Insight7

Hiring just got easier 🎉 Resumes only tell you so much. Candidate Screening helps you evaluate how potential hires actually perform - on soft skills that matter for sales and support roles - before you make the call. From initial hiring to new hire ramp-up and skills assessment, you get a structured way to make more confident hiring decisions. How it works: ✅ Simulate real sales or support scenarios tailored to your business ✅ Get results auto-scored against your own criteria ✅ Assess candidates on verbal or written skills See it in action 👇

Coaching Autopilot is here! Your team makes mistakes and they need feedback now - not the next week. Coaching Autopilot makes that happen, automatically. ✅ Detects skill gaps from real conversations ✅ Builds personalized training for each gap ✅ Assigns it to the right people ✅ Tracks improvement on autopilot Perfect for sales and customer service teams ready to level up. Watch this demo to see it in action.

Introducing Alert Workflows in Insight7 🎉 Stop discovering important conversation moments days after they happen. Alert Workflows monitors your calls and chats automatically - notifying your team the instant something critical occurs. ✅ Keyword & event tracking ✅ Real-time performance alerts ✅ Team collaboration on alerts ✅ Email + Slack notifications See it in action 👇

Revenue Intelligence is here! Closing deals in one conversation? We built something for that. Revenue Intelligence helps sales teams in Financial Services, Healthcare, and beyond turn more calls into conversions, without adding complexity. Here's how it works: → Monitor how prospects move through Opening, Discovery, Proposal, and Close stages → Spot where deals stall and get clear steps to move them forward → Learn from your best closers and apply their winning tactics across your team Want to see it in action? Check out our 2-minute walkthrough and try it yourself at insight7.io

Most CX leaders are waiting for the green light that never comes. Meanwhile, customer experience suffers, and the business loses ground. Ian Golding breaks down how to stop over-theorizing and start building a structured approach that gets leadership asking for your help instead of ignoring your recommendations. It's about maneuvering yourself into the position where you become the solution, not just another voice in the room. Full conversation on YouTube 👇 youtu.be/MSPvfJFUQLY?si…

Today, we’re announcing our biggest product yet - AI Coaching 🌟 🎊 Most of the work we do every day isn’t in docs or dashboards. It’s in conversations. AI Coaching is a natural extension of our work @Insight7_ - turning conversations into insights & decisions. But this time, its about people.

Introducing AI Coaching & Roleplay🚀 Traditional coaching doesn't scale. Sales reps wait weeks for manager feedback. New hires practice on live calls. Skill development becomes a bottleneck. We built AI Coaching & Roleplay to change this: unlimited, on-demand practice with realistic AI personas that adapt to your style, plus instant feedback on empathy, active listening, questioning, and more. Think of it as having an expert coach available 24/7 for every team member, without the managerial overhead. Perfect for sales, customer success, and support teams looking to accelerate ramp time, standardize training, and build confidence before high-stakes customer conversations. To celebrate this launch, we're giving away 20% discount on annual plans to interested teams. Just comment "Coaching" or Book a Demo today.

A lot of CEOs are thinking about AI. Some are adopting tools. Others are evaluating, building business cases, running pilots. A few are still exploring, separating hype from reality. But they all miss the same thing. The biggest unlock for AI isn't the tools themselves. It's the data and context you feed them. And most companies are skipping this step entirely. The default playbook looks like this: Buy the latest AI tool. Implement the newest model. Chase what competitors are doing. New sales AI. New customer service automation. New analytics platform. Each promises transformation. Each costs six or seven figures. Each takes months to implement. And most deliver incremental improvements at best. Why? Because you're trying to build on empty ground. Here's what most miss: The answer is already in your business. It's in the thousands of customer conversations happening every week. Sales calls. Support tickets. Success reviews. Onboarding sessions. These conversations contain everything you need: Why customers aren't buying Where reps excel or struggle What messaging actually resonates Which objections kill momentum What's changing in the market The insights that could transform your revenue are already there. You're just not extracting them. Even when companies analyze conversations, insights get trapped. Leadership sees fragments, not patterns. Managers can't scale coaching. Reps don't improve because feedback is vague and delayed. Data is only valuable when it flows to the people who can act on it. The companies that will win in 2026 won't be chasing the newest AI model. They'll systematically unlock the intelligence buried in their customer conversations. These companies will: Evaluate 100% of customer interactions, not just samples Surface patterns across every touchpoint Generate personalized coaching at scale Flow insights automatically to leadership for decisions This isn't about replacing human judgment. It's about giving your people the intelligence layer they need. So instead of asking: "How do we use AI to grow?" Ask this: "Are we using the data we already have?" Before you buy another AI tool, answer these: What % of our customer conversations are we learning from? Do insights reach the people who can act on them? Can we turn conversation data into systematic coaching? If the answer is no, you're not ready for more AI tools. You're ready for conversation intelligence. The companies that figure this out won't just win in 2026, they'll build an advantage that's impossible to copy. Learn how we're solving this at insight7.io

