Three engagements across healthcare, field services, and professional services. Different industries, same discipline: rigorous prioritization, the right AI for the right problem, and outcomes you can take to the bank.
Network of 12 clinics across Greater Cleveland and Columbus. 320 clinical and administrative staff, grown through acquisition, with fragmented billing workflows and no standardized front-desk processes across sites.
Every acquisition had brought a different billing system, a different prior authorization process, and a different way of handling patient intake. The network had grown revenue but not operational consistency. Claim denials ran at 11.4%, more than double the industry benchmark. AR days sat at 26. Front-desk staff across all 12 sites were spending upwards of 40% of their time on manual eligibility checks, fax intake, and prior auth paperwork. A chronic 18% no-show rate was quietly costing the network seven-figure capacity every year, and no one had a clear view into where the leakage was happening.
Automated eligibility and benefits verification now runs 48 hours before every scheduled appointment across all 12 sites, surfacing coverage gaps before the patient arrives. Standardized prior auth documentation packages for the top 22 procedure codes cut manual assembly from an average of 34 minutes per case to a single click. A claims scrubbing rule library, built collaboratively with the billing team, catches coding mismatches, missing modifiers, and payer-specific gaps before submission.
A no-show propensity model trained on 18 months of appointment data, incorporating 28 features including payer type, booking lead time, reminder response behavior, day-of-week, and appointment category, now drives intelligent slot overbooking at 115% capacity for high-risk appointment types. A parallel denial prediction model flags claims statistically likely to be rejected before they leave the system, routing them to a pre-submission specialist queue.
An ambient documentation assistant, piloted across four clinics, converts physician-patient conversations into structured SOAP note drafts automatically, cutting average post-visit documentation from 24 minutes to under 5. A fax and referral extraction tool parses incoming unstructured documents and pre-populates the EHR with patient demographics, diagnosis codes, and referring provider details, eliminating manual re-entry across all intake channels.
Six regional HVAC brands operating across the Mid-Atlantic and Southeast. 340 field technicians, 85 office and dispatch staff. Built through a buy-and-build acquisition strategy over four years, with no shared operating infrastructure.
Six acquisitions had produced six dispatch systems: spreadsheets, whiteboards, and institutional knowledge locked in the heads of regional managers. The holding company had revenue but not a unified business. Overtime was running $4.1M annually with no cross-brand visibility into why. Technician utilization averaged just 58% across the portfolio, meaning nearly half of available field capacity was being wasted. Commercial quote turnaround stretched to 4–6 days because estimators drafted every proposal from scratch. And service agreement renewal rates were declining as the company struggled to coordinate follow-up across thousands of customer contracts at scale.
A unified dispatch engine deployed across all six brands now routes jobs by technician certifications (EPA 608, brand-specific equipment training), territory boundaries, real-time GPS proximity, overtime accumulation status, and job urgency tier. Preventive maintenance work orders auto-generate when service agreement equipment hits runtime or calendar thresholds. AP invoice matching and approval workflows eliminated manual entry errors and cut processing time by 60%.
A regional demand forecasting model built on 3 years of job history, weather data, equipment age distributions, and seasonal patterns enables staffing plans 6 weeks out at 87% accuracy, eliminating most last-minute overtime decisions. A service agreement churn propensity model trained on contract history, service frequency, and call sentiment data flags at-risk customers 90 days before renewal, enabling a structured save motion for the portfolio's highest-value contracts.
A commercial quote generation assistant drafts full proposals for standard installation and replacement jobs from equipment specs and labor time standards. What previously took estimators 90 minutes now takes under 8. Post-job service summaries are auto-generated and delivered to customers within 30 minutes of job completion. A mobile-first knowledge assistant gives field technicians instant access to equipment manuals, warranty terms, and troubleshooting scripts across all six brand portfolios.
Pacific Northwest firm serving 220+ business clients and 1,400+ individual tax filers. 130 staff including CPAs, bookkeepers, client advisors, and administrative staff across three office locations.
Tax season had become an annual crisis, not from lack of talent, but from lack of system. The same work that once took 3 months was stretching to 4, even with headcount additions each year. Partners identified two consistent root causes: client document intake was fragmented across email, portal, physical mail, and fax, all handled manually by staff who should have been doing advisory work, and engagement letter turnaround averaged 5 days from scoping call to client signature. At 87% retention, the firm was losing clients it shouldn't, largely due to slow response times and reactive rather than proactive communication patterns.
A unified intake workflow standardizes document collection across all channels. Entity-specific checklists are auto-generated from prior-year service scope on day one of each engagement, tracking completeness in real time and sending automated nudges for outstanding items. Tiered deadline alerts surface at-risk filings 90, 60, 30, and 7 days out. Billing automation triggers invoice delivery, tracks aging, and initiates collections outreach at day 15, 30, and 45 without manual intervention by staff.
A client churn risk model built on engagement frequency, portal responsiveness, billing history, and service satisfaction signals flags at-risk clients 60–90 days before their next renewal decision point, giving partners time to act, not react. A workload forecasting model segments clients by entity type, engagement complexity, and prior-year hours, enabling the firm to allocate staff to tax season 30% more accurately and reduce reliance on last-minute seasonal hires.
A document extraction engine processes uploaded financial statements, prior-year returns, 1099 packages, W-2s, and QuickBooks exports, structuring data directly into the engagement workflow and eliminating manual keying for standard client files. An engagement letter and proposal drafting assistant produces first drafts in under 90 seconds from scope inputs. An internal knowledge assistant gives junior staff instant access to IRS guidance, firm policies, and client history, reducing senior review time on routine questions by over 70%.
Client names and identifying details have been anonymized to protect confidentiality. Results reflect actual documented outcomes from FractionAI engagements.
Every engagement starts with an honest conversation about where your biggest opportunities are. No pitch deck, no commitment, just a strategic look at what AI can do for you.
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