Services
Data Platforms
Pipelines, lakehouses, and real-time analytics.
How we approach it
Data platforms fail socially before they fail technically: nobody trusts the numbers. We build lineage, quality gates, and ownership into the substrate so every dashboard can answer 'says who?'
Tools of the trade
- Kafka
- Databricks
- Snowflake
- dbt
- Apache Iceberg
- Algoryq Grid
Services offered
Streaming and batch unification on one governed substrate
Lakehouse design with lineage and column-level ownership
Real-time decisioning pipelines at production SLAs
Self-serve analytics with built-in quality gates
What you get
- 01
Streaming and batch unified on one governed substrate
- 02
Self-serve analytics with lineage and quality gates
- 03
Real-time decisioning at production SLAs
Mini case study — Global bank
Risk engine rebuilt
A monolith handling $2T in positions re-architected onto a streaming platform: overnight batch became a continuous close.
40×
faster portfolio revaluation, zero missed opens since launch
Industries served
How we deliver
01
Discover
Two weeks inside your domain. We leave with the problem stated in one sentence.
02
Architect
Systems designed on paper first — reviewed, costed, and stress-tested before a line is written.
03
Build
Weekly shippable increments. Production quality from the first commit.
04
Ship
Progressive rollout with observability, runbooks, and rollback rehearsed.
05
Evolve
We stay through scale — measuring, hardening, compounding.
Adjacent capabilities
