One platform. Every model you need.

Connect your data and Consilience handles the rest — automated feature discovery, model training, and validation. Your team gets a production-ready model in days, not months.

SOC 2 Compliant · Runs in your AWS VPC · Zero data egress

How It Works

Days, not months

Traditional model development is a months-long slog. Consilience compresses it to days.

Traditional ML Build

6–12 months

Data 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 days

That tiny sliver is 3 days on a 12-month scale.

Let’s zoom in
Connect Data

Day 1

Build Optimized Model

Day 2

Model Validation with Audit

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. Our platform was built from the ground up to parse, understand, and extract predictive signals from this complexity automatically.

Raw Data
{
"tradelines": [{ "type": "revolving", "balance": 4200, "limit": 10000, "opened": "2019-03-14", "payment_history": ["C","C","1","C"...] }, ...],
"inquiries": [{ "date": "2024-11-02", "type": "hard", "creditor": "..." }, ...],
"derogatories": [{ "type": "collection", "amount": 1240, "date_filed": "2023-01-18" }],
"public_records": [ ]
}
Engineered FeaturesAuto-generated
avg_trade_age_monthsAUC +0.012

Average age of all open tradelines

pct_zero_balance_tradesAUC +0.008

Fraction of trades with zero current balance

delinq_recency_monthsAUC +0.006

Months since most recent delinquency

utilization_ratio_revolvingAUC +0.005

Total revolving balance / total revolving limit

Tradeline signal extraction
Inquiry timing & velocity patterns
Derogatory history features
Payment behavior sequences

Model Flexibility

Train on any prediction target

Binary classification, regression, or custom architectures — configure the prediction target to exactly match your business objective.

DQ30 — 30-day delinquencyDQ60 — 60-day delinquencyDQ90 — 90-day delinquencyDefault probabilityPrepayment riskFraud likelihoodLoss given defaultCustom targets

Integrations

Connects to your data, wherever it lives

Native connectors for every major data warehouse. No ETL pipelines to maintain.

Snowflake
Snowflake
BigQuery
BigQuery
Databricks
Databricks
Redshift
Redshift
Postgres
Postgres
S3
S3
SOC 2 Compliant
Runs in your AWS VPC

Versioned Feature Engineering Code

Every model version comes with the exact feature engineering code used to produce it. Reproduce any result. Meet model risk management requirements. Own your work completely.

Feature Audit Trail

Every feature has a human-readable definition, importance score, and lineage trace. Designed to support model risk reviews and regulatory examinations — without scrambling.

See it in action

Book a 30-minute demo and we’ll walk through the platform on your data.

Book a Demo