Full-Time
Machine Learning Engineer
Otter.ai
201-500 employees
Real-time AI transcription and meeting summaries
Compensation Overview
$196k - $221k/yr
Mountain View, CA, USA
Hybrid
Hybrid role; on-site in Mountain View, CA.
 AI & Machine Learning (1) |
|---|
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- Holds a Bachelor’s or Master’s degree in Computer Science or a related field with 2+ years of relevant industry experience; PhD is preferred.
- Deep, hands-on experience building, fine-tuning, and post-training large language models or other foundation models, including an understanding of failure modes and trade-offs.
- Demonstrates strong command of modern machine learning research, with the ability to critically evaluate new papers and decide what is production-worthy versus experimental.
- Interest in creating innovation and advancing applied research
- Extensive experience deploying, monitoring, and operating ML systems in production, including model versioning, rollback strategies, and performance regression detection.
- Comfortable working with large-scale speech and conversational datasets, including data preprocessing, augmentation, quality analysis, and labeling strategies to support model training and evaluation.
- Experience scaling ML systems across training, inference, and serving infrastructure while balancing cost, latency, and reliability constraints.
- Highly effective at cross-functional collaboration, working end-to-end with product, infra, research, and data teams to deliver outcomes—not just models.
- Ability to lead technical projects independently, driving clarity in ambiguous problem spaces and making sound architectural decisions.
- Experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration.
- Significant experience with at least one of the following areas: (1) Speech recognition (ASR), (2) Text-to-speech (TTS), (3) Multimodal (speech/text) foundation models, or (4) modern LLM NLP tasks (e.g., summarization, dialogue, speech understanding), especially in real-world production settings.
- Experience with personalization, recommendation systems, or user modeling is a plus
- Architect, build, and evolve large-scale SID / ASR / NLP / LLM systems that power mission-critical product experiences including summarization, chat, and speech understanding across millions of conversations.
- Lead the design and implementation of training, fine-tuning, post-training, and inference strategies for large language and speech models using PyTorch and/or JAX, making principled trade-offs across quality, latency, cost, and reliability.
- Design and improve model architectures, loss functions, decoding strategies, and training techniques for speech and language models, informed by both research and production constraints.
- Own end-to-end ML system lifecycles, from research prototyping through production deployment, monitoring, iteration, and long-term maintenance.
- Partner deeply with product, and infrastructure teams to develop and translate cutting-edge research into scalable, production-grade systems that deliver measurable user and business impact.
- Drive system-level improvements in model performance, robustness, observability, and operational excellence using real-world conversational data at scale.
- Set technical direction and best practices for ML infrastructure, data pipelines, evaluation frameworks, and deployment workflows in a cloud environment.
- Identify and resolve complex, ambiguous problems in model behavior, data quality, scaling, and system interactions, often before they surface as user-visible issues.
- Mentor and elevate other engineers, influencing team standards, reviewing designs, and contributing to a culture of strong technical decision-making and execution.
- Has PhD preferred.
- Has experience with or strong interest in agentic systems, tool-use frameworks, or multi-model orchestration.
- Experience with personalization, recommendation systems, or user modeling is a plus
Otter.ai offers real-time AI transcription and meeting automation. Its core product Otter transcribes live audio, identifies speakers, and creates summaries; OtterPilot adds recording, slide capture, action-item extraction, and AI-driven summaries. It integrates with Zoom, Teams, and Google Meet and uses subscription pricing for individuals, teams, and enterprises. The goal is to help people capture, organize, and act on spoken information to save time and boost productivity in meetings and lectures.
Company Size
201-500
Company Stage
Series B
Total Funding
$73M
Headquarters
Los Altos, California
Founded
2016
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Simplify's Take
What believers are saying
- Conversational Knowledge Engine targets $100B market with AI Chat Connectors for Gmail, Salesforce, and Notion.
