Tools We Implement
The modern data stack. Done right.
We're vendor-agnostic but opinionated. We've implemented dozens of tools across hundreds of use cases. We know what works, what doesn't, and when to use what.
Data Warehousing
Where your data lives.
How we help: Architecture design, migration, optimization, cost management
Related service:
Data Ingestion
Data Ingestion
How we help: Connector setup, custom integrations, monitoring, cost optimization
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Data Orchestration
Running and monitoring workflows.
How we help: Deployment, DAG development, monitoring, dbt integration
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Data Visualization / BI
Making data accessible and actionable.
How we help: Dashboard development, semantic modeling, embedding, governance
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Data Transformation
Turning raw data into business-ready models.
How we help: Architecture design, migration, optimization, cost management
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AI/ML Infrastructure
Building AI systems that work.
Pinecone / Weaviate / pgvector
Vector storage and semantic search
LangChain / LlamaIndex
LLM application frameworks
MLflow
Experiment tracking and model registry
How we help: RAG architecture, vector search implementation, LLM integration
Related service:
Reverse ETL
Activating warehouse data in operational tools.
Hightouch
Marketing activation, audience sync, CRM enrichment
Census
Product-led growth, operational analytics
How we help: Sync configuration, audience building, integration with marketing tools
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How We Choose Tools
01
Your existing ecosystem
What do you already use?
02
Team capabilities
What can your team maintain?
03
Scale requirements
How much data, how many users?
04
Budget
What's the total cost of ownership?
05
Use case
Analytics only? ML? Real-time?
If you need...
Zero-maintenance ingestion
Fivetran
Custom connectors or self-hosted
Airbyte
Real-time + APAC sources
Hevo
Google ecosystem + serverless
BigQuery
Multi-cloud + data sharing
Snowflake
Heavy ML + unstructured data
Databricks
Fast time-to-value BI
Metabase
Enterprise BI + governance
Looker
Spreadsheet-native users
Sigma
