Analytics and AI that create real, measured efficiency, not hype.

Your Home Senior Living runs two assisted-living communities in Colorado, and we built the operating system behind it end to end. A single relational structure is the source of truth for the whole pipeline: linked tables for every prospective resident and every staff candidate, a live dashboard, a moved-in residents roster, and an activity log that stamps who changed what, and when. One data model, not a stack of SaaS tools to keep alive.

Leads arrive three ways. A web form on the site, a phone-friendly form the field team fills out, and direct entry, each normalized into a structured row on arrival. A poller checks the intake forms every fifteen minutes and writes new leads straight in. When a caregiver types a loose note from the field, say “toured Tuesday, wants the shared room, call back Friday,” the agent reads it and turns it into the real update: stage, owner, next action, date. No fields to tab through. Every edit writes its own log entry, a stage change emails the team, a move-in promotes the lead into the resident roster, and a 7am digest and Monday rollup surface what’s due, overdue, or going stale, so nothing sits waiting on someone to remember.

Holding it together is a custom memory system. Not a chat log. A structured, persistent context layer of dozens of dated, sourced records covering licensure, staffing, billing, and the pipeline, indexed for retrieval and kept current by a weekly maintenance pass. Scheduled agents sweep email, text threads, and recorded calls through the day and fold new signal back in on their own, so the operational state of two communities stays loaded and current, and nothing gets re-explained from one working session to the next. A second layer runs the regulated side: CDPHE licensing documents under version control, Medicaid enrollment and claims tracked through the state portals, and a daily P&L that refreshes itself each session. This isn’t a deck. It’s the system that runs the company, in production, every day.

A national infusion-services organization’s field sales team relied heavily on personal networks for referrals. The approach drove early growth, then plateaued. We procured national claims data, integrated it into a relational database, and partnered with operations to read each provider’s referral patterns from the billing codes. Internal and external referral behavior, billing mix, and clinic proximity all became inputs to a single composite score: every signal standardized into a z-score across the full provider universe, then weighted to the business objectives, so diverse datasets collapsed into one comparable number per provider.

The payoff was an ordered list. Reps no longer had to internalize claims tables, referral patterns, and geography on their own; they worked straight down a ranked set of accounts, highest-impact first. When the model surfaced counterintuitive targets, the sales team pushed back. Rather than override them, we iteratively re-weighted the score and added rule-based exclusions for specific practice groups and specialty types. That collaboration built buy-in and drove rapid adoption.