- Egnyte integration launched April 2, 2026, exports transcripts to secure enterprise folders automatically.
- Kenny Scannell as CRO brings Zoom and Rocketlane experience to scale enterprise revenue.
What critics are saying
- Brewer v. Otter.ai (5:25-cv-07712) imposes ECPA and CIPA penalties for unconsented meeting recordings by September 2026.
- Consolidated In re Otter.ai Privacy Litigation awards BIPA $1,000-$5,000 per voiceprint violation to millions.
- Ohio State bans Otter.ai, forcing education users to Teams and eroding 35 million user base within 6 months.
What makes Otter.ai unique
- Otter.ai's Conversational Knowledge Engine maps meetings into longitudinal knowledge graphs for enterprise actions.
- OtterPilot automates real-time transcription, speaker ID, slide capture, and action item extraction in Zoom and Teams.
- Otter for Desktop captures conversations across any application, beyond video meetings.
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Benefits
Competitive salary
Comprehensive stock and equity package
Hybrid Work Options
Company Gatherings
Comprehensive health care package (medical, dental, vision, life, disability)
PTO
401(k) retirement savings program
Growth & Insights and Company News
6 month growth
↓ -1%1 year growth
↑ 0%2 year growth
↑ 7%Sembly AI review 2026: pricing, features, pros & cons. Sembly AI is a professional AI meeting assistant that extracts decisions, action items, risks, and KPIs from your meetings - not just a transcript. Here's an honest look at what it does well, what it costs, and whether it's worth it over Fireflies or Otter.ai. Quick verdict. Overall Rating Per User / Month Business Intel Core Positioning Best for: executives, business analysts, and sales teams who need structured decisions, risks, and KPIs extracted from meetings - not just a transcript and summary. What is Sembly AI? Sembly AI is a professional AI meeting assistant that records, transcribes, and analyzes meetings to extract business intelligence rather than stopping at a plain transcript. Beyond converting speech to text, Sembly identifies decisions made, action items assigned, risks mentioned, and KPIs discussed, automatically categorizing each so nothing important gets buried in a long summary. It works across Zoom, Google Meet, Microsoft Teams, and in-person calls, giving teams consistent meeting intelligence regardless of which video platform a given meeting happens to use. Sembly's standout feature, Glances, aggregates this structured data across your entire meeting history, surfacing trends over time - a recurring risk, a KPI mentioned repeatedly, or a commitment that keeps slipping - that would otherwise stay invisible inside dozens of individual meeting summaries. In 2026, Sembly competes with transcription-first tools like Fireflies and Otter.ai, but positions itself a level up: less "what was said" and more "what does this mean for the business," making it the choice for executives and analysts who need strategic signal, not just searchable notes. Sembly AI pros & cons. Pros. * - Extracts structured business intelligence beyond a basic transcript: decisions made, action items assigned, risks mentioned, and KPIs discussed are all automatically identified and categorized rather than buried in a wall of text * - The 'Glances' feature reveals trends across your entire meeting history over time, so patterns like recurring risks or repeated commitments become visible instead of living inside dozens of separate one-off summaries * - Works across Zoom, Google Meet, Microsoft Teams, and in-person calls, so teams don't need to standardize on a single video platform to get consistent meeting intelligence * - SOC 2 Type II compliance gives it a real edge for business teams with security or procurement requirements that many smaller meeting-note tools simply can't satisfy * - CRM integrations with Salesforce and HubSpot let sales teams pull decisions and commitments straight into deal records without manual re-entry * - Positioned specifically for executives and business analysts who need strategic intelligence from meetings, not just a searchable transcript - a genuinely different use case than most note-taking competitors target * - Searchable meeting history means finding what was decided three months ago about a specific topic doesn't require scrolling through calendar invites trying to remember which call it was Cons. * - More expensive than basic transcription tools - teams that only need a searchable transcript and simple summary will pay for business-intelligence features they