One platform. Every model you need.
The Consilience platform connects to your data warehouse, automatically discovers predictive features, trains models against any prediction target, and validates them with SR 26-2-aligned documentation. Your team gets a production-ready credit, fraud, or pricing model in days, without manual feature engineering or stitched-together notebooks.
How It Works
Days, not months
Time to a validated model
6–12 monthsvs.3 days
Traditional ML Build
6–12 monthsData Prep
Month 1
Manual Feature Brainstorming
Month 2
Feature Engineering Iterations
Month 3–4
Model Parameter Tuning
Month 5
Compliance Review & Feature Audit
Month 5–6
Model Validation
Month 6+
Consilience
~3 daysDay 1
Day 2
Day 3
Data Expertise
We know how to read complex financial data
Financial data is messy: deeply nested JSON, variable schemas across bureaus, raw transaction feeds with thousands of merchants and categories. The platform was built from the ground up to parse it, understand it, and pull predictive signal out of it automatically.
Average age of all open tradelines
Fraction of trades with zero current balance
Months since most recent delinquency
Total revolving balance / total revolving limit
Compliance First
Built for financial services from the ground up
Compliance is not a review stage bolted onto the end of a modeling project here. It runs inside the search: features are screened against Reg B before they can be selected, every retained feature carries its adverse-action reason, and the documentation a model risk team asks for is generated by the run that produced the model.
Open any card to see that step in the product.
What lands in your model-risk folder
Every training run emits its own documentation set. Nobody assembles a validation package by hand after the fact, because the run that built the model already wrote it.
Model governance report
Regulatory crosswalk, data sources, target definition, feature catalog, selection funnel, tuning record, performance, explainability, and a full feature dictionary.
Insight report
HTML
Performance metrics, decile rank-ordering table, the complete feature audit, every line of feature engineering code, and the runtime environment.
Dropped-feature record
XML
Every candidate that did not make the model, the stage that removed it, and the reason it was dropped.
Scored dataset
Parquet
Per-row split label, ground truth, and predicted probability, so validation can be reproduced independently.
Model Flexibility
Train on any prediction target
Binary classification, regression, or custom architectures. Configure the prediction target to match your business objective exactly.
Integrations
Connects to your data, wherever it lives
Native connectors for every major data warehouse. No ETL pipelines to maintain.
