01 / 06
Data pipelines
Streaming and batch, schema-enforced at the seams, with backpressure, replay and end-to-end lineage — so “where did this number come from?” has a one-click answer.
Capabilities · Data Engineering · ML Ops · LLMs · Agents · Computer Vision
Data & Intelligence
Enterprise data and AI, shipped as intelligence — engineered to run in production.
Manifesto / Data & Intelligence
Engineering practice · est. 2018
Most ML lives and dies in a notebook. We aren’t in that business.
We engineer the systems around the model: the pipelines that feed it, the evaluation harnesses that catch it failing, and the deployment surface that lets it ship without taking the rest of the platform down. The model is the easy part.
We treat evaluation as the live system, not a chart from before launch. We treat drift as inevitable, not surprising. And we treat the production loop as the actual product — the offline metric is one signal in a much larger conversation.
Discuss this opportunityThe pillars
What we engineer — and how we hold the line.
Each pillar is staffed by the same senior engineers, from first architecture review to production.
02 / 06
MLOps + LLMOps
Training pipelines as first-class systems, model and prompt registries as the source of truth, canary deploys and shadow traffic, instant rollback when the eval line crosses.
03 / 06
Computer vision + VLM
Defect detection on real factory floors, OCR on real claims documents, identity verification on real ID cards — plus vision-language models for multimodal work.
04 / 06
Agentic systems + chatbots
RAG that works at scale, tool use you can audit and token budgets that are enforced — with eval harnesses that score the reasoning chain, not just the final answer.
05 / 06
Evaluation as first-class
Offline metrics by cohort and edge case, online metrics from shadow traffic, calibration checks and drift detectors that fire before the business notices.
06 / 06
AI governance
Twelve AI controls — from model cards to red-team review — mapped to NIST AI RMF, the EU AI Act, ISO/IEC 42001 and the OWASP LLM Top 10.
The pipeline
Five stages, from first byte to production.
We integrate foundation models into production systems — and engineer every stage around them so the model is never the weakest link.
- 01
Ingest
Kafka, Kinesis and Pub/Sub streams, batch with manifests and checksums, schema enforcement and lineage from the first byte.
- 02
Transform
Idempotent, schema-checked transforms built for replay, with dead-letter queues and tooling — not just logs.
- 03
Train
Reproducible training pipelines with experiment tracking and model registries as the source of truth.
- 04
Evaluate
Held-out evals on the customer’s own data, faithfulness checks for RAG and tool-use correctness for agents.
- 05
Serve
Shadow, canary and A/B rollouts — automatic promotion when the gate passes, automatic rollback when a guardrail fires.
Posture
Receipts,
not slides.
Figures from the practice dossier. Client identities are held back by default and available under NDA.
- 99.7%
- Inference uptime · 2025
- 200ms
- p99 latency · production
- 47
- Models in production
- 12
- Active data pipelines
“Computer-vision claims pipeline for an insurer. Document classification accuracy from a 76% baseline to 98.4% with calibrated abstain; manual review queue cut by 71%.”
Engagement · redacted sample · client identity available under NDA
Frameworks, standards & certifications
Discover more
Four practices, one studio. Keep exploring.
The same engineers sit behind every capability — no siloed teams handing work over walls.
01
Engineering
End-to-end software for systems that must not fail.
02
Domain Solutions
Engineered for the institutions that depend on it.
03
Security
Engineered defense for the systems we build.