may not use * - AI extraction accuracy for decisions, risks, and KPIs varies with meeting quality; messy, unstructured conversations produce less reliable categorization than clean, well-run meetings * - Mobile app is noticeably less polished than the desktop experience, which matters for executives who want to review meeting intelligence between calls on the go * - Free tier is limited, so evaluating the full Glances and business-intelligence extraction properly generally requires committing to a paid plan first * - Value is concentrated for a specific persona - sales leaders, executives, and analysts get the most from decision/risk/KPI extraction, while individual contributors mostly wanting simple notes may find it more tool than they need * - Per-user Team pricing at $30/user/mo can add up quickly for larger teams compared to flatter-rate competitors * - Cross-meeting trend analysis (Glances) needs a meaningful volume of recorded meetings before patterns become genuinely useful, so the value takes time to compound rather than showing up on day one Sembly AI pricing 2026. Sembly uses a freemium model, with paid plans scaling from individual professionals to full teams with CRM integration and compliance needs. Free. * - Limited meeting transcription * - Basic summaries * - Restricted history Testing Sembly before committing Most Common Personal / professional. $15-$29/mo * - Full transcription across Zoom, Meet, Teams * - Decision, action item & risk extraction * - Glances cross-meeting trends * - Searchable meeting history Individuals wanting structured meeting intelligence Team. $30/user/mo * - Everything in Professional * - CRM integration (Salesforce, HubSpot) * - SOC 2 Type II compliance * - Team-wide meeting intelligence Sales and ops teams tracking decisions at scale Sembly AI vs Fireflies vs Otter.ai. | Feature | Sembly AI | Fireflies | Otter.ai | | Decision/risk/KPI extraction | | Core feature | | Basic action items only | | Basic action items only | | Cross-meeting trend analysis | | Glances feature | | Not offered | | Not offered | | CRM integration | | Salesforce, HubSpot | | Salesforce, HubSpot | | Limited | | SOC 2 Type II compliance | | Yes | | Yes | | Yes | | Starting price | $15/mo | Free tier + $10/mo | Free tier + $16.99/mo | | Best fit | Execs & analysts needing business intelligence | Sales teams wanting transcripts + CRM sync | General-purpose meeting transcription | Frequently asked questions. Is Sembly AI worth it? Sembly AI is worth it for executives, business analysts, and sales leaders who need more than a transcript - specifically, structured extraction of decisions, action items, risks, and KPIs discussed across meetings, plus trend visibility over time via the Glances feature. Teams that just want basic call transcription and summaries at the lowest cost may find cheaper alternatives sufficient, since Sembly's premium sits on the business-intelligence layer rather than transcription accuracy alone. How much does Sembly AI cost? Sembly AI offers a limited free tier, a Personal plan around $15/mo, a Professional plan around $29/mo, and a Team plan at $30 per user per month that adds CRM integrations and SOC 2 Type II compliance features suited to larger organizations. Sembly AI vs Fireflies - what's the difference? Fireflies is built primarily around fast, accurate meeting transcription with searchable recordings and solid CRM sync, aimed at a broad range of teams. Sembly goes a step further into structured business intelligence - automatically tagging decisions, risks, and KPIs, and surfacing trends across your entire meeting history through its Glances feature - making it a stronger fit for executives and analysts who need strategic insight rather than just a clean transcript. What is Sembly AI's Glances feature? Glances is Sembly's cross-meeting analysis feature that aggregates structured data (decisions, risks, KPIs, action items) extracted from many individual meetings into trend views over time. Instead of reviewing meetings one at a time, Glances lets you see patterns - like a risk that keeps resurfacing across multiple calls, or a KPI mentioned repeatedly across a quarter of meetings. Does Sembly AI work with Zoom, Teams, and Google Meet? Yes - Sembly AI records and transcribes meetings across Zoom, Google Meet, and Microsoft Teams, as well as in-person conversations, so teams don't need to standardize on a single video conferencing platform to get consistent meeting intelligence. See how Sembly AI stacks up against other AI meeting assistants and transcription tools.
