure pattern:
- Problem: Raw transcripts lack speaker attribution, punctuation, and code-switching handling, feeding LLMs low-quality input.
- Impact: Hallucinated action items, misattributed decisions, and increased post-processing toil that compounds with scale.
- Quick fix: Use async transcription with speaker diarization and word-level timestamps as your foundation.
- Long-term approach: Build a modular multi-stage pipeline with JSON schema enforcement and confidence thresholding per stage.
- How Gladia helps: Gladia’s async API delivers diarized, punctuated, and code-switched transcripts that reduce downstream LLM errors and improve extraction accuracy.
Designing reliable multi-stage note pipelines
The core architectural decision you make before writing a single prompt is whether to use a monolithic pipeline or a modular one. Monolithic pipelines are fast to prototype and guaranteed to fail under production load. Modular pipelines require more design work upfront and hold up in production.
Modular vs. monolithic prompt design
A monolithic prompt asks a single LLM call to transcribe intent, identify speakers, extract action items, summarize key themes, and output structured JSON, all from a raw 60-minute transcript. The failure modes are predictable. LLMs exhibit what researchers call a "lost-in-the-middle" effect, information placed in the middle of long inputs receives less processing weight than content near the beginning or end. Research on long-context degradation documents this pattern, and the Liu et al. study confirmed it across multiple frontier models. In practice, this means action items mentioned mid-meeting may receive less reliable extraction than those discussed at the start or end of a conversation.
Instruction dilution compounds this: when a single prompt contains multiple extraction tasks, the model's effective attention per task drops with each added instruction.
The modular alternative splits the pipeline into discrete stages, each with a single responsibility:
- Transcription stage: Async STT with diarization and word-level timestamps.
- Summarization stage: LLM pass grounded strictly in the transcript.
- Action item extraction stage: Structured JSON extraction with owner and deadline resolution.
- Decision extraction stage: Separate pass filtering finalized decisions from brainstorming.
- Downstream routing stage: Validated JSON dispatched via webhooks.
Each stage receives only the data it needs, runs against a defined schema, and passes a typed output object to the next stage. This means you can isolate failures, swap models per stage, and run regression tests against individual extraction prompts without retesting the entire pipeline.
Managing pipeline state for verifiable output
State management between stages is where most pipelines accumulate silent debt. When stage 3 (action item extraction) needs to reference the speaker labels established in stage 1 (transcription), you need a shared state object that both stages can read and write without mutating each other's outputs.
The practical pattern is to use an immutable pipeline context object, typically a JSON blob serialized to a fast key-value store like Redis, that each stage reads from and appends to without overwriting prior fields. Frameworks for LLM orchestration commonly use this pattern at the implementation level, but the underlying principle applies regardless of orchestration choice: each stage receives the full prior context as read-only input and appends only its own typed output.
{
"meeting_id": "mtg_20260405_abc123",
"transcript_id": "tr_gladia_xyz789",
"utterances": [...],
"summary": null,
"action_items": null,
"decisions": null,
"stage_statuses": {
"transcription": "complete",
"summarization": "pending",
"action_extraction": "pending",
"decision_extraction": "pending"
}
}
Every stage writes its output to its designated field and updates its status. If stage 3 fails, stage 4 sees "action_extraction": "failed" and can skip or flag accordingly, rather than proceeding with a null input it treats as an empty result.
Rollback strategies for LLM pipelines
When an LLM returns malformed JSON, a null field, or output that fails validation, you need a deterministic recovery path. Three patterns are worth implementing:
- Retry with exponential backoff: On a validation failure, wait and retry the same stage with the same prompt. Exponential backoff means doubling the wait interval on each attempt before marking the stage as failed.
- Fallback model routing: If the primary model (e.g., GPT-4o) fails twice, route to a secondary model (e.g., Claude 3.5 Sonnet) with the same prompt. Different models fail differently, so a secondary pass often succeeds where the primary did not.
- Human review queue: After failed retries across two models, flag the meeting ID for human review rather than silently dropping the extraction.
