Generative AI creates.
Predictive AI decides.
Most companies only buy one.
For 30 years, we have built predictive intelligence systems for the world's largest institutions. Here is what we keep finding in their data.
30+
Years refining predictive architecture at institutional scale
50–100
Fortune 500 clients across financial services, insurance, and healthcare
16,000+
Variables per US property — 140 million properties in the database
7
Companies built around the same architecture by the same team
Two categories. One conversation.
Not the same problem.
Not the same architecture.
Most AI vendors sell you one category and call it everything. Here is the actual difference — and why it determines where your ROI lands.
WHAT IT PRODUCES
Generative AI — LLMs
Text, images, code, summaries; content created from patterns in language.
Predictive AI — Swarmalytics
Predictions, scores, risk rankings; forward-looking outputs tied to testable outcomes.
DATA IT RUNS ON
Generative AI — LLMs
Text corpus; language at scale.
Predictive AI — Swarmalytics
Petabyte-scale structured data; transactions, behavior, geography, 30 years of outcomes.
FAILURE MODE
Generative AI — LLMs
Hallucination; confident, plausible, wrong.
TRACK RECORD
Predictive AI — Swarmalytics
Every output is a testable prediction. You can score it. There is no hallucination equivalent.
Generative AI — LLMs
2020 to present.
VARIABLE DEPTH
Generative AI — LLMs
Emergent from language patterns; not configurable per domain.
Predictive AI — Swarmalytics
16,000+ variables per target entity. The gap between what you see and what we see is where the findings live.
WHEN TO USE IT
Generative AI — LLMs
Communication, drafting, summarization, generation.
Predictive AI — Swarmalytics
Risk pricing, lead scoring, fraud detection, site selection, demand forecasting, portfolio analysis.
Predictive AI — Swarmalytics
30 years of institutional engagements. AARP, UBS, Bank of America, Aetna, GE Health, and dozens more.
A REAL FINDING
A Top 20 insurer asked us to validate their pricing model. We found something their actuaries hadn't.
The assignment was routine: run analysis on their ExCAT Property line, confirm the model is working. What we found was not routine.
Their riskiest accounts were running a 98.1% loss ratio. To reach a sustainable 50%, those accounts needed a 96% rate increase. The model had been systematically under-pricing the worst risk in the portfolio.
The counterintuitive part: their safest accounts were over-priced. At a 33.5% loss ratio, they were profitable; but they were also giving every low-risk customer a reason to shop a competitor at renewal. And the liability line had inverted entirely: low-risk accounts were generating higher losses than high-risk ones.
The problem ran in both directions simultaneously, and it was invisible in their existing analytics stack.
Their actuaries were not incompetent. They were working with the variable resolution that their tools allowed. We brought 16,000 variables to a problem their system was solving with dozens. That resolution gap is where this finding was hiding.
This pattern (valuable signal buried below the resolution of standard analytics) is what we find when we look closely at data-rich companies that have not yet applied predictive intelligence to their own books.
Highest-risk accounts — actual loss ratio
98.1%
Sustainable target: 50%. Rate increase required to close the gap: +96%. Invisible to their existing analytics stack.
Lowest-risk accounts — loss ratio
33.5%
Profitable — but over-priced relative to any competitor with better risk resolution. Every renewal was a shopping invitation.
Liability line — risk relationship
Inverted
Low-risk accounts generating higher loss ratios than high-risk accounts. The model was working against the portfolio in both directions.
Variable resolution applied
16,000+
Per account. This finding does not surface at standard analytical resolution. It required the depth the swarm model provides.
30 years of findings
What we keep discovering
in institutional data.
Different industries. Different data environments. The same finding: the signal that changes the numbers was already there. Nobody had looked at the right variables.
AARP — Membership Analytics
Scoring 60 million seniors found $8 million a year hiding in the wrong mailing list.
AARP was spending on blanket membership acquisition mailings. We scored every senior citizen in the US for membership likelihood, then optimized contact cadence per person — how often, how long, when to rest. The outcome was a structural reallocation of spend, not a marginal improvement. The system ran for five years after delivery.
$8MILLION / yr
Annual savings — in use 5+ years after delivery
RealeFlow — Real Estate Targeting
A 100 million household cold call center, replaced entirely. The model knew who was going to sell.
RealeFlow was cold-calling 100 million US households per year at a 99% rejection rate — treating volume as the only lever. We ran a head-to-head test. AI targeting found the sellers without the calls. The call center closed. The same system now runs 700,000 skip-tracing queries per day for the platform's current operation.
700,000+ /day
Skip-tracing queries — successor to 100M annual cold calls
Bank of America — Fraud Detection
People steal in round numbers. The model found it in the transaction data. Nobody had looked for it.
BofA was writing off $1 billion annually in credit card bad debt. Fraud detection at that scale is not about unusual amounts — it is about pattern structure. Legitimate charges look like $16,462.15. Fraud is $5,000 flat. Trailing zeros are a predictive signal. The model found the pattern. The write-offs dropped.
$1BILLION / yr
Annual credit card write-off base the model worked against
Regional Hospital Network — Patient Analytics
150 patients with undiagnosed congestive heart failure. Found before they knew they had a heart problem.
When we unified the hospital's siloed systems and ran analysis, we found patients receiving insulin who were not diagnosed as diabetic — a financial, clinical, and compliance problem. And 150 cases of undiagnosed congestive heart failure. The signal was already in the data. It needed someone to look at the right variables out of the thousands available.
150 cases
Undiagnosed CHF cases identified before clinical presentation
The question worth asking
What is hiding in your data?
Every client above had the answer already in their data. They just hadn’t looked at the right variables yet.
No pitch. No deck. Just the conversation.
20 minutes to find out what your data is hiding.
Doug, the founder, takes a limited number of discovery conversations each month. If your company has significant data and decisions that depend on what that data actually says; that is the conversation.