Transformation Stories
Explore how we use cutting-edge technology to solve complex infrastructure, data, and customer service challenges.
Name Matching with AI
~90%
Matching Accuracy
The Challenge
Name reconciliation between databases was done manually by a dedicated team. Each process could take hours of record-by-record review — subject to human error and impossible to scale with volume.
- - 100% manual process
- - Hours spent per file
- - Team stuck in repetitive tasks
✓ The Solution
We built an orchestration system with generative AI and embeddings that automatically compares and reconciles names, understanding variations, abbreviations and data noise — without relying on rigid rules.
~90%
Accuracy
Seconds
Used to take hours
100%
Automated process
Data Ingested, Available in Seconds
~30s
From raw data to query
The Challenge
Ingested data needed to reach the application with maximum efficiency — fast for the user and cheap to operate. The classic trade-off: traditional pipelines either delayed data availability or required always-on infrastructure, driving up costs.
- - Data took too long to become available
- - Always-on infrastructure costs
- - Heavy searches over large volumes
✓ The Solution
We designed a fully serverless pipeline on AWS: ingestion feeds a data lake organized in layers (raw → processed → enriched), with event-driven orchestration. From there, data is distributed to query layers — ClickHouse for analytics and Typesense for search — responding in milliseconds even over large volumes, paying only for what's processed.
~30s
Data available end-to-end
Milliseconds
Search response time
On-demand
Cost scales with usage
AI Customer Service Agent
~$0.01
Cost per interaction
The Challenge
Customer support — via WhatsApp and within the platform — was flooded with repetitive questions, overloading the human team. The company needed an AI capable of resolving those queries using internal operational knowledge, securely.
- - Support team overwhelmed
- - Same questions arriving through multiple channels
- - Knowledge scattered across internal bases
✓ The Solution
We built an AI operating across multiple customer service channels — WhatsApp and within the platform — with secure access to internal knowledge bases. It combines RAG, vector search, function calling and Text2SQL to query internal data in natural language. Serverless architecture on AWS delivers all of this at minimal operating cost.
~-20%
Human support tickets
~$0.01
Cost per interaction
Multichannel
WhatsApp and in-platform
FinOps: AWS Cost Optimization
~20%
AWS cost savings
The Challenge
The AWS bill had already crossed five digits per month and was growing without clear visibility into where the money was going. There was no structured process to identify waste — and, the hardest part — to keep the savings from slipping back over time.
- - Five-digit AWS bill per month
- - No visibility into where the waste was
- - One-time savings with no continuity
✓ The Solution
We ran a FinOps process on three fronts: discovery (mapping where the cost actually was), optimization (eliminating waste and right-sizing resources) and team alignment, building practices so the savings would last beyond the engagement. All within 50 days.
~20%
Savings recovered
50 days
From discovery to results
Ongoing
Monthly savings sustained
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