TurboScribe vs Otter.ai (2026): which transcription tool is worth it? Researched by Sohail Akhtar TheToolsVerse People compare TurboScribe vs Otter.ai all the time - but here's the thing most reviews skip: they're not really the same kind of tool. One is built to transcribe files you upload without limits. The other is built to sit in your live meetings and take notes. Picking the wrong one isn't about quality - it's about buying a hammer when you needed a screwdriver. I've used both and dug into their 2026 pricing. Here's the honest breakdown of which fits which job, and where each one quietly costs more than it looks. The 20-second answer. TurboScribe is for files - podcasts, interviews, videos, multilingual audio - with truly unlimited transcription from $10/mo. Otter.ai is for live meetings - it joins your calls, transcribes in real time, and summarizes - but it caps your minutes and is English-first. Match the tool to whether your audio is recorded or live. TurboScribe vs Otter.ai at a glance. | / | TurboScribe | Otter.ai | | Built for | Uploading & transcribing files | Real-time meeting notes | | Transcription limit (paid) | Unlimited | 1,200 min/mo (Pro) | | Free tier | 3 transcriptions/day (30 min each) | 300 min/mo (30 min/convo) | | Real-time meeting bot | No | Yes (OtterPilot joins calls) | | Languages | 134+ | English-first | | File length | Up to 10 hours / 5GB | Via upload (limited) | | Entry paid price | $10/mo (annual) | $8.33/mo (annual), $16.99 monthly | | AI features | Summaries + ChatGPT chat on transcripts | Meeting summaries, action items | | Best for | Podcasters, journalists, video, multilingual | Teams living in Zoom/Meet | Round 1: What they're actually built to do. This is the whole ballgame, so let's be clear. Otter.ai is a meeting assistant. Its standout feature is OtterPilot, which automatically joins your calendar's Zoom, Meet, and Teams calls and transcribes them live, then spits out summaries and action items. If your day is back-to-back calls, that's genuinely useful. TurboScribe is a file transcriber. You upload a recording - a two-hour podcast, an interview, a lecture, a video - and it returns an accurate transcript fast. There's no meeting bot; it's a post-production tool. So the first question isn't "which is better," it's: is your audio live or recorded? Live meetings | Otter. Recorded files | TurboScribe. Round 2: Pricing and limits - where Otter hides the catch. Both look affordable up front. The difference is in the limits. | / | TurboScribe | Otter.ai | | Free | 3 transcriptions/day, 30 min each | 300 min/mo, 30 min/convo | | Entry paid | $10/mo annual ($20 monthly) - unlimited | $8.33/mo annual ($16.99 monthly) - 1,200 min | | Higher tier | (Unlimited is the top) | $20/mo annual - 6,000 min (Business) | Here's the part that matters: Otter quietly cut its Pro plan from 6,000 minutes to 1,200 minutes - without dropping the price. That's 20 hours of audio a month. A few long meetings and you're out, and you either upgrade to Business ($20/mo) or stop. TurboScribe's paid plan, by contrast, is genuinely unlimited - transcribe hundreds of hours and the price doesn't move. Do the minute math before you commit. If you transcribe more than ~20 hours a month, Otter's Pro plan will run dry and TurboScribe's unlimited model is far cheaper per hour. If you do a handful of short meetings, Otter's free or Pro tier is plenty. Count your hours first. Round 3: accuracy and languages. Both are accurate on clean, single-speaker audio. The differences show up at the edges. Otter performs well in clear meetings but can struggle with overlapping speech, heavy accents, and technical jargon. It's also English-first - multilingual support is limited. TurboScribe is built on Whisper-class models, supports 134+ languages, and includes noise reduction that cleans up rough audio before transcribing. For non-English content or messy field recordings, that's a real edge. Round 4: AI features. Both go beyond raw transcripts. Otter generates meeting summaries and action items linked to the conversation. TurboScribe adds a ChatGPT-style chat on top of your transcript - you can summarize, extract quotes, or repurpose a transcript into content without leaving the app. For meetings, Otter's action-item extraction is more purpose-built. For turning long recordings into articles or notes, TurboScribe's transcript chat is more flexible. The honest pros and cons. TurboScribe. Pros & cons. Pros. * +Genuinely