Otter.ai vs Fireflies.ai vs Granola (NEW): Comparison

Transcription pipelines now differentiate by documented live capture, assistant positioning, and macOS only note generation surfaces.

Boundary conditions for meeting capture and summarization

Scope definition should treat this objective as two coupled services: audio to text transcription, then text to summary synthesis with traceability back to timestamps. Requirement setting starts with meeting modality constraints, including live versus post call processing, because latency budgets drive buffer sizing, diarization strategy, and incremental summarization design. Governance definition must include consent capture, since recording triggers retention obligations and can force a no store architecture in regulated environments.

Taxonomy alignment separates tool responsibilities from stack responsibilities so procurement does not replace engineering controls with vendor assumptions. Tool selection should cover capture surface and default summarization behavior, while platform architecture should own identity, policy enforcement, storage, and evaluation. Output trust should come from evidence linking, because summaries without cited spans create untestable failure states during disputes or compliance reviews.

• Set boundary contracts: treat each tool as a meeting capture and summarization product surface, not as an enterprise data plane.
• Centralize policy checks: implement consent flags, retention timers, and access controls outside the tool, even when the tool provides notes.
• Stabilize meeting IDs: map calendar events, conferencing session identifiers, and transcript objects into a single canonical meeting key.
• Own storage semantics: decide whether transcripts remain in vendor storage, customer storage, or dual storage with hash based deduplication.
• Instrument quality gates: score transcription accuracy, speaker attribution quality, and summary faithfulness using internal evaluation, not vendor marketing claims.

Ingestion pipeline across conferencing audio and notes generation

Transport architecture must choose between live audio streaming capture and recorded media ingestion because each path changes error recovery and cost predictability. Live capture needs jitter buffers, reconnect logic, and partial transcript compaction, while recorded ingestion needs chunking, parallel decode, and deterministic ordering. Data minimization should happen before downstream summarization, because raw audio storage increases breach impact and expands discovery scope.

Orchestration design should model transcription and summarization as separate stages with explicit artifacts, including raw transcript, speaker segments, and summary drafts. Workflow control should include reprocessing triggers, because model updates or diarization fixes require backfills without corrupting prior audit trails. Evaluation routing should isolate high risk meetings, because legal, sales, and HR calls have different tolerance for hallucinated action items.

Surfaces and authentication paths

Endpoint selection typically involves either a SaaS user interface workflow or an application level integration that joins meetings on behalf of users. Identity binding must map a meeting participant to an account principal, because transcript access implies read permission to meeting content. Session admission should apply least privilege tokens, because a bot style participant can exfiltrate audio if conferencing controls permit unrestricted joins.

Transforms from audio to text

Decoder staging should normalize audio codecs, sample rates, and channel layouts, because diarization quality drops when channel separation collapses in mixed device meetings. Segmentation logic should emit timestamps at word or phrase granularity, because summary citation and downstream search depend on stable offsets. Speaker labeling should handle unknown speakers, because meeting platforms frequently provide display names that change mid call.

Guardrails and evaluation loop

Policy controls should require summary faithfulness checks, because abstractive summaries can invent decisions when transcripts contain ambiguity. Automatic validation can compare extracted action items against supporting transcript spans, then block publication when evidence coverage falls below a threshold. Review workflows should route exceptions to humans, because executive summaries can drive commitments and ticket creation.

Rights and retention checks

Ledger design should record who initiated capture, what consent basis applied, and when retention expiry occurs, because transcript artifacts become regulated records in many jurisdictions. Redaction mechanisms should support post processing removal of sensitive strings, because participants often disclose credentials, customer identifiers, or health data. Deletion semantics should include vendor side deletion verification, because partial deletion leaves summaries searchable even after transcript removal.

Breakpoints and mitigations

Telemetry planning should treat transcription errors as operational incidents, because systematic mishearing of product names or numbers can propagate into CRM updates and project plans. Mitigation can apply domain vocabulary injection where supported, then fall back to custom post correction dictionaries maintained by the customer. Resilience tactics should include offline note taking options, because meeting capture fails when participants deny recording permission or networks block bot joins.

Operational signatures by product surface

Workload fit should start from what the public materials explicitly claim, then treat everything else as an integration risk until validated in a pilot. Evidence in the provided sources covers only core transcription and summary generation positioning, which means implementation teams must assume missing knobs and build compensating controls. Contract review should remain part of engineering due diligence, because rights, retention, and export formats dictate whether transcripts can enter enterprise knowledge bases.

Latency expectations should follow from whether live transcription is documented, because live output implies incremental decoding and continuous UI updates under variable network conditions. Operational maturity also depends on edit and regeneration tooling, because summary correction workflows reduce downstream rework when transcripts contain errors. Platform scope matters for endpoint planning, because a macOS app imposes device management requirements that differ from browser based SaaS usage.

Otter.ai

• Implements meeting transcription with documented live transcription, which drives decisions on real time display, incremental buffering, and in meeting accessibility workflows.
• Provides automatic meeting summaries and notes, which introduces a second stage artifact that needs evidence linking if teams use summaries for task execution.
• Public docs do not specify: prompt or style controls, editing or regeneration workflow, export formats, licensing or usage rights, explicit limitations.

Fireflies.ai

• Positions the product as an AI meeting assistant with transcription, notes, and summaries, which suggests a broader workflow surface that may affect adoption and permission scoping.
• Delivers AI powered transcription and summarization as described on the product page, which requires validation for speaker attribution and action item extraction if used for ticketing.
• Public docs in the provided description omit: user controlled prompting, iteration controls, output formats, rights and licensing terms, stated limitations.

Granola (NEW)

• Targets automatic AI generated meeting notes for calls, which makes note synthesis the primary artifact and shifts accuracy risk to omission and misclassification of decisions.
• Constrains deployment surface to macOS as documented, which forces device fleet coverage analysis and may exclude mixed OS teams from uniform rollout.
• Public announcement details do not cover: transcription behavior, prompt controls, editing loops, export formats, licensing, or other limitations beyond platform scope.

Decision matrix with packaging visibility gaps

Matrix driven selection should treat feature claims as necessary but not sufficient, because transcription and summary outputs must integrate with identity, retention, and search systems. Procurement should require a transcript artifact model, because downstream systems need stable meeting identifiers, timestamped segments, and versioned summary objects. Pilot design should include adversarial meetings, because crosstalk, accents, and screen share audio often break diarization and degrade summary reliability.

Packaging uncertainty should push teams toward reversible integration patterns, because missing public detail on exports and rights can block enterprise data portability. Migration planning should include transcript escrow or mirrored storage, because vendor lock in emerges when only the vendor can reproduce historical summaries. Engineering should prioritize reduce rework cycles by building review, correction, and re publish steps around any chosen tool.

Tradeoff selection currently rests on one verifiable split: Otter.ai documents live transcription, Granola (NEW) documents macOS scope, while Fireflies.ai documents assistant positioning with transcription and summaries. Next validation should run a controlled pilot that measures word error rate on domain terms, diarization stability across interruptions, and summary faithfulness using transcript span citations and human review sampling.