Case Studies

Real Delivery.
Hard Numbers.

Every engagement below started with a real problem. These are the results — no vague claims, no cherry-picked testimonials.

73%

AI Query Time Reduction

99.1%

Offline Data Capture

58%

Case Handling Improvement

0 min

Downtime During Migration

01

Agentic AI · Enterprise

AI agent pipeline cut query resolution time by 73%

Results

73%
reduction in query resolution time
< 1.8s
average AI response latency
94%
confidence-passing responses (no human review needed)
0
policy data transmitted to any external cloud

Situation

A financial services enterprise was handling thousands of internal policy queries per week through email — slow, inconsistent, and consuming analyst hours that should have been spent on higher-value work.

Approach

We built a private multi-agent AI pipeline running on-premise SLMs inside their existing infrastructure. The system classifies intent, retrieves from a policy knowledge base, generates a grounded response, and routes edge cases to a human analyst — with a confidence score on every output.

02

Mobile App · Offline-First

Field inspection app achieved 99.1% data capture rate in zero-connectivity zones

Results

99.1%
data capture rate in offline environments
3-4 days → < 2 hours
data lag improvement post-sync
82%
reduction in transcription errors
6 weeks
from discovery to production deployment

Situation

A logistics and infrastructure company had field teams operating in remote areas with no reliable internet. Paper-based inspection forms were causing a 3–4 day data lag and frequent transcription errors.

Approach

We built a native Android application with full offline-first architecture. All inspection data is captured to a local encrypted database, syncs automatically when connectivity returns, and includes conflict resolution for multi-user field teams.

03

Salesforce · AI Integration

Salesforce AI integration reduced case handling time by 58%

Results

58%
reduction in average case handling time
3.2×
increase in cases resolved per agent per day
91%
agent satisfaction with AI-generated drafts
100%
data kept inside Salesforce perimeter

Situation

A B2B services company had service agents spending 40% of their time searching for information across Salesforce, email threads, and documentation — before they could even begin resolving a case.

Approach

We built an AI agent layer on top of their Salesforce Service Cloud org. The agent listens to case context, retrieves relevant knowledge and history, and surfaces a pre-drafted response to the agent — inside the Salesforce interface they already use.

04

Web Development · Legacy Migration

Zero-downtime migration from legacy PHP monolith to Next.js microservices

Results

0 minutes
downtime during the entire migration
4 months → 3 weeks
new feature delivery cycle time
67%
reduction in infrastructure cost post-migration
12 weeks
total migration delivery

Situation

A growing SaaS platform was running on a 9-year-old PHP monolith. Every new feature took months to ship, the deployment process required 4-hour maintenance windows, and the codebase had accumulated 11 years of undocumented decisions.

Approach

We used a strangler fig migration pattern — incrementally extracting modules into Next.js frontends and Node.js API services while the legacy system remained live. Blue-green deployments eliminated maintenance windows entirely.

05

n8n Automation · Workflow

n8n automation eliminated 28 hours of manual data work per week

Results

28 hours
of manual data work eliminated per month
100%
reduction in copy-paste errors
< 4 minutes
end-to-end report generation time
3 days
total build and deployment time

Situation

A professional services firm was manually pulling data from three different SaaS platforms every Monday morning to compile a weekly report — a process taking 7 person-hours per week with frequent copy-paste errors.

Approach

We designed an n8n automation architecture that pulls from all three platforms via their APIs, aggregates and formats the data, runs validation rules, and delivers a formatted report to Slack and email on a schedule — with failure alerts.

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