unlimited transcription on the paid plan * +134+ languages with noise reduction * +Handles long files (up to 10 hours / 5GB) * +Free tier is usable (3 per day) * +ChatGPT chat to repurpose transcripts Cons. * −No real-time meeting bot * −Not built for live collaboration * −Free tier caps you at 30-minute files Otter.ai. * +OtterPilot joins live meetings automatically * +Real-time transcription and collaboration * +Strong meeting summaries and action items * +Clean integrations with Zoom, Meet, Teams * −Pro plan cut to 1,200 minutes at the same price * −English-first; weak multilingual support * −Struggles with overlapping speech and accents * −Heavy users burn the allowance fast Which should you pick? The decision, by who you are: * Podcaster, journalist, or video creator transcribing recorded files | TurboScribe. Unlimited + long files win. * Team that lives in Zoom/Meet/Teams | Otter.ai. The live meeting bot is the whole point. * You work in non-English audio | TurboScribe. 134+ languages, decisively. * You transcribe heavy volume monthly | TurboScribe. Otter's minute caps make volume expensive. * You mostly need quick notes from a few calls | Otter's free or Pro tier is enough. "TurboScribe and Otter aren't rivals so much as different tools that happen to both make text from speech. One is for the files on your drive; the other is for the calls on your calendar." Sohail Akhtar - Founder, TheToolsVerse See TurboScribe's full pricing & review. Free tier, unlimited plan, languages, and where it fits - on its verified tool page. Frequently asked questions. Is TurboScribe or Otter.ai better? It depends on your audio. TurboScribe is better for transcribing recorded files - podcasts, interviews, videos - with unlimited transcription and 134+ language support. Otter.ai is better for live meetings, since it can join your calls automatically and transcribe in real time. For files, choose TurboScribe; for meetings, choose Otter. Is TurboScribe really unlimited? Yes. TurboScribe's paid plan ($10/month billed annually, or $20 monthly) offers genuinely unlimited transcription, with files up to 10 hours and 5GB. This is its biggest advantage over Otter, whose Pro plan caps you at 1,200 minutes per month. How many free minutes does Otter.ai give? Otter's free Basic plan includes 300 transcription minutes per month, with a 30-minute cap per conversation. Note that Otter reduced its paid Pro plan from 6,000 to 1,200 minutes per month without lowering the price, so heavy users hit the limit quickly. Does TurboScribe work for languages other than English? Yes - TurboScribe supports 134+ languages and includes noise reduction, making it a strong choice for multilingual or lower-quality audio. Otter.ai is English-first with limited support for other languages. Can Otter.ai transcribe uploaded files? Yes, but with limits - the Pro plan allows around 10 file imports per month, and all transcription counts against your monthly minute cap. TurboScribe is purpose-built for file uploads with no transcription cap on paid plans. Which is cheaper, TurboScribe or Otter? Otter's entry price is slightly lower ($8.33/mo annual vs TurboScribe's $10/mo), but it caps you at 1,200 minutes. If you transcribe more than ~20 hours a month, TurboScribe's unlimited plan is far cheaper per hour. For light use, Otter's free tier is the cheapest option. Final verdict. If your work is recorded audio - podcasts, interviews, lectures, video, or anything multilingual - TurboScribe is the clear pick, and its unlimited model means the price won't punish you for volume. If your work is live meetings and you want a bot that shows up and takes notes for you, Otter.ai is built for exactly that. Test both free tiers this week against your real audio. The right tool reveals itself in about ten minutes. Browse its verified directory of transcription and speech-to-text tools, each with honest pricing and an editor review. Article by Sohail Akhtar, Founder of TheToolsVerse. Pricing reflects publicly listed plans as of June 2026 and can change - always check each provider's site before subscribing. Last updated June 2026. Some links may be affiliate links. If you buy through them, Thetoolsverse may earn a small commission at no extra cost to you - it's how Thetoolsverse keep the directory free. Browse its curated directory of 782+ verified AI tools. Some links may be affiliate links. Thetoolsverse may earn a small commission at no extra cost to you.