Stage 1: Clean and segment raw meeting data
LLM output quality is ceiling-bounded by transcript quality. If diarization is wrong, the LLM assigns action items to the wrong person. If code-switched words are garbled, the LLM hallucinates plausible-sounding alternatives. Fixing bad transcripts with better prompts is a losing strategy. Fix the transcript layer first.
Gladia’s async API processes pre-recorded audio and returns structured JSON output ready for LLM processing. The async API documentation covers the full request structure, including how to enable diarization, timestamps, and audio intelligence features in a single API call.
Speaker diarization in LLM pipelines
Speaker diarization is the single most important structural property your transcript can carry into an LLM. Without it, action items like "I'll follow up with the vendor by Friday" cannot be resolved to an owner, forcing the LLM to either guess or omit the assignment entirely.
Gladia's Solaria-1 model handles transcription, and speaker diarization is powered by pyannoteAI's Precision-2 model. Both are included at the base hourly rate. The async API output structures each utterance with an explicit speaker ID, start time, end time, and word-level confidence data:
Example response structure from Gladia async API
{
"utterances": [
{
"speaker": 1,
"start": 0.0,
"end": 12.4,
"text": "Example utterance from first speaker",
"words": [
{ "word": "Example", "start": 0.0, "end": 0.2, "confidence": 0.99 },
{ "word": "utterance", "start": 0.2, "end": 0.5, "confidence": 0.98 }
]
},
{
"speaker": 2,
"start": 12.6,
"end": 24.1,
"text": "Example utterance from second speaker",
"words": [...]
}
]
}
This structure lets your action item extraction prompt receive a pre-formatted conversation where every line is prefixed with Speaker 1: or Speaker 2:, and the LLM has unambiguous information to resolve pronouns to specific speakers. For a deeper technical dive on the Precision-2 architecture and its production accuracy characteristics, see the Gladia x pyannoteAI webinar.
Ensuring timestamp integrity for LLMs
Gladia provides word-level timestamps by default, with each word carrying a start, end, and confidence value. These serve two functions in a meeting intelligence pipeline.
First, they let you build verifiable UI features where a user clicks a summary bullet and jumps to the exact second in the recording where that statement was made.
Second, they give you a programmatic way to validate that LLM outputs are grounded in real transcript content rather than hallucinated. When your summarization stage generates a key point, you run a reverse-lookup against the word timestamps to find the transcript segment that supports it, and flag any summary bullet with no supporting match for human review.
Chunking long transcripts for LLMs
A long meeting can exceed practical context limits for reliable LLM extraction. Feeding a multi-hour transcript monolithically into a summarization prompt both triggers the lost-in-the-middle degradation described earlier and inflates your LLM costs with tokens that carry diminishing return.
The production pattern for chunking diarized transcripts follows three rules:
- Never split mid-utterance. Use speaker turn boundaries as natural chunk breaks to preserve the semantic unit of a single speaker's contribution.
- Add overlap between chunks. Some token overlap at chunk boundaries ensures that action items discussed across speaker turns are captured in at least one chunk's context window. In practice, overlap between chunks helps reduce information loss at boundaries and improves extraction stability across speaker turns.
- Tag each chunk with metadata. Include the meeting ID, chunk index, start time, and end time on each chunk object so your reduction step can reconstruct the full sequence after per-chunk LLM passes.
Ensuring accuracy in code-switched text
Code-switching, where speakers alternate between languages mid-sentence or mid-phrase, is one of the most reliable ways to break a monolithic transcription pipeline. A bilingual support call might switch between two languages every few sentences. A European sales call might layer three or more languages depending on the speaker. When a transcript renders those switches as garbled text or misidentified language, every downstream LLM stage inherits that corruption.
Gladia’s native code-switching detection handles mid-conversation language changes across 100+ supported languages, including 42 languages unsupported by other STT APIs, without requiring the developer to specify which languages may appear. The transcript output reflects each switch with correctly attributed text per language segment, so downstream LLM stages receive clean, parseable text rather than guessing at corrupted spans. For detailed WER comparisons across 7 datasets and 74+ hours of audio, see Gladia's benchmark methodology.
