How SupervizeAI tackles unconscious bias in QA

When we started BPR Hub, we weren’t thinking about building another “software tool.” We were thinking about the people behind supplier quality — the QA managers, engineers, and plant heads who keep manufacturing running. These are the unsung heroes who stay late preparing for audits, chase suppliers for missing certificates, and fix problems quietly before they hit customers. But they’ve been left behind. The tools available are either spreadsheets (fragile and frustrating) or enterprise systems (expensive and overwhelming). That’s why we built the AI Supplier Quality Management (AI SQM) Portal. It’s intuitive. It’s AI-powered. And it’s designed to make life easier for the people who carry the weight of supplier quality every day. If you’re part of a QA team and want to take back your time (and sanity), I’d love for you to check it out → https://www.bprhub.com/sqm #supplierquality #qualitymanagement #AISQM #AI #manufacturingcompliance #auditreadiness #productlaunch

We helped a client save $700,000. In 12 months. Using AI. All without: - complicated strategies - fancy tech stacks - hiring more people Just through smarter call center operations. Here's what we found when we audited their system: → QA teams drowning in manual reviews → Inconsistent evaluation standards → Zero visibility into customer pain points → Data buried in spreadsheets The solution? We implemented AI-powered conversation analysis. The results shocked everyone, even us: • $700k reduction in operational costs • 27,000 hours saved on evaluations • 100% visibility into every customer interaction • 3x faster response to customer complaints But the real transformation wasn't in the numbers... Their team finally had clarity. - No more guessing what customers wanted - No more missing important feedback The system worked automatically: 1. Flags compliance issues 2. Spots emerging trends 3. Identifies coaching opportunities 4. Predicts customer satisfaction 5. Highlights best practices Now they're handling 2x the call volume without adding headcount. Simple changes. Massive impact. Sometimes the biggest wins come from fixing what's broken, not adding more complexity. PS - What's your opinion on this? I'll be reading the comments!

Quality Thought of the Day – Day 5: STRATEGY, NOT LUCK 🎯 A real pain for quality professionals: problems show up faster than solutions. Time is always too short, teams are exhausted, customers push harder, and you feel like you’re only firefighting. The truth? Continuous improvement is not about luck. It’s about strategy, method, and execution. AI doesn’t make decisions for you, but it gives you: ✅ clarity in chaos, ✅ quick options when pressure is high, ✅ long-term vision to prevent issues before they explode. 💡 Ideas alone have no value. Execution is everything. A strategy without action is just another forgotten PowerPoint. 😉 📌 The moral: With Q-SOLVER AI as your support and Q-SOLVER PROMPT AI as your guide, problems stop being crises. They become opportunities for growth and customer trust. 🎁 A gift from www.calitateonline.ro – Practical AI Prompt for you: “You are an AI quality consultant. Analyze the situation where the organization does not implement AI solutions in quality management. Tell me: What are the main risks (operational, financial, competitive, reputational)? How does the lack of AI impact the speed and efficiency of problem-solving? What opportunities are we missing (in defect prevention, data analysis, audits)? What are the long-term consequences for customer relations and market position?”  

🔥 Most contact centers spend 30% more on labor just to audit calls manually - but the real cost is much higher. Manual QA isn’t just expensive. It’s a growth blocker. While companies invest in CX, they lose speed and scale because reviews are slow, partial, and error-prone. 📊 The numbers: Up to 40% of operational costs = manual call evaluations Only 1–2% of conversations reviewed → blind spots everywhere Feedback cycles 10x slower than AI Human audits miss up to 28% of compliance errors AI QA cuts time & cost by 60–80% with ROI in under 6 months 🔍 Why it matters: Manual QA is a hidden tax on agility and reputation. Leaders who automate first spot churn risks, compliance gaps, and sales friction before they hit revenue. Laggards stay stuck in labor bottlenecks. 💡 What works: Start with the touchpoints where compliance, sales, or service outcomes hinge on manual review. Automate QA there. Measure the before-and-after. Most companies see results in the first quarter - enough to fund further automation. 💬 Where in your workflow are you still relying on manual review - and what would change if you automated it? #AIAutomation #BusinessAutomation #ProcessOptimization #AIAgents #BusinessEfficiency #ContactCenter

Call QA is broken. Most teams still do random spot-checks, spend hours pressing play, and miss the patterns that hurt CX, violate policy, and drain margins. AI can fix that! 1) Full coverage, not samples. Instead of randomly picking up 2-5% for Quality assurance, AI sits in every call and analyzes all of them. 2) Consistent standards. Objective 100-point scoring (opening, verification, troubleshooting, policy, closure, etc). People always have biases: we tend to like some people more than others, which leads to subjective judgment. 3) Real context. Accurate transcription (incl. role split + non-verbal cues) and role mapping (CSR / Client) before evaluation. 4) Actionable outputs. Coaching notes + scores land in Sheets for reporting, alerts, and training loops. They can also be integrated to your Teams or Slack for instant feedback. 5) Operationally simple. n8n pipeline that just works when a new call appears —no manual babysitting. This is designed for scale (100s/1000s calls) with predictable cost & results. You can even later audit the overall results of a specific employee, specific dates or average score across the whole team. For sure, you can AI for this too! More about this I talk in my recent video (link in the description). #callcenter #qualityassurance #customerservice #automation #elevenlabs #n8n #speech2text #contactcenter