Fireflies vs Otter: which AI note-taker is worth it? By Lex Thomas · June 22, 2026 sales tools AI transcription sales productivity call recording If you've been shopping for an AI meeting note-taker, you've landed on the same two names everyone else does: Fireflies.ai and Otter.ai. Both record meetings, generate transcripts, and promise to save you from scribbling notes mid-call. But dig one layer deeper and they're solving pretty different problems - and picking the wrong one will cost you time and money. This breakdown covers everything that matters: transcription quality, pricing, search and retrieval, CRM integrations, and whether either tool is actually built for salespeople who need to improve, not just document, their calls. ## What Fireflies and Otter Actually Do Both tools join your Zoom, Google Meet, or Teams calls as a bot participant, record the audio, and produce a timestamped transcript with speaker labels. That's the core feature set they share. Where they diverge is in what they do after the transcript is generated. Fireflies.ai is built around search and collaboration. Its standout feature is a searchable meeting database - you can pull up every call where a specific topic was mentioned across your entire history. It also generates AI-written summaries, action items, and has a "Threads" feature for commenting on specific transcript moments. The target user is a team lead or operations person who needs to audit a lot of conversations efficiently. Otter.ai started as a personal transcription app and expanded into meetings. Its strength is real-time transcription that syncs across devices and is genuinely fast. Otter also introduced a live AI chat feature ("OtterPilot") that lets you ask questions about the meeting while it's still happening. The target user is someone who wants clean, readable notes with minimal setup. ## Fireflies vs Otter: Feature-by-Feature Comparison ### Transcription Accuracy Both tools have improved significantly over the past two years, but there's a real difference in edge cases. Fireflies handles multi-speaker environments and technical vocabulary better than Otter in most head-to-head tests. Otter's accuracy drops noticeably with accents or when multiple people talk over each other - a common scenario in sales calls where discovery gets heated. Neither tool is perfect. Both will misattribute speaker labels on the first call with a new participant. If you're relying on transcripts for coaching or deal review, you'll want to skim for errors before sharing anything. ### Search and Retrieval This is where Fireflies has a clear edge. Its global search across your meeting library is genuinely useful - if a prospect mentioned a competitor six weeks ago and you can't remember which call, Fireflies can find it in seconds. Otter's search works within individual transcripts but doesn't scale as well across a large library of recordings. For sales reps who want to pull up a specific objection a prospect raised three calls ago, Fireflies wins this category outright. ### Pricing Fireflies pricing: * Free: 800 minutes of storage, limited AI features * Pro: $10/user/month - unlimited transcription, AI summaries, CRM sync * Business: $19/user/month - video recording, advanced analytics * Enterprise: Custom Otter pricing: * Free: 300 monthly minutes, 30-minute max per conversation * Pro: $16.99/month - 1,200 monthly minutes, import audio files * Business: $30/user/month - admin controls, shared workspace * Enterprise: Custom For a solo closer who just wants transcripts, Fireflies Pro at $10/month is the better deal. Otter's 300-minute free tier is restrictive enough that anyone doing more than a few calls a week will hit the wall quickly. Otter Business at $30/user is priced closer to enterprise tools like Gong and Chorus without matching their sales-specific depth. ### CRM and Sales Tool Integrations Fireflies integrates directly with Salesforce, HubSpot, Pipedrive, Zoho, and others. It can auto-log calls and push notes to the relevant deal record. This works reasonably well for high-volume reps who don't want to manually update their CRM after every call. Otter's CRM integrations are thinner. HubSpot and Salesforce exist but the setup requires more manual work and the sync is less reliable. If CRM logging is important to your workflow, Fireflies is the safer pick. ### Collaboration Features Fireflies has a built-in collaboration layer - your team can comment on specific moments in a transcript, create clips, and share highlights. This is useful for managers reviewing rep calls or onboarding new hires with real call examples. Otter is more personal-productivity focused. Sharing transcripts is easy, but the collaborative annotation layer doesn't exist in the same way. ## Where Both Tools Fall Short for Salespeople Here's the honest problem with using Fireflies or Otter as your primary