"Excellent multilingual real-time transcription with smooth language switching... Superior accuracy on accented speech compared to competitors... Clean API, easy to integrate and deploy to production." - Yassine R. on G2
Stage 2: Hallucination-free meeting summary engines
With a diarized, timestamped, and properly chunked transcript as input, the summarization stage has a well-defined job: extract what was discussed, attributed to the right speakers, without inventing content not present in the source text.
Crafting prompts for accurate summaries
The ICE method provides a reliable structure for grounding summarization prompts: Instructions, Constraints, and Escalation. A production summarization prompt looks like this:
You are a meeting analyst. Using ONLY the transcript below,
produce a bullet-point summary of the main topics discussed.
Do not infer, extrapolate, or add information not present
in the transcript. If a topic is unclear or incomplete,
note it as [unclear] rather than completing it.
Transcript:
{transcript_chunk}
Adding step-by-step reasoning to the prompt, for example "first identify the main topic of each speaker turn, then group related turns into themes, then summarize each theme," incentivizes inner consistency and reduces the logic gaps that produce hallucinations.
Mapping summaries to transcripts
For your UI to let users verify summaries against source audio, store the timestamp range that each summary bullet was derived from by asking the LLM to return both the summary text and the supporting utterance indices, then resolve those indices to timestamps using your state object:
{
"summary_bullets": [
{
"text": "Team discussed prioritizing mobile app fixes over new feature development.",
"supporting_utterances": [14, 15, 16],
"start_time": 1823.4,
"end_time": 1901.2
}
]
}
Strategies for verifying hallucinations
A secondary LLM verification pass asks a second model to check whether each summary bullet is supported by the transcript. The prompt structure is:
Given this transcript: {transcript_chunk}
And this summary bullet: {bullet_text}
Does the transcript explicitly support this bullet?
Answer YES or NO, then cite the exact utterance that
supports it, or state "not found" if none exists.
Anthropic's chain-of-thought guidance recommends adding "think step-by-step before answering" to verification prompts, which produces fewer false positives. Enforce that the verification response returns a typed JSON object rather than free text so your pipeline can act on the YES/NO field programmatically.
Monitoring summary accuracy
User behavior is the most reliable signal for summary quality. Track three metrics per meeting:
- Edit rate: The fraction of summary bullets users modify before sharing
- Thumbs-down rate: Explicit negative feedback per bullet
- Verification rate: The fraction of users who click through from a summary bullet to the source audio
High edit rates on bullets from specific time ranges point to lost-in-the-middle degradation, while high edit rates on specific languages point to transcript quality issues upstream.
Stage 3: Pinpointing next steps reliably
Action item extraction is where pipeline failures have the highest business cost. A missed action item from a customer call is a missed follow-up. A misattributed task lands in the wrong person's project management queue.
Schema for verifiable action items
Define your action item schema before you write the extraction prompt. OpenAI's JSON Schema mode and the Instructor library for Pydantic-based validation both enforce that the model returns exactly this structure or raises an error that triggers your retry logic:
{
"action_items": [
{
"id": "ai_001",
"owner_speaker_id": 2,
"task": "Send revised API contract to legal for review",
"deadline": "YYYY-MM-DD",
"context": "Discussed at 23:14, following agreement on Q2 scope",
"source_utterance_ids": [34, 35],
"confidence": 0.85 // model certainty score, 0-1
}
]
}
The confidence field is populated using logprobs from the model's token generation or a secondary evaluation pass. The source_utterance_ids field ties every action item back to verifiable transcript segments.
Extracting actionable owners & due dates
Pronoun resolution is the step most teams skip and most regret. When Speaker 2 says "I'll follow up with the vendor by next week," your extraction prompt needs to resolve "I" to speaker_id: 2 and "next week" to an ISO date relative to the meeting timestamp. Include both the speaker-labeled transcript and the meeting date in your extraction prompt so the model can produce owner_speaker_id: 2 and deadline: "2026-04-10" rather than owner: "I" and deadline: "next Friday", which are useless for CRM or task creation.
Measuring action item quality scores
Three programmatic checks cover most failure modes: owner completeness (is owner_speaker_id populated and does it match a speaker ID present in the transcript), task specificity (does the task description contain a verb and an object), and deadline parsability (is the deadline a valid ISO 8601 date that falls after the meeting date). Any action item failing two or more checks routes to your human review queue rather than to a CRM write. The Gladia audio to LLM documentation shows how the structured transcript output maps into this extraction pattern.