First update of 2026. Today, we're announcing Insight7 Call Performance Metrics 🎉 With Call Performance Metrics, it's easier than ever to evaluate, measure, and improve how your team handles conversations - Sales, CX and many other cases where performance matters. Key features: ✅ Track essential call metrics like talk time, words per min & filler words ✅ Score performance against benchmarks based on call type ✅ Get actionable suggestions for improvement with evidence

Today, we're announcing Insight7 Call Performance Metrics 🎉 With Call Performance Metrics, it's easier than ever to evaluate, measure, and improve how your team handles conversations - Sales, CX and many other cases where performance matters. Key features: ✅ Track essential call metrics like talk time, words per min & filler words ✅ Score performance against benchmarks based on call type ✅ Get actionable suggestions for improvement with evidence Here's a quick demo

Teams came to us for speed. They had call transcripts and needed a fast TL;DR they could act on. So we built summarization. It worked. People used it heavily. Then the same question kept coming up: “Can we control the format? They wanted summaries that fit how they already worked: • an email follow-up ready to send • an exec one-pager for leadership • a checklist with prioritized actions What they were really asking for wasn’t better text. It was predictable structure. This mattered more than we expected. Customers were feeding summaries into CRMs and ticketing systems. When headings changed or fields went missing, automations broke. Enterprise teams ended up manually fixing AI outputs. Legal and compliance teams couldn’t rely on summaries that didn’t follow a guaranteed format. Our first attempt was the obvious one. We took the user’s template, added it to the prompt, sent everything to one big model, and returned the output. It demoed well but failed in production. Headings drifted. Placeholders disappeared. Outputs changed run to run. Across 1,000+ templates, only ~62% actually followed the structure customers asked for. We tried tightening prompts. Adding examples. Asking the model to “follow headings exactly.” None of that fixed the core issue. So we changed the approach entirely. Instead of hoping the model would behave, we made structure non-negotiable. Templates stopped being instructions and became contracts. We parse and sanitize the template. Turn it into a machine-checkable schema. Generate against that schema. Then validate the output, and repair it if something’s missing. Code defines the shape. AI fills in the content. After this change, structural adherence jumped to 92%. The business impact was immediate: • 75% fewer manual edits • Reliable CRM and ticketing automations • Faster enterprise adoption • Legal and compliance teams actually trusted the output. That 30-point jump wasn’t about nicer summaries. It was the difference between AI that looks helpful and AI that can run inside real workflows. Big lesson: If format matters, prompts aren’t enough. Make structure explicit. Validate outputs mechanically. Treat user intent as a product requirement, not a suggestion. That’s how summaries stop being “AI-generated text” and start powering business-critical systems.

