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SPEC№ 0001
PG. SUB · data-intelligence
/ CAPABILITY · DATA-INTELLIGENCE

Data & Intelligence

Enterprise data, shipped as intelligence.

Manifesto

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, the deployment surface that lets it ship without taking the rest of the platform down. The model is the easy part; the system around it is what makes it production.

We treat eval as the live system, not a chart from before launch. We treat drift as inevitable, not surprising. We treat the production loop as the actual product, and the offline metric as a single signal in a much larger conversation.

The pillars
/ 01

Data pipelines

Streaming + batch, schema-enforced at the seams, with backpressure and replay. Idempotent transforms with end-to-end lineage so the question 'where did this number come from?' has a one-click answer.

  • Kafka
  • dbt
  • Spark
  • Flink
  • Lineage
/ 02

ML Ops, end to end

From notebook to production. Training pipelines as first-class systems, model registries as source of truth, canary deploys and shadow traffic, instant rollback when the eval line crosses.

  • MLflow
  • DVC
  • Ray
  • Triton
/ 03

Computer vision

Defect detection on real factory floors, OCR on real claims documents, identity verification on real ID cards. Not COCO benchmarks, the messy data that breaks them.

  • PyTorch
  • ONNX
  • TensorRT
  • OpenCV
/ 04

Agentic systems

RAG that actually works at scale. Tool use you can audit. Token budgets enforced. Eval harnesses that score reasoning chains, not just final answers. Productionised, not demoed.

  • LangGraph
  • DSPy
  • vLLM
  • Bedrock
/ 05

Evaluation as first-class

Offline metrics segmented by cohort and edge case. Online metrics from shadow traffic and holdouts. Calibration checks. Drift detectors that fire before the business notices. Eval is the product.

  • Eval harness
  • Drift
  • Calibration
  • Cohort

What we ship

  • Data engineering · pipeline design · stream processing
  • ML Ops · model training & deployment
  • Computer vision (defect detection, OCR, identity)
  • Agent development for retrieval and reasoning

Stack

Snowflake · Databricks
PyTorch · ONNX · MLflow
The pipeline · interactive

Five stages. Pick one to see what we ship there.

/ Stage 01 · Ingest

The data front door

Every system upstream has a different definition of 'event.' We make sure they all land in ours, schema-enforced at the seam, with backpressure and replay for the days the upstream is having a worse day than we are.

  • Kafka, Kinesis, Pub/Sub, RabbitMQ for streams
  • S3 / GCS batch with manifest + checksum
  • Schema enforcement at the seam (Avro, Protobuf, JSON Schema)
  • Dead-letter queues with replay tooling, not just logs
  • Lineage capture from the first byte
Throughput · last 30 hours
47 / hr · p99 200ms · OK

The pipeline, in the only chart it'll ever need.

Posture

99.7%
Inference uptime · 2025
200ms
p99 latency · production
47
Models in production
12
Active data pipelines
Monitoring active
Frameworks & methodology
  • MLflow Registry
  • DVC Lineage
  • Triton Serving
  • OpenTelemetry
  • Drift Detection

Tools & runtimes

  • PyTorch
  • TensorFlow
  • ONNX
  • Hugging Face
  • vLLM
  • Bedrock
  • Vertex AI
  • Databricks

Coverage

  • Computer vision · industrial + claims
  • Retrieval & RAG · enterprise knowledge
  • Forecasting · supply chain + retail
  • Decision agents · workflow automation
Engagement · redacted sample

Engagement with Insurance, computer-vision claims pipeline. Document classification accuracy from 76% baseline to 98.4% with calibrated abstain; manual review queue cut by 71%.

Hover or focus the bar to reveal · client identity protected

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