Resolving duplicate action items
In a 90-minute meeting, the same task is often raised, reconfirmed, and mentioned again in a wrap-up. A deduplication pass after extraction compares task descriptions using embedding similarity and merges records that describe the same commitment, keeping the record with the highest confidence score and the most complete fields.
Stage 4: LLM-driven decision extraction for notes
Decisions and action items are structurally distinct. An action item is a future commitment. A decision is a finalized conclusion about a question or trade-off that was open before the meeting. Conflating them in a single extraction pass produces a list that mixes outputs with rationale, making notes harder to use for either follow-up or institutional memory.
LLM decision vs. discussion parsing
The challenge in decision extraction is filtering brainstorming from resolution. A discussion might consider several options before the group finalizes a choice. The instruction pattern that works in production is:
Identify only decisions that were explicitly finalized during
this meeting. A decision requires: (1) a clear statement of
what was chosen, and (2) evidence that the group agreed or
that a decision-maker confirmed the choice. Do not extract
options that were discussed but not selected.
Adding step-by-step reasoning to this prompt, as Anthropic's prompt engineering guidance recommends, produces fewer false positives from exploratory discussion.
Capturing decision context & why
A decision without its rationale has a short shelf life. Your decision schema should capture the reasoning explicitly:
{
"decision": "Use PostgreSQL for the analytics datastore",
"rationale": "The team needed better query flexibility for ad-hoc reporting requirements that emerged during customer discovery",
"stakeholders": [1, 3],
"timestamp": 1847.2,
"linked_action_items": ["ai_003", "ai_007"]
}
The linked_action_items field connects decisions to the implementation tasks that follow from them, giving your project management integration the context to create properly described tickets.
Extracting actionable decisions
Not every decision requires follow-up, but decisions that do should link explicitly to the action items that implement them. Your pipeline state object already contains both the action item list and the decision list from their respective extraction stages. A final linking pass uses utterance ID overlap to identify which action items were discussed in the same conversational segment as each decision, then writes those links to the state object before downstream routing.
Stopping LLM hallucinations and errors
Defense mechanisms at the pipeline level are cheaper than debugging hallucinated outputs that reached production.
Enforcing LLM output schemas
OpenAI's Structured Outputs ensure the model will always generate responses that adhere to your supplied JSON Schema, so you don't need to worry about the model omitting a required key or hallucinating an invalid enum value. The Instructor library for Python provides the same guarantee via Pydantic models, with automatic retry on validation failure built in. Both approaches eliminate a class of bugs where the LLM returns "deadline": "next week" instead of a properly formatted date like "deadline": "2026-04-12". Define the schema before you write the prompt: the schema is the contract that every downstream stage depends on.
Verifying LLM output coherence
Schema compliance confirms structure. Coherence verification confirms meaning. Two automated checks catch most coherence failures:
- Named entity consistency: Every speaker ID referenced in action items or decisions should appear in the source transcript. If an action item assigns a task to
speaker_id: 5 but the transcript only contains speakers 1 through 3, you have a hallucination. - Summary length ratio: If a meeting produces a summary with an unusually high number of bullets relative to transcript length, flag the output for human review. Define expected length relative to audio duration and alert on outliers.
Gladia's Named Entity Recognition is included in the base rate and returns structured entity data from the transcript. You can use this as a reference list to check whether entities mentioned in LLM outputs (company names, product names, person names) actually appeared in the meeting audio.
LLM confidence thresholds for review
Logprobs (log probabilities) are the logarithm of the probability assigned to each output token and provide a per-token confidence signal for validation. Higher logprobs, closer to zero, indicate greater certainty in a token's selection, while lower logprobs signal that the model was uncertain. Route low-confidence extractions to human review before they reach downstream systems. Calibrate your specific thresholds against a golden dataset of your own meeting audio rather than using fixed values across all meeting types, since the optimal threshold varies with meeting domain, speaker count, and language distribution.