Ever tried to get an AI to actually follow instructions and sound human? Yeah. That was the hardest part of building our AI coaching system. We assumed the challenge would be “make it sound natural.” Turns out the real problem was control. How do you get an AI to strictly follow rules sometimes and improvise other times, without breaking everything? Because AI coaching isn’t one thing. It needs to switch between being: • a strict examiner • a supportive trainer • a guided conversational partner All inside the same system. What most people get wrong: They think one big prompt can handle all of this. It can’t. That’s how you get skipped questions, hallucinated facts, broken JSON, and conversations that go nowhere. Our first attempt did exactly that. One giant prompt. One model. One mess. Only ~60% of sessions followed the script. Some assessments literally made things up. Others forgot to ask key questions. The breakthrough came when we stopped trusting the AI. We flipped the model: Code is the boss. AI is the worker. Instead of one “smart” AI, we built a system with clear boundaries: • Code defines the structure • AI handles narrow, well-scoped tasks • Evaluation happens with rules, not vibes Now every interaction follows a dialogue graph. Each step has: • a clear purpose • a defined level of freedom • validation rules • recovery paths when things go wrong Strict when facts matter. Flexible when practicing skills. Guided when conversations need to hit goals without feeling scripted. The result: • Compliance jumped from 62% → 91% • Hallucinations dropped below 2% • Conversations became explainable, auditable, and trustworthy • Practice felt natural without losing direction The big lesson: If you need AI to be both controlled and human, don’t push that responsibility into prompts. Put structure in code. Give the AI clear jobs. Design for multiple modes from day one. Good AI coaching isn’t about one model doing everything. It’s about building a system that knows when to be strict, when to adapt, and when to say “I don’t know.” That’s what makes it actually useful.

People keep asking, “Can AI replace human coaches?” The real question is: can human coaches see what AI systems see? That’s where the conversation actually gets interesting. Human coaching today is like a basketball coach who only watches the last game. Feedback comes from whatever they reviewed most recently, not from actual patterns across time. AI isn’t smarter, it just sees every game, every play, every mistake, and every win across the entire season. Humans give feedback based on memory. AI gives feedback based on evidence. Another uncomfortable truth: managers unintentionally coach reps to sell like them. Their style, their personality, their habits. It works for some people and completely derails others. AI breaks the clone problem by coaching each rep based on what works for their buyers, their territory, their situation. Human coaching compounds advantage. AI coaching levels it. And then there’s coaching latency - the silent killer. A rep makes a mistake on Monday. They get feedback next Wednesday. By then, they’ve already repeated the error ten times. AI catches it the same day and gives feedback before the next call. The mistake never becomes a habit. Elite athletes train in safe environments. Sales reps practice on real customers. AI finally fixes that by giving reps unlimited simulation without burning opportunities. There’s also a category of insights humans simply cannot perceive: • tiny shifts in sentiment • micro-patterns across dozens of calls • a 10% change in speaking speed • filler word frequency • cross-call trends that reveal root problems It’s not a matter of skill. It’s a matter of perception. Humans see snapshots. AI sees the full signal. And this doesn’t diminish managers, it amplifies them. When AI handles pattern detection, feedback, and repetition training, managers finally get to do the work only humans can do: lead, motivate, mentor, and develop people. Reps also get coaching that’s fast, fair, consistent, and tailored to the way they sell, not the way someone else used to sell five years ago. At Insight7, we believe every revenue team should access coaching that goes beyond human limitations: • coaching that sees what humans can’t • coaching that happens instantly • coaching that treats every rep equally • coaching that learns from every conversation across the org • coaching that creates real practice, not just post-mortems Your teams generate thousands of conversations every month. Hidden inside them are insights no human coach could reasonably uncover alone. Human coaches scale by multiplying themselves. AI coaches scale by revealing patterns humans can’t see. And that single capability reshapes the entire coaching model.

Conversations are messy. They carry emotion, tone shifts, hesitation, sarcasm, signals humans catch instantly but machines often miss. We wanted to close that gap, and that meant building a system that could evaluate conversations with accuracy and evidence. The challenge was to design something that could score AI agents against clear criteria, adapt to different conversational styles, and explain why it gave the score. Not just a number - proof. The hardest part was teaching the system to understand nuance. Real conversations aren’t clean. People talk over each other, pause awkwardly, switch tone mid sentence, or speak in fragments. Capturing that required tighter rubrics, sharper definitions, and fast iteration. Along the way, it became clear that quality isn’t an afterthought. It’s a structure. If criteria are vague, the scoring drifts. If evidence isn’t extracted, trust disappears. Every refinement made the system more consistent, more confident, more useful. Edge cases ended up being the real teachers - background noise, clashing speakers, low empathy moments, escalations. Each one forced the model to become more robust and handle conversations the way humans actually have them. The system now works through a simple flow: Conversations get transcribed across 60+ languages → evaluated through structured rubrics → scored with weights → supported by quotes, summaries, and evidence. Teams get a clear breakdown of what happened and why. Since launching, product, sales, CX, and research teams have used it to spot weaknesses, coach their agents, improve AI performance, and make faster decisions backed by evidence. The insights they uncover translate directly into better customer experience and stronger outcomes. What began as a one week sprint has grown into one of our flagship products - a system shaped by human nuance, built on structured evaluation, and designed to bring clarity to conversation data. This project became a reminder of why we build: to help people understand data, discover insights, and act smarter.

