Technology ecosystem
The data and AI stack we build on
Our preferred data engineering stack: Python, dbt, Apache Airflow, BigQuery, Snowflake, Postgres, Metabase, Looker. Our AI stack: OpenAI, Anthropic Claude, LangChain, LlamaIndex. Our cloud infra: AWS, GCP, Vercel. We use these because they are the modern standard, not because of partnership incentives.
How this works
This page follows the same DostFlow methodology used across DataDost work: discovery, solution design, build, UAT, deploy, and hypercare.
Every client-facing route documents scope, risk, expected artifacts, and the next action so buyers can evaluate the business outcome without decoding agency jargon.
Governance by default
We maintain access controls, change logs, data handling notes, and handover documents even for lean teams. This is how enterprise operating discipline becomes practical for growing companies without a full internal data department.
Next step
Map the first reliable data path.
Bring the source exports, dashboards, and decisions that currently create friction. We return a scoped pilot path with owners, evidence, and handover expectations.