Quick answer
LLM agents, RAG, ETL pipelines, warehouses, dashboards, and model evaluation harnesses — plus the ad-data pipelines we are known for.
The problem
Most AI features stop at chatbots that cannot act. Most data stacks stop at spreadsheets that do not scale. We build both layers to production standards.
Our approach
Ground agents in your real data via RAG
Model warehouses in dbt so metrics mean the same thing everywhere
Add evaluation and guardrails before production autonomy
Monitor pipelines before clients notice breaks
What this includes
- Agentic workflows, RAG, and evaluation harnesses
- ETL/ELT pipelines from every ad platform and SaaS source
- dbt models on Snowflake or BigQuery
- Real-time dashboards and reporting automation
Typical stack
dbtSnowflakeBigQueryLangChainOpenAI / AnthropicAirflow
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