Most teams think transcript analysis is simple: Record call → get transcript → get insights. But conversational data is messy, unstructured, and far from plug and play. The hidden challenges no one talks about: • Different call types need different handling • No built-in numerical data • Disjointed transcripts • Messy formats (PDFs, audio, mixed speakers) And the biggest misconceptions - • “AI can do it all.” • “All transcripts are the same.” • “Readable = accurate.” • “Any quote represents the whole call.” None of these are true. Conversational data is complex. Names get misidentified. Important context gets scattered. And the structure rarely fits how teams actually need to analyze it. The real pipeline looks like this: Clean → Process → Identify • Clean noise • Segment conversations • Identify speakers + context Miss any of these steps and your insights fall apart. Practical techniques we use at Insight7: • Detecting call types • AI-powered metadata extraction • Index parsing for structure • Hybrid NER (LLM + rules) • LLM-based recovery for broken transcripts And the impact is massive! Teams using transcript intelligence uncovered: • Hidden churn signals • Integration pain points blocking deals • Broken support workflows • Real customer language for marketing • Coaching gaps managers couldn’t hear live Yes - you can extract goals, intent, themes, sentiment, and opportunities from transcripts. You just need the right system. That’s what Insight7 automates: • Actionable insights • Recurring themes • Customer pain points • Visual dashboards • Collaboration for Sales, CX, Product & Research Across 60+ languages - securely. Your conversations are full of insights. But raw transcripts won’t get you there. A refined pipeline will.

Most companies think they’ve solved conversation intelligence: “We record calls. We have AI. We’re good.” Reality? You’ve built the oil well. You’ve got barrels of crude. You haven’t built the refinery. 🛢️⚡ Recording a call = pumping oil into a barrel. Useful? Sure. Valuable? Not yet. Kerosene lights lamps, but gasoline, lubricants, and asphalt? Untapped potential sitting in storage. Traditional platforms extract one thing: sales insights. They transcribe, tag, and feed coaching moments. That’s 15% of what’s in the barrel. The rest? Marketing, CX, Product, L&D. Sitting there… wasted. Every conversation contains: Objection patterns & deal signals → Sales Early churn signs & onboarding gaps → Customer Service Actual customer language & brand perception → Marketing Top performer strategies → L&D Your “single product” approach misses 85% of value. The problem isn’t capturing calls. It’s distribution. 100% goes to Sales. Marketing, CX, Product? Empty-handed. That’s a warehouse of crude, not a refinery. A refinery processes the same crude multiple ways: Gasoline for cars. Jet fuel for airports. Lubricants for factories. Each product goes where it’s needed. Conversations work the same way. Insight7 built the refinery model: Parallel processing, multiple lenses, each team gets refined insights automatically. Sales learns deals. CX learns churn signals. Marketing learns positioning. L&D learns coaching. Shift from “conversation intelligence” → “organizational intelligence.” Stop storing barrels. Start building refineries. Your conversations are crude oil. Are you extracting the full value?

Insight7 is partnering with UT Dallas’ Center for Professional Sales to transform how students learn sales discovery. Instead of scripts, 60 - 80 students each semester conduct deep interviews with executives, learning to ask better questions and uncover real business insights. By their eighth interview, students aren’t just asking different questions, they’re thinking differently. They build curiosity, develop business acumen, spot market patterns, and synthesize insights across hundreds of conversations. The challenge is most AI tools can’t handle this scale. The partnership with Insight7 changes that, enabling students to analyze 200+ interviews at once, track their questioning evolution, and see patterns that would otherwise stay hidden. This is more than a course. It’s a transformation teaching students to think critically, uncover insights, and prepare for leadership in business, powered by AI and experiential learning. #SalesEducation #AI #UTDallas #BusinessLeadership