sales improvement tool: they tell you what was said, not what went wrong. You get a transcript. Maybe some auto-generated action items. What you don't get is: why did the prospect go cold after this call? Which moment killed the deal? What should you have said instead of what you said? Transcripts without analysis are just documentation. A 40-minute call produces 5,000+ words of text that most reps never read all the way through. The signal - the moment you lost control of the call, the objection you fumbled, the close you telegraphed too early - is buried in there somewhere, but you have to dig for it. This is the gap that tools like GradeMyClose are built to fill. Instead of producing a transcript and leaving you to audit it yourself, GradeMyClose analyzes the call across seven categories - discovery, objection handling, closing mechanics, tonality, and more - and surfaces the exact quotes where the deal shifted. If you lost the prospect's interest in minute 18 when you jumped to pricing before establishing value, it shows you the quote and gives you a script for how to handle that moment differently next time. That's a different workflow. Fireflies tells you what happened. A call grader tells you what to fix. ## Which One Should You Choose? ### Choose Fireflies if: * You're on a team and need shared meeting history with search across dozens or hundreds of calls * CRM auto-logging is a priority in your workflow * You do post-call reviews with a manager and want comment threads on transcripts * You're running demos and want to track competitor mentions across all your calls over time ### Choose Otter if: * You want the fastest possible real-time transcription with minimal setup * Most of your calls are 1:1 with clear audio and no heavy accents * You use transcripts primarily for your own reference, not sharing or team review * You want live AI questions during a meeting (Otter's OtterPilot feature is genuinely useful here) ### Consider pairing either tool with a call grader if: * You're trying to improve your close rate, not just document calls * You want to know why a deal went cold, not just what was discussed * You're a solo closer without a manager to review your calls If you want to see what structured call analysis looks like versus a raw transcript, try creating a free GradeMyClose account and paste a transcript from your last deal that didn't close. You'll see the difference in about 60 seconds. ## The Real Question Behind the Fireflies vs Otter Debate Most salespeople shopping these tools are asking the wrong question. The question isn't "which note-taker is better" - it's "what do I actually need from my calls after they're over?" If the answer is a searchable record of what was discussed, pick Fireflies. Better search, better integrations, better team features at a lower price point than Otter for most use cases. If the answer is fast, clean personal notes, Otter works well and the real-time transcription is genuinely impressive. But if the answer is I want to understand why I'm not closing and fix it - neither tool does that on its own. You need analysis, not just documentation. A transcript tells you what the prospect said. Call analysis tells you what they meant, where you lost them, and what to do differently on the next rep of that conversation. The reps who improve fastest aren't the ones with the cleanest transcripts. They're the ones who systematically review what went wrong and have a concrete script for handling it better. Fireflies and Otter can capture the raw material. What you do with it determines whether your close rate moves. ## Key Takeaways * Fireflies wins on search, CRM integrations, and price - the better default for sales reps doing high call volume on a team * Otter wins on real-time transcription speed and personal usability - better for 1:1 calls where you want notes fast and don't need deep search * Neither tool tells you why you lost a deal - they document calls, they don't diagnose them * Fireflies Pro at $10/month beats Otter Pro at $16.99/month for most sales use cases * If you're using either tool to improve your sales performance, pair it with structured call analysis - otherwise you're just building a searchable archive of missed closes Stop guessing what went wrong. GradeMyClose analyzes your sales calls across 7 categories and gives you word-for-word scripts to fix what's broken. Try it free - paste any transcript. Free: 10 Scripts That Close Deals Word-for-word scripts for the 10 objections that kill the most deals. Used by reps closing at 35%+. No spam. Unsubscribe anytime. See how YOUR calls actually score. Paste any sales call transcript and get an AI scorecard in 60 seconds. Free. See exactly where you're losing deals. Upload any sales call. AI scores 7 categories and gives you word-for-word scripts to fix what went wrong. Free - 3 grades every week.