Compliance isn’t a checkbox. It’s a reputational risk. In today’s CX landscape, where every call can be a liability (or an opportunity), relying on manual call notes just doesn’t cut it anymore. 💬 Too vague. ⏳ Too time-consuming. 🧠 Too dependent on human memory. Enter AI-generated call summaries. These aren’t just a fancy add-on—they’re reshaping how contact centres protect themselves and elevate performance. 🔍 What’s changing? • Every call is captured with rich, contextual insight. • Notes are consistent, compliant, and instantly searchable. • Risk exposure from miscommunication or missing data? Dramatically reduced. 🧠 But here’s the real value: AI summaries free agents to focus on the conversation, not the admin. That’s how you improve both customer experience and compliance in one move. 💡 Want to challenge the “we’ve always done it this way” mindset in your CX team? Start by asking: ➡️ What would it mean if every customer call had an instant, accurate summary—aligned with your compliance policy? Because the cost of inaction isn’t just regulatory—it’s operational inefficiency, agent frustration, and customer trust on the line. 🛠️ At Puzzel, we’re helping contact centres unlock smarter, safer conversations with AI-powered summaries that do the heavy lifting. Ready to move from risk to resilience? #ContactCentre #CustomerExperience #Compliance #AI #CXLeadership #Automation #CallSummaries #Puzzel #ConversationalIntelligence

80%+ Quality Assurance coverage without adding headcount is intelligent automation. Traditional contact centers struggle with limited QA coverage, leaving performance gaps undetected. SupervizeAI changes the game: • Automated quality scoring frees supervisors from administrative tasks • Speech analytics at scale captures insights from every interaction • Strategic labor reallocation shifts focus from paperwork to coaching Our clients are seeing dramatic productivity gains while their supervisors finally have time for what matters most: developing their teams and driving strategic outcomes. Ready to modernize your contact center operations? Let's talk about transforming your QA process.

I had no idea there was an AI complaint management market, but now I’m realising how much sense it makes and the size of the opportunity for vendors more focused on resolutions as a key metric. I think the human and AI balance comes down to playing to each of their strengths. Humans excel at communication, empathy, emotional intelligence, and creativity. We interpret nuance and navigate ambiguity effortlessly. AI is great at structure, data analysis, and pattern detection, which makes it ideal for spotting root causes and predicting issues before they escalate. The fact that the AI complaint management market is expected to grow from $5.29B in 2024 to nearly $29B by 2033 is a clear indication of how important it will be to combine AI’s speed and insight with genuine human empathy. It will be interesting to see which vendors make it obvious they’re going after a slice of that $29B pie. It seems like a pretty tasty one! :) https://lnkd.in/eyJfbqmx

𝗗𝗔𝗧𝗔 𝗔𝗚𝗘𝗡𝗧𝗦 𝗙𝗢𝗥 𝗤𝗨𝗔𝗟𝗜𝗧𝗬 𝗖𝗢𝗡𝗧𝗥𝗢𝗟 Manufacturing operations generate an enormous amount of data every day — from machines running on the production line to inspection checks and quality audits. This data holds critical insights for ensuring consistent product quality, reducing defects, and improving overall efficiency. However, not every user needs the same information. Shop floor supervisors, quality managers, and customer experience teams all have different priorities. To make data actionable for everyone, the solution must be intuitive, role-specific, and capable of delivering the right insights at the right time. That’s where BDB Data Agents come in — intelligent, personalized assistants that empower every role to monitor, analyze, and act on data effectively. Attached video attached shows the working on Agents for Quality Control in manufacturing operations. https://lnkd.in/gjRatE7N #BDB #DataAgents #Qualitycontrol #QualityAssurance

🔍 Intent-Based IVR vs Traditional IVR — Which One Should You Choose? In our latest blog, we break down how intent-based IVR (which uses AI, intent recognition, natural language understanding) stacks up against legacy, menu-driven IVR systems. Some key takeaways: Intent-based IVR can understand what the caller wants rightaway (not force them through rigid menus), improving first-call resolution and user experience It enables personalization and context awareness (e.g. connecting the caller based on who they are and their history) Intent-based solutions tend to reduce operational costs, cut down on call transfers, and support more complex, open-ended requests Traditional IVR still has use in simple routing scenarios, but often frustrates callers with “press 1, press 2…” loops The blog helps you assess: based on your call volume, complexity of requests, and customer expectations — which IVR model is right for your business 💡 Curious to see a full comparison and decision framework? 👉 Check out the full blog here: https://lnkd.in/dyUQi3Dw