Tracking LLM output quality over time
At minimum, log the following fields per pipeline run: meeting ID, audio duration, language distribution, speaker count, LLM model used per stage, prompt version hash, token count, validation pass/fail status, and human review routing reason. Tools like LangSmith provide LLM-specific tracing that captures the full input-output pair for each stage alongside latency and cost metrics, which makes prompt regression testing against historical runs significantly easier.
Build your AI note-taker's API connectors
A pipeline that produces validated JSON but cannot route it reliably to downstream systems is a pipeline that fails at the last mile.
Accurate CRM contact & deal updates
Matching meeting participants to CRM contacts requires a resolution step before any data is written. Use the participant email list from the calendar invite (if available) or a speaker-to-contact lookup based on meeting room context to map speaker_id: 1 to a CRM contact record before the write call. Write operations should be idempotent: include the meeting ID as a deduplication key so that a retried webhook does not create a second activity record for the same meeting.
Project management: Task creation and assignment
Each validated action item from your extraction stage maps to a ticket creation call in your project management tool. The mapping is direct:
| Example action item field |
Common ticket field mapping |
task |
Title |
owner_speaker_id (resolved) |
Assignee |
deadline |
Due date |
context |
Description |
linked_decision_id |
Parent epic or label |
Converting notes to calendar invites
When a decision or action item contains a follow-up meeting intent, use a dedicated LLM extraction pass for scheduling intents, returning a typed object with proposed date, participants, and agenda context. Surface the draft to the meeting organizer for confirmation rather than creating the invite automatically.
Ensuring webhook reliability with retries
Downstream APIs will fail. Network timeouts, rate limits, and transient errors are production realities, not edge cases. Your webhook dispatcher needs exponential backoff with jitter to prevent thundering herds, a dead-letter queue (DLQ) for failed writes after all retries (with the full payload, failure reason, and timestamp), and idempotency keys in every downstream API call so retries do not produce duplicate records. The Google Cloud retry strategy documentation covers the exponential backoff pattern in detail.
Ensuring LLM reliability and monitoring
Managing pipeline latency and costs at scale
An async note-taker pipeline does not share a latency budget with a real-time voice agent. The transcription stage is the dominant latency driver: Gladia’s async API processes approximately 60 seconds per hour of audio, giving you a structured, diarized transcript ready for LLM processing in roughly one minute for a 60-minute meeting. The LLM extraction stages can run in parallel after chunking is complete, so total LLM time is bounded by your slowest single-stage call rather than the sum of all calls.
At 1,000 hours of audio per month, the STT layer cost is the most predictable part of your pipeline because it scales linearly with audio duration. Public pricing models differ in how clearly they bundle transcription and downstream audio intelligence.
Pricing as of April 2026:
| Provider |
Public async base rate |
Feature packaging |
| Gladia |
Starter: $0.61/hr async; Growth: as low as $0.20/hr async |
Paid plans are positioned around included languages and audio intelligence rather than per-feature add-ons |
| Deepgram |
Nova-3 Multilingual: $0.0092/min (~$0.552/hr) |
Nova pricing presents diarization as an advanced capability; feature availability should be checked per model docs |
| AssemblyAI |
Universal-3 Pro: $0.21/hr |
Current docs position diarization, automatic language detection, and code switching as included on supported models rather than separate diarization surcharges |
For Gladia, current public pricing includes languages and audio intelligence features across paid plans, while competitor packaging varies by model and feature set. Always verify current feature availability against the live pricing and model documentation before modeling total cost.
Troubleshooting extraction failures
When a user reports a missing action item, your debugging path needs to be deterministic. The log fields described in the monitoring section give you the following trace: query the pipeline state object by meeting ID, check stage_statuses to identify which stage last ran successfully, pull the input and output logs for the failing stage, check whether the failing utterance was in a chunk that had a validation failure, and check the confidence score for that chunk's extraction output. If the action item exists in the transcript but was not extracted, the failure is typically a prompt issue (the task was implicit rather than explicit) or a chunking issue (the action item spanned a chunk boundary and overlap was insufficient).