Otter + Egnyte integration: automatically save meeting data where your team works. April 2, 2026 Meetings are where decisions get made. But too often, the context from those conversations gets lost. Notes live in personal folders, transcripts go unread, and critical details get buried or forgotten entirely. The result? Time wasted retracing steps, miscommunication across handoffs, and knowledge that walks out the door. The Otter + Egnyte integration fixes that. Watch it in action. What the integration does. Otter automatically creates a new text file in your Egnyte account for select meetings with the full meeting export, including the transcript, summary, insights, and a link back to the original Otter recording. Every meeting becomes structured content stored alongside your team's most important files in a governed, centralized knowledge system they're already using. How it works. Setting up the integration is simple. From the Otter desktop app, navigate to Integrations and select Egnyte. Once connected, you have flexible control over how your meeting content gets exported. Export behavior can be configured to run in two ways: * Automatically after every meeting ends * On demand, when a user shares a meeting to a designated Otter export channel This means your team can decide what gets saved and when, keeping your Egnyte workspace organized without unnecessary clutter. What gets saved to Egnyte. When a meeting is exported, Otter securely sends the content to a user-created Otter Exports folder in Egnyte. There, each meeting gets its own subfolder with a text file containing: * The meeting title * Full transcript * Automated summary * AI-generated insights and action items * A direct link back to the original meeting in Otter Everything your team needs to revisit a conversation without relistening to a single minute of the recording. Built for enterprise governance and security. Meeting data belongs inside your organization's trusted, permissioned environment. By routing meeting content through Egnyte, teams benefit from stronger governance, compliance controls, and security standards that Egnyte is built to provide. Your conversations stay inside the walls, secure and exactly where they should be. Why it matters. The cost of lost meeting context is real. Projects stall, decisions get punted, and new team members struggle to get up to speed. With the Otter + Egnyte integration, that context doesn't disappear, it becomes something your team can actually use. Here's what that looks like: * Revisit what was agreed on without scheduling a follow-up * Pull exact quotes to align on decisions * Confirm next steps from past meetings in seconds * Onboard new members by giving them access to a searchable history of relevant conversations Get started. The Otter + Egnyte integration is available for enterprise teams today, so your meetings don't just live in Otter, they live where your team actually works.
Otter.ai has appointed Kenny Scannell as Chief Revenue Officer to lead its global go-to-market strategy across sales, partnerships, demand generation and customer success. Scannell brings over 20 years of experience scaling revenue organisations at high-growth SaaS companies. Most recently, Scannell served as SVP of Global Sales at Rocketlane, tripling year-over-year growth. He previously held roles at Zoom as GM overseeing Marketing Solutions product lines, and senior positions at Klaviyo, ON24 and Citrix. Otter.ai, which has transcribed over 1 billion meetings for 35 million users, is positioning itself as an AI-powered conversation intelligence platform that transforms spoken conversations into searchable knowledge and actionable insights. The company is expanding beyond meeting transcription to capture institutional knowledge across enterprises.