Technologies
Data, analytics and search
Good reports and good AI both depend on clean, well modelled data. We store operational data in PostgreSQL or MySQL, move it into a warehouse with scheduled pipelines, and put dashboards on top in Power BI or Metabase. For search and AI retrieval we add Elasticsearch, Qdrant or pgvector.
4 tools
Operational databases
PostgreSQL
Our default database for business applications.
MySQL
Existing apps, WooCommerce stores and PHP systems.
MongoDB
Flexible records such as forms, catalogues and event logs.
Redis
Caching, sessions, rate limits and short lived queues.
6 tools
Warehouses and pipelines
ClickHouse
Fast analytics over large event and transaction tables.
Snowflake
Managed warehouse for teams that want little to operate.
Databricks
Large data and machine learning work on a lakehouse.
Apache Kafka
Streaming events between systems in near real time.
Apache Airflow
Scheduling and monitoring data pipelines.
dbt
Tested, documented SQL models that turn raw tables into reporting tables.
5 tools
BI and search
Power BI
Dashboards for teams already on Microsoft 365.
Metabase
Simple, low cost dashboards and self service questions.
Elasticsearch
Full text search across products, documents and logs.
Qdrant
Vector search for large AI retrieval collections.
pgvector
Vector search inside PostgreSQL when a separate store is not needed.
What we build with it
Typical work in this stack
- Management dashboards
- Sales, collections, stock and production views that update daily or hourly from your source systems.
- Data warehouses
- One place where ERP, CRM, e-commerce and Tally data meet, with agreed definitions for each number.
- Pipelines
- Scheduled jobs that pull, clean and load data, with alerts when a source changes or a load fails.
- Search
- Product, document and record search that handles spelling mistakes, filters and Indian names.
- Data for AI
- Clean, chunked and permissioned document stores so assistants answer from the right sources.
How we choose
Most companies need less data infrastructure than they are sold. If your reporting runs off a few million rows, PostgreSQL with dbt and Metabase or Power BI will serve you for years at low cost. We move to ClickHouse, Snowflake or Databricks when volumes, query times or the number of sources make that worthwhile, and we show the cost before you commit. For AI retrieval we start with pgvector in the database you already run and only add Qdrant when the collection grows large. If the source data is not ready, we say so and fix that before building dashboards on it.
Questions
Good to know
Other parts of the stack
Can you pull data from Tally?
Yes. We extract ledgers, vouchers and stock data from Tally and combine them with your other systems for reporting.
Power BI or Metabase?
Power BI suits teams on Microsoft 365 who want rich reports. Metabase is cheaper and simpler for quick internal dashboards.
Do we need a data warehouse?
Not always. If one database holds most of your data, a reporting schema inside it may be enough to start.
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