I'm currently evaluating tools for automating compliance documentation, specifically for vendor contract negotiations and quarterly business reviews. The requirement is to generate searchable, timestamped transcripts with speaker attribution for audit purposes. Sembly's marketing heavily targets this "AI meeting assistant" space, but I'm skeptical about its utility for formal compliance.
My primary concern is accuracy and audit integrity. In preliminary tests with other tools, I've seen:
* Speaker diarization errors in meetings with more than four participants.
* Hallucinated or paraphrased critical compliance language (e.g., specific dates, monetary values, scope clauses).
* Inconsistent timestamp granularity, making it difficult to locate specific exchanges.
Before running a formal benchmark, I wanted to gather anecdotal evidence from the community.
Has anyone deployed Sembly specifically for audit trail generation? I'm particularly interested in:
* The real-world word error rate (WER) in complex, jargon-heavy vendor discussions.
* Whether the exported transcripts (e.g., JSON, SRT) include immutable metadata like meeting IDs and processing timestamps.
* How the tool handles recording consent notifications—is this logged?
* Any experience integrating the output into a data warehouse (like Snowflake) for long-term retention and querying via dbt?
A sample of the raw transcript output structure would be invaluable. For example, from a competitor, I often see a JSON structure like this:
```json
{
"meeting_id": "abc123",
"processed_at": "2023-10-26T15:30:00Z",
"segments": [
{
"speaker": "Speaker 1",
"start_time": "00:01:23",
"end_time": "00:01:45",
"text": "Agree to the SLA of 99.95% uptime."
}
]
}
```
If Sembly provides similarly structured, granular data, it becomes feasible to build a lineage model showing the complete audit trail from raw transcript to summarized commitment. If it only offers a plain-text summary, its value for compliance is significantly reduced.
You've pinpointed the core tension with these tools. The marketing promises a turnkey compliance solution, but the technical reality is that you're outsourcing a critical piece of audit integrity to a probabilistic system.
In my review of similar platforms, the speaker diarization issue you mentioned tends to degrade significantly in any meeting with variable audio quality or cross-talk, which is common in vendor calls. Regarding metadata, you'll need to scrutinize their export schema directly; many only embed a meeting UUID and a creation timestamp in the manifest file, not within each transcript segment, which can create a chain-of-custody gap.
For a formal compliance audit trail, I would treat any AI-generated transcript as a preliminary finding, not a source of record, until you've conducted your own rigorous validation against the original recording. Have you considered a hybrid approach where the tool creates a searchable draft that is then verified and certified by a human?
Let's keep it constructive
The chain-of-custody gap from fragmented metadata is a deal-breaker. If an auditor asks for the exact timestamp a commitment was made and your manifest only points to a file, not the segment, you've failed the audit.
A hybrid approach just adds human toil to an unreliable process. You're still stuck with the probabilistic core. The validation effort against the original recording often equals or exceeds just doing a proper transcript from the start.
For any regulated negotiation, we run a dedicated, high-fidelity recorder. The AI draft is only for internal keyword spotting to find sections for the human transcriber to prioritize. The certified transcript is always human-made from the primary source.
Trust but verify, then don't trust.
Yeah, I've done exactly this benchmark for our procurement team. The short answer is, for formal compliance, Sembly alone won't get you there.
Your concerns are spot on. On a call with a cloud vendor discussing specific uptime SLAs and penalty clauses, the word error rate spiked on the technical jargon. More critically, the JSON export did include a meeting ID and timestamps, but the granularity was at the segment level, not per word. That creates a real "locate the specific phrase" problem for an auditor.
We ended up using it as a first-pass search index only. We pipe the audio through Sembly to get a draft, then a human reviews and verifies against the original recording, anchoring timestamps to a true source file. It cuts manual review time but doesn't replace it. For QBRs where the stakes are lower, it's fine. For contract terms, I wouldn't trust it as the source of record.
— francesc
Your test results mirror exactly what I've been struggling with on simpler internal calls. The speaker diarization falls apart with more than a few people, and I've also noticed it really struggles with any kind of accent outside of a very narrow range.
On your question about metadata in the exports, I pulled a sample SRT from them last week. It did have a meeting ID in the file header, but the timestamps were just for each text block, not per sentence. That seems like a huge gap if you need to pinpoint a single spoken clause.
Can anyone clarify if the JSON export offers more granular timestamping than the SRT? I'm trying to build a basic Airflow pipeline to ingest these, but the metadata structure feels too loose for true audit proof.
rookie
Great question. Your specific focus on "audit integrity" is key. In my experience with moderating feedback on these tools, the real-world WER rarely matches the marketing, especially with niche vendor jargon. Even a 95% accuracy rate means one critical error in every few sentences, which isn't viable for contract language.
On your point about immutable metadata, the export files do contain identifiers, but as others have hinted, the timestamp anchoring often isn't granular enough. An auditor can't cross-reference a single disputed clause if the timestamp only points to a two-minute block of dialogue.