Benchmarking LLM prompt performance
A golden dataset is a collection of real meeting transcripts with manually verified expected JSON outputs. Build this dataset before you ship the pipeline to production, and add to it every time a user reports a failure that reveals a new edge case. Run your extraction prompts against the golden dataset on every prompt change and track precision (what fraction of extracted action items match expected output), recall (what fraction of expected items were successfully extracted), and F1 score (the harmonic mean of precision and recall). A CI step that blocks prompt changes reducing F1 below a defined threshold prevents prompt regressions from reaching production.
Key questions for your AI note-taker system
Managing LLM context for long meetings?
Use a map-reduce chunking strategy (process chunks in parallel, then merge results): split the diarized transcript at speaker turn boundaries with a 10-20% token overlap per chunk, run your extraction prompts per chunk in parallel, then merge and deduplicate chunk outputs using embedding similarity for action items and utterance ID overlap for decisions. This keeps each LLM call within a manageable context window and avoids the lost-in-the-middle degradation documented for long inputs.
Defining action item performance metrics
The most reliable production metrics for action item quality are edit rate (what fraction of extracted items users modify before acting on them), owner resolution accuracy (what fraction of items have a valid assignee who was actually in the meeting), and deadline parsability (what fraction of deadlines are valid ISO dates). Track these per language, per meeting length, and per speaker count to identify where the pipeline degrades and which audio conditions require upstream transcript improvement.
Test Gladia on your own multilingual audio to evaluate automatic language detection, accent-heavy speech, and code-switching under real production conditions. Start with 10 free hours, or review the async transcription documentation for request structure and integration patterns.
FAQs
What is the minimum viable JSON schema for meeting action items?
An action item schema needs at minimum: owner_speaker_id (integer mapping to a diarized speaker), task (string with a verb and object), deadline (ISO 8601 date), context (string with source meeting segment), source_utterance_ids (array of integers), and confidence (a score indicating extraction certainty). Any extraction that returns a null owner_speaker_id or an unparseable deadline should route to the human review queue before reaching downstream systems.
How does Gladia handle code-switching in async transcription?
Solaria-1 detects mid-conversation language switches automatically across 100+ supported languages, including 42 languages unsupported by other STT providers, without requiring you to specify which languages may appear. The transcript output reflects each switch with correctly attributed text per language segment, so downstream LLM stages receive clean, parseable text.
What is the WER for Gladia's Solaria-1 in production?
Solaria-1's performance varies by dataset. Gladia's benchmark comparison shows results across 8 providers and multiple datasets including Switchboard (35.8% WER for conversational English), Mozilla Common Voice, and VoxPopuli, covering 74+ hours of audio. Customers such as Claap have achieved 1-3% WER in production using Gladia.
Does Gladia charge separately for diarization in an LLM pipeline?
No. Gladia’s paid plans are positioned around included speaker diarization, code-switching, and audio intelligence capabilities rather than per-feature add-on fees. For the latest public rates and plan details, see the pricing page.
Key terms glossary
WER (word error rate): The percentage of words in a transcript that differ from the reference transcription, calculated as (substitutions + deletions + insertions) / total reference words. A 6% WER means 6 out of 100 words are incorrect.
Diarization: The process of segmenting an audio transcript by speaker identity, assigning each utterance to a specific speaker label (e.g., Speaker 1, Speaker 2). Gladia implements diarization using pyannoteAI’s Precision-2 model, included within the paid-plan pricing model.
Code-switching: Mid-conversation alternation between two or more languages by a single speaker or across speakers. Most STT models handle this poorly. Solaria-1 detects it automatically across 100+ languages.
Logprobs: Log probabilities assigned to each output token by an LLM during generation. Higher logprobs (closer to zero) indicate greater model certainty. Used as a confidence signal for routing low-confidence extractions to human review.
JSON schema enforcement: A technique using tools like OpenAI Structured Outputs or the Python Instructor library to force LLM outputs to conform to a predefined data structure, eliminating unparseable or missing fields before they reach downstream systems.
Async transcription: Batch processing of pre-recorded audio, as opposed to real-time streaming. For meeting note-taker use cases, async transcription produces higher-quality diarization and allows processing of complete audio files without streaming latency constraints.
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