By the Time You See the Problem, It's Already Cost You
Claim denials, schedule delays, member complaints, menu profitability — the patterns are in your data. We build AI that spots them before they become problems.
You find out about problems after they've cost you money. The denied claim, the delayed project, the unhappy member who already canceled. The data was there. Nobody had time to look.
The patterns are buried in spreadsheets nobody reads. Denial codes, delay reasons, complaint categories. The trends exist, but they're spread across systems and time periods no human can track manually.
Decisions are reactive instead of proactive. Your team fights fires instead of preventing them. Every meeting starts with what went wrong last week, not what's at risk next week.
The cost of each surprise is significant. A denied claim costs $25-$200 to rework. A schedule delay costs $50K-$500K. A lost member costs years of dues. Prevention is cheaper than recovery.
Most businesses don't have a data problem. They have a pattern-recognition problem.
How This Looks In Your Industry
Same capability. Different risks, different data, different stakes.
Projects go over schedule because problems compound silently. A material delay in week 3 becomes a missed deadline in week 12. By the time the PM notices, the options are expensive.
We build AI that monitors project data in real time — material delivery timelines, subcontractor schedules, permit statuses, weather patterns, and change order velocity. It identifies projects trending toward delay 2-3 weeks before they miss a milestone. Project managers get specific alerts: which task is at risk, what's causing it, and what the downstream impact looks like. They intervene early instead of scrambling late.
Claims get denied. Your team reworks them. The same denial codes show up month after month, but nobody has time to analyze the pattern. Revenue leaks at a steady, predictable rate.
We build AI that pre-screens every claim against historical denial patterns before submission. It auto-corrects common coding errors, flags edge cases for human review, and generates a denial analysis dashboard showing exactly which payers, codes, and patterns cause the most leakage. Your team fixes root causes instead of reworking individual claims. We took one lab from a 12% denial rate to 3%.
New cases arrive and nobody knows the litigation risk until weeks of research. Bad cases get taken on. Good cases get passed up. Risk assessment depends on which attorney reviews it and how much time they have.
We build AI that screens incoming cases against historical outcomes, jurisdiction data, opposing counsel track records, and case-type benchmarks. It generates a risk profile for each potential case: estimated timeline, probability range, comparable outcomes, and red flags. Attorneys still make the call, but they make it with data instead of gut feeling. Case acceptance improves. Surprise outcomes decrease.
Structured risk profiles for every incoming matter
Members leave and nobody saw it coming. Usage drops over 6 months, dining visits decrease, event attendance falls off. By the time someone notices, the resignation letter is already written.
We build AI that analyzes member engagement across all touchpoints — dining, golf, fitness, events, spa, guest activity. It identifies members showing early signs of disengagement: declining visit frequency, reduced spending, dropped activities. The system triggers proactive outreach from membership staff before the member reaches the resignation decision. Retention is cheaper than recruitment.
Early disengagement detection across all member activity
Your menu has items that lose money every time someone orders them. Food costs fluctuate and nobody recalculates which dishes are profitable this month versus last month. The chef likes the dish. It stays.
We build AI that combines POS sales data, current ingredient costs, prep labor estimates, and plate-level profitability calculations. It identifies which items make money, which lose money, and which are popular but margin-negative. The system flags menu items where ingredient cost increases have eroded profitability. It recommends price adjustments, portion modifications, and menu positioning changes based on actual data.
2-4 point food cost improvement through menu optimization
Four phases. Same process every time. No surprises.
STEP 1
AI Assessment
We diagnose your top 3 automation opportunities on a 30-minute call. You tell us where your team spends time. We tell you where AI can help. Free, no strings.
Free, 30 min call.
STEP 2
AI Roadmap
We go deep into your workflows, interview your team, analyze your data systems, and deliver a custom report with ROI projections. This is the document your CFO needs to approve the build. If we can't identify at least $100K in annual savings, the roadmap is free.
$7,500 flat.
STEP 3
Build
Structure historical data, train prediction models, build alerting workflows, and deploy dashboards with actionable risk indicators.
$25K–$75K
STEP 4
Run
Ongoing model tuning, accuracy monitoring, new risk pattern identification, and threshold optimization as your data grows.
$2K–$5K/mo, 30-day cancel
Every implementation is custom-built for your business. Your risk factors, your historical data, your thresholds, your alert workflows. We don't sell a prediction tool — we build your early warning system.
Results From Similar Implementations
75%
Fewer surprises
2 wks
Early warning window
Cross-system
Pattern detection
Proactive
Not reactive
“The AI catches patterns our team never could. Our denial rate dropped from 12% to 3% in the first quarter.”
— Revenue Cycle Director, National Diagnostics Lab
It depends on the use case, but most implementations need 6-12 months of historical data to build reliable prediction models. Claims denial prediction works well with 12 months. Schedule delay prediction needs at least 6 months of project data. The good news: the data doesn't need to be clean. We spend the first phase of every project structuring and normalizing whatever you have. The model improves continuously as it processes new data.
A BI dashboard tells you what happened. It shows you last month's denial rate, last quarter's schedule delays, last year's complaint trends. All past tense. Risk analysis tells you what's about to happen. It flags the claims that will get denied before you submit them. It identifies the projects trending toward delay before they miss a deadline. The dashboard shows history. The AI shows the future.
Accuracy varies by use case and data quality. Claims denial prediction typically reaches 85-92% accuracy within 90 days. Schedule delay prediction runs 75-85% in the first quarter. Menu profitability analysis is near-instant because it's math, not prediction. We set confidence thresholds for every model — the system only flags items above a meaningful probability. False positive rates stay below 10% once the model stabilizes.
The AI identifies the risk. Prevention depends on the workflow we build around it. For claims denials, the system pre-screens every claim and auto-corrects common errors before submission — that's prevention. For schedule delays, the system alerts project managers 2-3 weeks before a deadline is at risk — giving them time to adjust resources. Prediction without action is just a better dashboard. We build both.
A claims denial workflow that reduces denials from 12% to 3% recovered $1.2M in annual revenue for a diagnostics lab. The total project cost was under $100K. A construction schedule delay prediction system that prevents even one major delay saves $50K-$500K per incident. Menu engineering that identifies unprofitable items and optimizes pricing can shift food cost by 2-4 points. The assessment is free. The ROI model is specific to your numbers.