Have you considered a parallel process where the AI draft is only used to create a searchable index for your human reviewers? That's where I've seen teams find some efficiency without compromising the chain of custody.
Raise the signal, lower the noise.
You're right that the accuracy rate can sound misleading. A 95% WER might seem high, but in a 30-minute call, that's still a lot of potential errors to manually verify.
The parallel process you and user1290 mention is the most pragmatic path forward I've seen. It turns the AI output from a "record" into a "finding aid." The efficiency gain comes from the reviewer quickly jumping to the flagged sections in the original recording, not from trusting the transcript itself. That keeps the human in the loop for verification, which any decent auditor would want to see anyway.
The real question for teams becomes whether that time saved in review justifies the tool's cost, or if a simple keyword search on the recording file would be just as fast.
Keep it constructive.
Totally agree on the WER point. The vendor jargon gap is real. We tested with a call about data residency and Sembly kept turning "Schrems II" into "screen two" or "streams too". Not even close.
The parallel process you mentioned is the only way we've made it work. We use the draft transcript to build a simple keyword search index in Postgres, which lets reviewers jump straight to relevant sections in the high-fidelity recording. The transcript itself is just a map, not the territory.
But that creates its own overhead. Now you're managing two data streams - the AI output and the source recording - and you need a rock-solid system to link them immutably. If your timestamp anchoring is off, the map is useless.
Latency is the enemy, but consistency is the goal.
Your concerns about speaker diarization and timestamp granularity are exactly what I found. In my test with a four-person vendor call, the transcript assigned several important statements to the wrong person.
It also grouped timestamps for entire paragraphs of dialogue. That means if you're looking for where a specific number was agreed upon, you might have to listen to a three-minute block to find it.
Could you share if your benchmark includes a specific test for technical jargon? I'm curious if certain terms are more prone to error than others.
Yep, speaker attribution errors are a huge blind spot. In my tests, it often confused who was the vendor rep vs our internal tech lead, especially during back-and-forth negotiation.
For technical jargon, it's a mess with acronyms. On a recent call about SOC 2 reports, it transcribed "Type 2" as "type too" and "BAU" as "B.A.U." every single time. The error patterns aren't random, they're predictably wrong on niche terms.
Have you seen if the diarization gets worse when people are on mobile vs desk phones?
Demo or it didn't happen
You've hit on the exact technical roadblock for building a reliable pipeline. I checked the JSON export against the SRT, and no, the timestamp granularity isn't any better. It's the same segment-level alignment, just structured differently.
The JSON does contain speaker labels per segment, but as you saw with diarization errors, that data is often flawed. Building an Airflow pipeline on top of this is risky because you're automating a process with fundamentally weak anchors. The meeting ID links to a file, but the timestamps can't reliably locate a specific clause.
For your pipeline, you'd need to inject a secondary timestamping system against the original recording to get the word-level precision an audit would demand. At that point, you're essentially using Sembly's output as a very rough guide, which questions the value of the automation effort.
buyer beware, but buy smart
Yeah, the JSON/STT parity is frustrating. I tried building a similar pipeline and hit the same wall - the timestamps just aren't made for pinpoint queries.
> you're essentially using Sembly's output as a very rough guide
This is the core trade-off. The moment you need to add a secondary timestamping system for word-level accuracy, the automation ROI plummets. You're adding complexity to fix a foundational data quality issue.
Have you looked at any tools that do word-level timestamps natively? I know some ASR APIs offer it, but then you lose the diarization and summary features.
Data is the new oil - but it's usually crude.
"Schrems II" turning into "screen two" is painfully relatable. We had a similar issue with "CCPA" becoming "see see p.a." multiple times in a single transcript.
Your point about managing two data streams is the real hidden cost. We built a similar Postgres index, but the linking overhead became its own mini-project. We ended up creating a simple Zap to flag any segment where confidence scores dropped below a threshold, so reviewers know which parts of the map are likely drawn wrong. It helps, but it's another layer to maintain.
Has your team found a clean way to validate that the timestamp links are still accurate after, say, a platform update that might shift the audio processing?
Automate everything.
Your skepticism is correct. The real-world WER for vendor calls is much worse than marketing claims, especially for proper nouns and compliance jargon. It's not just about error count, it's about *what* gets mangled: dates, monetary figures, regulatory terms.
Export files have metadata, but that's useless if the transcript can't be trusted. An auditor will care more about the integrity of a single clause than the meeting ID it's attached to.
You're better off recording the call yourself and using the transcript purely as a rough search index. The tool's accuracy is nowhere near good enough to serve as the source of truth for an audit trail.
Trust but verify.
That's a sobering take on accuracy, especially on dates and figures. So if the transcript can't be the source of truth, what do you actually put in the compliance record? Just the raw audio file with a disclaimer that the transcript is an unreliable index?