Record volumes, request loads, and the constraints they were delivered under. Every entry links to a written account of how it was done.
Our clients are not named. The engineering is described in full.
Partitioned and migrated a Heroku Postgres estate serving real-time visibility across every US ZIP code, with no outage and no maintenance window.
Dyno strategy, Redis architecture and failure-mode design for a national real-time data and API platform.
A Feast-centric feature store that stabilised the full ML lifecycle by making features consistent and versioned.
MLflow, Great Expectations and Evidently wired into CI/CD to detect and block drift rather than discover it in a quarterly review.
BigQuery, Firestore and Feast aligned behind a contract-first design resilient to upstream changes outside our control.
Feature store, CI/CD, Kubernetes, IAM controls and observability — built to run, not to demonstrate.
Connecting raw data, mathematical scoring and model learning so decisions can be defended, not just produced.
Double Diamond process applied to a consumer review journey, balancing credibility against conversion.
Auralis: local model inference, hybrid retrieval and message-level provenance running entirely on infrastructure the customer owns.
We work under CMMI Level 3 and ISO/IEC 27001:2022. Client names and engagement specifics are disclosed only with written permission, which is why the work below is described by what it did rather than who it was for. If you would like to talk to a reference, ask us and we will arrange it directly.