decisions at scale

decision diagnostics

to improve profits


Businesses are automating more decisions at scale.

algorithmic trading

fraud flagging

transaction risk assessment

lead prioritization



search ranking

matching & dispatch

product recommendations


Unfortunately, these systems are fragile and opaque—a high-stakes liability.

As systems decay,
errors compound silently,
losing billions.



old definitions

model drift

swapped data sources

fragile pipelines

shifting requirements

These systems comprise a complex ensemble of models, heuristics, algorithms, and data sources developed and managed by disparate teams of various disciplines.

Worse, nobody feels accountable.

We lack the global visibility needed to prioritize issues for business impact.


AI that observes your automated decisions in production and alerts precisely where you’re leaving value on the table.

project - selected issue node value.png
  • after a lightweight agent is installed atop your production decision systems, dcyd learns the dependency graph of data (sources, models, and transformations) supporting those decisions.

  • when combined with the decisions’ outcomes, an intelligent analysis alerts how much value can be recovered by fixing the components having the worst impact on your objectives.

Existing solutions aren't solutions

datastore monitoring:

looking in the wrong haystack.

The reams of data in your datastores a mere shadows of the vital decisions your automated systems have made. hoping to find actionable insights simply by slogging through a morass of logs is hopeless.

You need to observe the decisions themselves and see their impact on your bottom line.

model monitoring:

missing the big picture.

Your business decisions don’t run on isolated models. They run on chains of models, sprinkled with heuristics, complicated by feedback loops. Model monitoring doesn’t give you what you need because it lacks visibility across systems and doesn’t know your business objectives.


You need a solution that provides cross-system visibility and understands your business objectives.

anomaly detection:

too noisy and too needy.

Anomaly detection routines detect what’s different, not what’s bad. steady underperformance goes unnoticed, while alerts are riddled with noisy distractions. your teams  waste their time responding to false positives and adjusting alert thresholds manually.


You need a solution that bothers you when—and only when—decisions are harming your business.

                  if you care about profits, revenue, and growth​.

  • observes automated decisions in production

  • learns behaviors and data dependencies

  • analyzes the impact of each component on business objectives

  • alerts only business-critical issues, because it understands what's important

This is the future of business decision-making.
better decisions at scale

schedule a demo

good dcysion!