In 2025, most Voice AI Agents still sound robotic, fail to understand intent, and struggle to deliver outcomes at scale.
For us at SquadStack, the goal was always straightforward: enable conversations that genuinely feel personalised, drive revenue, and do so at scale. Our journey from a remote-ops plus human talent model to an AI-first approach was not just an update; it was a transformation.
Our shift to SquadStack.ai is not just a domain shift, but marks our commitment to seamless, outcome-oriented customer conversations, where intelligence and execution are built right into the core.
The Problem: As CX Scales, It Breaks
These are some of the challenges we kept seeing over the years, things our customers faced, and we experienced ourselves while building and scaling CX:
Slow Responses: Leads reached within 5 minutes are 21× more likely to convert, yet 73% are lost when replies are delayed.
Missed Opportunities: Over 44% of sales reps report being too busy to follow up with every lead, so valuable prospects frequently slip through.
Rising Costs: Scaling agent teams can cost as much as $4,900 per month per person, making it expensive to deliver quality at scale.
Lack of Personal Touch: 66% of customers are ready to leave brands that fail to offer personalised experiences.
Fragmented Workflows: Multiple tools force teams to juggle calls, chats, and emails, causing lost context and repeated questions.
Robotic Interactions: Bots that misinterpret languages and accents contribute to customer frustration, making experiences feel impersonal.
A single delayed callback, like waiting 6 hours for a loan response, can turn interest into a lost opportunity. We've seen it firsthand at SquadStack: a lead drops off because no one followed up fast enough, or a callback happens hours too late. At first, these seem like small misses. But it kept happening. And slowly, we saw how much trust and revenue were being lost, not because teams didn’t care, but because the system just couldn’t keep up.
That’s when it really hit us: what if we could fix this at the root? Not with more people, not with co-pilots, but with smart, unified workflows and AI that could actually take ownership. This realisation became the starting point for what we’ve built today: the Humanoid AI Agent Stack.
What We Did and Didn’t Build: A Full-Stack AI Platform, Not a Bot
To address these recurring gaps, we developed the Humanoid AI Agent Stack. We wanted this stack to be a full-stack solution which blends truly human-like conversations with reliable, always-on automation. To achieve this, we built the two tightly integrated layers into the stack:
Humanoid AI Agent:
Trained on 900M+ Minutes: Learns tone, intent, emotion, and context from real interactions, ensuring every conversation feels human-like.
Language & Accent Adaptability: Handles Indian languages, dialects, and even noisy backgrounds for natural engagement.
Real-Time Updates: Instantly reflects new info or SOPs, with no retraining.
Handles Complex Queries: Manages layered, multi-turn conversations with empathy and accuracy, even at scale.
Execution Stack:
AI Lead Manager: Prioritises and routes leads so high-value opportunities never get missed.
Omnichannel Engagement: Connects with customers via voice, WhatsApp, SMS, and email, all in a single, seamless experience.
Instant SOP Management: No-code tools enable quick, compliant process updates, immediately visible to the AI agent.
Continuous Improvement: Features A/B testing, CRM sync, detailed audits, and Voice of Customer feedback for non-stop optimisation.
Scalable & Always-On: Handles lakhs of conversations 24/7, supporting your business as it grows with reliable, human-like support.
How We Built It: The Tech Behind the Humanoid AI Agent Stack
When we set out to build the Humanoid AI Agent Stack, the goal was clear: every part had to contribute to conversations that feel natural, fast, and reliable, and every revenue opportunity had to be seized.
We didn’t start with a pre-packaged solution. We assembled the stack component by component, optimising for India-first use cases and high-volume production environments.
Here's what powers it:
Speech to Text (STT) The STT tech built in house converts spoken input to text in under 300ms. Supports Hindi, English, and Hinglish. Vernacular languages are already in progress.
Voice Activity Detection (VAD) Detects when a user starts and stops speaking, even through background noise and acknowledgements, so the agent knows exactly when to listen and respond.
NLU / LLM The core intelligence layer. It interprets tone, context, and user intent. We use multiple LLMs based on use cases, enabling features like emotion recognition, multi-turn context, function calling (e.g., sending SMS mid-call), and more. To ensure we find the best pathway for each resolution, we test various LLMs and match the ones best for each particular use case.
Temperature Control This adjusts how creative or strict the agent is. We keep it at 0.5 to balance structure with conversational flow, letting agents stick to the script without sounding robotic.
Text to Speech (TTS) We have 20+ human-like voices to choose from, powered by ElevenLabs and our in-house proprietary TTS engine. We can even replicate the voices of our top-performing human agents for consistency and warmth.
Orchestrator This is what keeps everything working together in real time, managing audits, syncing workflows, handling channel shifts (voice to WhatsApp), and routing leads with no lag.
Why It Works at Scale: A Decade of Ops → The Foundation for Autonomous AI
What sets this stack apart is the operational groundwork behind it. We spent a decade building and refining CX systems with our 10,000+ remote agents, resulting in over 3 billion annotated customer interactions. This wasn’t just data for us; it’s contextual knowledge mapped into real business outcomes, fueling models that truly “think” and adapt like your best people.
Handles lakhs of conversations every day, with no manual oversight needed.
Live with top fintech, D2C, edtech, and healthcare brands, driving a 2× increase in connect rates and 30% more qualified leads.
99.9% uptime SLA, plus 100% compliance (DND, ISO, SOC 2), so you never have to trade reliability for scale.
Real-World Results: Impact Across Leading Brands
For clients like Shiprocket and other leading brands, the Humanoid AI Agent Stack delivers results that speak for themselves:
2× improvement in lead connectivity
30% more qualified conversions
All managed by AI, no human intervention required
Additional Business Impacts:
3-day go-live: Fast deployment, immediate value.
90%+ response accuracy: AI agents consistently deliver context-aware, correct answers.
Zero retraining required: Process or SOP changes reflect instantly in conversations.
Indian-accent-tuned speech and intent models: Conversations remain natural and credible nationwide.
Sub-1-second response time: Customers never wait.
Real-time orchestration: Messages, audits, and workflow sync instantly.
99.9% uptime SLA across all verticals
These results show what’s possible when advanced AI, operational wisdom, and robust automation unite.
The Future of CX: Fewer Agents, Smarter Conversations
We didn’t just build another AI agent; we built a future where customer journeys are no longer limited to a single team or a single interaction. With the Humanoid AI Agent Stack, we wanted to build a future where businesses don’t have to choose between quality and scale, where conversations can be consistently, contextually, and timely handled by technology that learns and improves continuously.
Ready to Experience It?
The Humanoid AI Agent Stack is already transforming businesses large and small.
Plug it into your CRM and go live in three days. See how every customer conversation can be timely, personal, and outcome-focused, without getting lost in the shuffle.
Experience the difference: outcome-driven conversations, at scale, powered by technology that quietly works in the background, so teams can focus on what matters most.
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