Case Studies

Real systems. Real results.

These are real-world scenarios where AI, automation, and integrations were applied to fix broken sales and operational processes.

Case#1

Key outcome: Service level stabilized at ~93%

Stopping service collapse without hiring

Financial services platform handling thousands of monthly customer interactions with a small support team.

Before optimization

Problem

Rising call volume overwhelmed the team
Long wait times and increasing missed/abandoned calls
Pressure to hire just to maintain service level

Risks

Revenue risk: poor service → lost trust → churn.

After optimization

Intervention

AI voice agent handling routine and overflow calls
Automated triage and routing to human agents
Knowledge base built for common queries

Optimization outcome

Service level stabilized at ~93%
+23% improvement in service level
No additional hires required

Why it worked: Low-value repetitive calls were removed from human workload. Humans focused only on high-context problems, where they add real value.

Case#2

Key outcome: 93% of calls handled automatically

Eliminating inbound noise and admin drag

Recruiting company with high inbound call volume and heavy manual post-call work.

Before optimization

Problem

90% of inbound calls were irrelevant or spam
Recruiters spent ~45 minutes writing summaries after calls
Constant interruptions slowed candidate processing

Risks

Lost productivity and operational drag.

After optimization

Intervention

AI receptionist filtering and handling inbound calls
Keyword-based routing with intent detection
AI-generated call notes and summaries

Optimization outcome

93% of calls handled automatically
90% reduction in calls reaching humans
Summary time reduced from 45 to 15 minutes (−67%)
Transfer time reduced by 50%

Why it worked: Noise was eliminated at the entry point so only relevant work reached people.

Case#3

Key outcome: 30% faster forecasting process

Cutting forecasting time and deal uncertainty

B2B financial services company with long, complex sales cycles.

Before optimization

Problem

Sales data fragmented across tools
Managers manually reviewed calls
Forecasting required reconstructing deal history

Risks

Slow decisions, missed deal risks, and inconsistent forecasts.

After optimization

Intervention

AI call transcription and summaries
Automated deal risk detection
CRM integration as a single source of truth
Shared visibility across teams

Optimization outcome

30% faster forecasting process
30 hours/week saved on deal reviews
Earlier detection of deal risks

Why it worked: Sales conversations became structured searchable data. Managers stopped guessing and operated on real signals.

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Case#4

Key outcome: +67% increase in deals per rep

Turning conversations into a growth engine

Fast-growing B2B SaaS company scaling its sales team rapidly.

Before optimization

Problem

Knowledge lived in people, not systems
New reps took months to ramp
Inconsistent handling of objections and competitors

Risks

Inconsistent execution and slower scale.

After optimization

Intervention

Centralized call capture and analysis
AI-generated summaries and insights
Structured tracking of competitor mentions
Reusable playbooks from real conversations

Optimization outcome

+67% increase in deals per rep
+13% increase in win rate
Onboarding time reduced 3×

Why it worked: Learning became systemized. Reps learned from real data at scale instead of relying only on individual experience.

Case#5

Key outcome: +5% conversion increase at key funnel steps

Fixing conversion drop-offs in the funnel

Online financial platform with high inbound traffic and multi-step application process.

Before optimization

Problem

Users dropped off at key friction points
Unclear steps and waiting for support
Missing information blocked progress

Risks

Lost applications and increased support load.

After optimization

Intervention

AI chatbot for real-time assistance
Automated customer communication (SMS and reminders)
Dynamic workflow based on user data

Optimization outcome

+5% conversion increase at key funnel steps
Reduced need for human support
Faster processing

Why it worked: Automation was applied exactly where users got stuck. That is where conversion impact is highest.

What these cases have in common

Speed replaced delay
Systems replaced manual work
Data replaced guesswork

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