Our work

Retrieval that holds up in production.

Search engineering is the foundation of every AI system we build. These engagements show what that looks like at scale - measured in latency, relevance, cost and revenue.

Financial Intelligence
AlphaSense

90% faster financial document search

Transforming search infrastructure to handle millions of financial documents with sub-second response times.

90%
faster search queries
99.99%
uptime achieved
3x
improvement in relevancy

Challenge

  • Slow search performance on large datasets
  • Complex financial document structures
  • Need for real-time updates

Approach

  • Custom Elasticsearch cluster optimization
  • Advanced relevancy tuning
  • Real-time indexing pipeline
Read the AlphaSense case study
Recruitment Platform
GrabJobs

Scaling job search to 10 million listings

A resilient OpenSearch infrastructure that powers job matching for millions of users across Southeast Asia.

10M+
job listings indexed
5ms
average query time
40%
lower infrastructure costs

Challenge

  • Multi-language search requirements
  • High write throughput
  • Complex job matching algorithms

Approach

  • Multi-language analyzer optimization
  • Sharding strategy redesign
  • Custom scoring algorithms
Read the GrabJobs case study
E-Commerce
Lenskart

Personalized product discovery at scale

Search and personalization for India's largest eyewear retailer, serving millions of daily searches.

300%
increase in conversion
<100ms
response times
60%
improvement in relevancy

Challenge

  • Complex product attributes
  • Personalization requirements
  • Peak traffic handling

Approach

  • ML-powered relevancy tuning
  • Distributed caching layer
  • Auto-scaling architecture
Read the Lenskart case study

Your use case could be next.

Whether it's search that needs to scale or an AI assistant that needs better retrieval, a free 30-minute call is the fastest way to find out what it would take.