Service pillar
Data & Intelligence
When your data is begging to be useful.
Predictive ML, computer vision, and generative content pipelines. Turn the data you already have into decisions, alerts, and downstream products.
What you get
- Predictive models (churn, demand, downtime, quality) using the right tool — classical ML or LLMs, not whatever's trendy
- Computer vision pipelines for quality control, document understanding, and physical-world automation
- Generative content pipelines — image, video, voice — wired into your publishing workflow
- Data engineering glue: dbt, Airflow, Dagster — whichever fits your stack
- Eval, monitoring, retraining cadence — models that stay accurate after week one
When this is the right fit
- You have data sitting in warehouses no one queries
- Your QC team is the throughput bottleneck
- Your content team is the throughput bottleneck
Sample builds
Predictive maintenance for machine fleets
LightGBM + classical ML on SCADA/IoT telemetry, retrained weekly, surfacing alerts to ops before downtime happens.
Vision QC for production lines
YOLO-based defect detection with active-learning loops. Routes uncertain cases to inspectors instead of guessing.
End-to-end content pipeline
Idea generation → script → voiceover → thumbnails → publishing → repurposing. With humans approving at the 3 spots that matter.
Tech we reach for
- scikit-learn
- XGBoost / LightGBM
- YOLO
- OpenCV
- ElevenLabs / Runway
- dbt / Airflow / Dagster
- Modal / Replicate
FAQ
Do we need a data warehouse before you start?
No, but you'll save weeks if you have one. We work with what you have — flat files, Postgres tables, S3 dumps — and tell you honestly when a data engineering pass needs to happen first.
Will the model still be accurate next year?
Only if you keep training it. Every ML build we ship includes a retraining cadence and a drift-detection dashboard. If you don't operate it, you didn't really ship it.
Talk to a human about this.
20 min. No deck. We'll tell you what's possible — and what isn't.
