Process cost
Cut waste in staffing, routing and inference spend.
32%Capabilities
Six ways we cut operating cost.
Hover a path to lift it. Click for how the work lands.
Fraud and leakage
Score risky events in line with the transaction, with a cost on every miss.
0.79Visual inspection
Catch defects on the line and close the review loop.
96.8%Internal copilots
Answers over your documents, with citations a person can open.
0.94Decision scoring
A score with a kill-switch, an audit trail and a named owner.
74.5%Demand and inventory
Cut overstock, stockouts and overtime with a forecast planners actually use.
2.7%Engineering canvas
What actually happens inside a project.
Six stages, in order, every time. Scroll through them to see the tools attached to each and what we hand over when it closes.
01 · Data
Do we have the signal at all?
Source audit, lineage, freshness and volume. We answer whether the problem is even learnable from what exists before writing a model.
- Python
- ClickHouse
- Kafka
- Airflow
01
Data
Source audit, lineage, freshness and volume. We answer whether the problem is even learnable from what exists before writing a model.
- rows
- 1,842,031
- sources
- 6
- null rate
- 0.8%
audit output: feasibility memo + gap list
02
Features
Feature engineering with offline and online parity. Every feature has an owner, a definition and a freshness guarantee.
- features
- 247
- parity drift
- 0.00%
- freshness
- 5 min
handover: feature registry + contracts
03
Model
Baseline first, then iterate. Rolling-origin validation, tracked experiments, and a metric agreed with you before training starts.
- experiments
- 184
- best auc
- 0.913
- train time
- 03:42:18
handover: model card + experiment log
04
Inference
Serving inside your latency and cost budget. Versioned artifacts, blue/green rollout, and rollback that has actually been tested.
- p50
- 5ms
- p95
- 47ms
- rps
- 3,400
handover: deployed endpoint + runbook
05
Monitoring
Drift, data quality and business metrics on the same dashboard. Alerts that map to an action, not just a threshold.
- drift psi
- 0.041
- uptime
- 99.95%
- alerts / wk
- 1.2
handover: dashboards + alert routing
06
Business impact
Measured against the pre-registered metric, with the experiment design fixed before launch so the result means something.
- kpi lift
- +18%
- cost delta
- -32%
- payback
- 4.1 mo
handover: impact readout + next bets
01 · Data
Do we have the signal at all?
Source audit, lineage, freshness and volume. We answer whether the problem is even learnable from what exists before writing a model.
- Python
- ClickHouse
- Kafka
- Airflow
- rows
- 1,842,031
- sources
- 6
- null rate
- 0.8%
02 · Features
What does the model get to see?
Feature engineering with offline and online parity. Every feature has an owner, a definition and a freshness guarantee.
- dbt
- Spark
- Feature store
- Great Expectations
- features
- 247
- parity drift
- 0.00%
- freshness
- 5 min
03 · Model
Does it beat the honest baseline?
Baseline first, then iterate. Rolling-origin validation, tracked experiments, and a metric agreed with you before training starts.
- PyTorch
- LightGBM
- Optuna
- MLflow
- experiments
- 184
- best auc
- 0.913
- train time
- 03:42:18
04 · Inference
Can it answer fast enough, every time?
Serving inside your latency and cost budget. Versioned artifacts, blue/green rollout, and rollback that has actually been tested.
- FastAPI
- Docker
- Redis
- Kubernetes
- p50
- 5ms
- p95
- 47ms
- rps
- 3,400
05 · Monitoring
How will we know when it breaks?
Drift, data quality and business metrics on the same dashboard. Alerts that map to an action, not just a threshold.
- Prometheus
- Grafana
- PSI / KS tests
- Sentry
- drift psi
- 0.041
- uptime
- 99.95%
- alerts / wk
- 1.2
06 · Business impact
Did the number we agreed on move?
Measured against the pre-registered metric, with the experiment design fixed before launch so the result means something.
- A/B framework
- CUPED
- Metabase
- Cost model
- kpi lift
- +18%
- cost delta
- -32%
- payback
- 4.1 mo