Applied AI
Studio

The Challenge

A Series B fintech company's internal team had spent months trying to implement AI in customer support—with little to show for it. Support costs were climbing, C-SAT scores were stagnant at 48%, and the CEO knew they needed outside expertise to get it right.

Our Approach

We started with a full diagnostic: process mapping, tooling audit, and gap analysis in collaboration with their internal team. Then we got to work:

• Built the foundation: Set up comprehensive evaluation frameworks to measure quality and reliability before scaling
• Redesigned the org: Helped hire the right talent and restructured workflows for AI-first operations
• Deployed the systems:
  • AI copilot for agent assistance
  • Mixture-of-experts architecture handling different customer problems and use cases
  • Custom workflow creation tools for non-technical teams
  • Control tower for real-time monitoring and intervention
• Scaled with confidence: Implemented A/B testing infrastructure and scaling pipelines to expand coverage safely

The Outcome

Over 6 months, we transformed their support operations:

• C-SAT increased from 48% to 94% (2x improvement)
• Cost per ticket dropped from ₹23 to ₹13 (44% reduction)
• 80%+ tickets now handled automatically without sacrificing quality

Not a POC. Not a pilot. A production system handling real customer volume with measurable business impact.

The Challenge

An enterprise financial services provider was running a massive outbound calling operation—thousands of agents making B2C calls daily across 7 languages to sell their services. The operation faced brutal realities: extreme background noise, spam call rejections, and massive variability in call duration (5 seconds to 7 minutes). With costs mounting and scalability limited by hiring, they needed a fundamentally different approach.

Our Approach

We didn't try to automate everything on day one. We started with rigorous diagnosis:

• Mapped the operation: Understood their business, processes, scripts, success criteria, team structure, and existing tools
• Identified the right scope: Analyzed call types and isolated the 35% that could be reliably automated with current AI capabilities—avoiding the trap of overpromising
• Built production-grade infrastructure:
  • Custom voice pipeline optimized for Indian accents and multilingual conversations
  • Aggressive latency reduction to keep conversations natural
  • Comprehensive evals pipeline and model selection process
  • Live transcription with context engineering for dynamic responses
  • A/B testing framework to validate quality before scaling
  • Automated call routing with human-in-the-loop for edge cases
  • Workflow creation tools for non-technical teams

The Outcome

We deployed AI calling at scale—but only where it could reliably deliver:

• Focused on 35% of calls that met quality thresholds
• Trained internal teams to continuously evaluate and expand coverage as AI improves
• Reduced cost from ₹24 to ₹9.50 per active call minute (60% reduction)
• Handling 500K+ calls per month with consistent conversion quality

The key wasn't automating everything. It was automating the right things reliably, at massive scale.

The Challenge

A large manufacturer and exporter producing 100,000+ units daily faced a critical problem: their entire QC process relied on manual human inspection. This created a perfect storm of issues—high employee turnover, constant training needs, and worst of all, subjective quality decisions that led to costly international returns and reputational damage. When your QC inspector has a bad day, your customers pay the price.

Our Approach

We built an automated vision system that removes human subjectivity from quality decisions:

• Understood the ground truth: Deep diagnosis of the business, QC parameters, and how human inspectors actually evaluate quality—not just what the manual says
• Designed the capture system: Deployed commercially available cameras to capture the QC process without disrupting production flow
• Built the dataset: Collected real production data and worked with experienced QC staff to create training labels that captured institutional knowledge
• Engineered the pipeline:
  • Custom preprocessing pipeline optimized for factory lighting and camera positioning
  • Developed a vision model trained specifically on their products and defect types
  • Iterated through multiple model generations with continuous feedback from the floor
• Deployed at scale: Integrated into production workflow with real-time detection and flagging

The Outcome

Automated quality control that actually works in harsh factory conditions:

• 92%+ accuracy in defect detection with ongoing improvements
• 40% reduction in QC costs post-implementation
• Eliminated subjectivity from quality decisions—consistent standards across all shifts
• Reduced international returns by catching defects before shipping

The system doesn't just match human performance. It provides consistency humans can't—the same rigorous evaluation on unit 1 and unit 100,000.

The Challenge

A traditional luxury goods retail chain was expanding rapidly—but flying blind. They couldn't accurately track how many customers visited each store, understand customer movement patterns, or monitor how employees engaged with high-value clientele. Their solution? A control tower team manually reviewing footage across all locations daily. Expensive, slow, and impossible to scale.

For a luxury brand where every customer interaction matters, this lack of visibility was costing them conversions and brand reputation.

Our Approach

We built an AI-powered analytics system that turns camera footage into actionable retail intelligence:

• Understood the real problems: Visited stores, spoke with teams on the ground, and identified what metrics actually drive business decisions
• Designed the infrastructure: Determined optimal camera angles and types for accurate capture without disrupting the luxury shopping experience
• Built custom models:
  • Developed vision models trained specifically for retail environments and luxury store layouts
  • Optimized for efficiency to enable edge deployment
  • Created pipelines for people counting, movement tracking, and behavior detection
• Deployed at the edge: Models run locally in each store to reduce costs and latency
• Centralized monitoring: Built a control tower dashboard for automated detection and real-time alerts across all locations

The Outcome

Real-time intelligence that transforms store operations:

• 95% accuracy in visitor counting and tracking
• Heatmaps showing customer movement patterns and high-traffic zones
• Employee engagement metrics tracking customer interactions
• Time-based analytics revealing peak hours and day-of-week patterns
• Automated monitoring replacing manual footage review

No more guessing. No more manual reviews. Just data-driven decisions on staffing, layout, and customer engagement—delivered in real-time.

The Challenge

A late-growth stage B2C company had built out their entire voice AI infrastructure—inbound bots and callback systems handling thousands of daily conversations. But they hit a wall: high background noise in customer calls was breaking their transcription accuracy, which cascaded into poor LLM responses downstream. Their AI could handle the conversation logic, but couldn't hear the customer clearly enough to matter.

They had one major advantage: a massive database of recorded calls with real-world noise patterns.

Our Approach

We built a custom noise cancellation model trained on their actual operating conditions:

• Diagnosed the root cause: Worked with business teams, analyzed workflows, and listened to hundreds of customer calls to understand exactly where transcription was failing
• Aligned on success criteria: Defined what "good enough" meant for their conversion metrics and user experience
• Built the training pipeline:
  • Gained access to their voice data repository
  • Cleaned and curated a high-quality dataset representative of real customer environments
  • Trained a custom noise cancellation model optimized for their specific noise profiles
  • Iterated on model architecture to achieve sub-20ms latency
• Deployed in production: Integrated seamlessly into their existing voice AI stack

The Outcome

A lightweight model that fundamentally improved their voice AI performance:

• Sub-50ms latency enabling real-time processing without noticeable delays
• Improved Voice Activity Detection (VAD) with better turn-taking in conversations
• Higher transcription accuracy leading to more contextually appropriate LLM responses
• 15% improvement in conversion rates from better customer experiences

The model's tiny footprint meant it could run in their existing infrastructure without adding cost or complexity—just better results.