A clear path to the best business decision – in real time, every time, where your data lives.
FatBrain’s Outcomes™ Engine automates business decision-making to grow sales, reduce risk, and enhance customer experience for lifetime loyalty.
What We Do
We don’t let bias enter your decision-making equation.
Our insights are not hidden inside a black box. Our engine recommends actions but also tells you why at every turn, ensuring decisions made are:
Fair
Principled, transparent analysis ensures you can make unbiased decisions.
Auditable
Auto-generates a blockchain audit trail of how every decision was derived.
Trustable
Verifiable business decisions tied to data and model(s).
Explainable
Quantifies the efficacy of possible decisions to assist in identifying the best choice.
How We Do It
All data left in place. None left behind.
Transaction gravity won’t bog us down. We bring decision-making to where your business data lives with a feather-light footprint and API interface.
Our learned vector embeddings (LVE™) technology uses 100% of your business data and signals — whether numerical, categorical, text, image, sound, or video — to increase data surface area and deliver amplified analysis.
How it Works
Input
Connects multiple data sources (structured, unstructured) in any location (on premise, cloud) from any platform (mainframe, midrange, x86).
Assess & Learn
Creates a vector-space view of the data bound to a common backplane to learn all possible business outcomes through “what-if” analysis.
Assist & Decide
Quantifies the impact of each possible business decision, directing the business analyst or data scientist to a recommended action.
Self Optimize
Learns from past decisions and payoffs to continuously refine future decision-making.
Comply & Explain
Auto-generates a blockchain audit trail of the each decision, along with data features most impacting the decision and model lineage.
What’s Enabled
With high fidelity data,
the answers become clear.
Before FatBrain After FatBrain
Option 1
Option 2
Option 3
Before FatBrain, decision fields with missing or non-numerical data were simply discarded, leading to biased or error-prone estimates.