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Chapel Hill is academic and healthcare research — home to UNC-Chapel Hill, one of the top public research universities in the country, and UNC Healthcare, which operates hospitals and clinics across the region. The automation opportunity is distinct from corporate hubs like Cary: instead of sales operations or regulatory submissions, Chapel Hill automation addresses research workflow acceleration (grant intake and tracking, research data management, publication workflow), academic operations (student enrollment and records, course scheduling, classroom utilization), and healthcare research (clinical trial patient recruitment and screening, research data coordination, regulatory submission for Institutional Review Board approvals). These workflows are often fragmented across legacy academic systems, spreadsheets, and manual email coordination. An engagement addressing research data management might cost seventy-five to one hundred fifty thousand and run twelve to sixteen weeks. An engagement addressing clinical trial recruitment and screening might cost one hundred to one hundred seventy-five thousand and run fourteen to eighteen weeks. The consulting talent is a mix of academic IT (UNC's own IT staff), boutique academic operations consulting, and larger consulting firms with academic practice areas.
Updated May 2026
UNC receives thousands of research proposals annually — from faculty applying for grants, from foundations and government agencies issuing RFPs, and from collaborative institutions proposing partnerships. A typical research workflow: an RFP arrives, the grants office routes it to relevant faculty (based on keyword matching), faculty decide to pursue it, the grants office coordinates a proposal team, timeline and milestones are tracked, and if funded, the grant is handed off to research administration for compliance tracking. Currently, much of this routing and coordination is manual. An agentic automation system reads incoming RFPs, extracts key details (funder, deadline, research areas, funding amount), matches to faculty expertise (by reading faculty profiles and prior grant history), routes the RFP to matched faculty with a summary, and tracks response. When a proposal is submitted, the automation system monitors the funder's decision portal, and alerts the grants office and PI when a decision is rendered. For a research university managing 500-1,000 RFPs annually, this automation saves 30-40% of grants-office labor. The engagement typically runs twelve to sixteen weeks and costs seventy-five to one hundred fifty thousand dollars. The complexity is integrating with UNC's legacy grants management system and faculty research administration databases.
UNC Healthcare runs hundreds of active clinical trials. A typical trial needs to recruit 30-100 patients, and screening candidates is labor-intensive: research coordinators review electronic health records to identify potentially eligible patients, contact them, verify eligibility in detail, and enroll if the patient is interested. With hundreds of trials competing for the same patient pool, automation that screens and prioritizes candidates is valuable. An agentic automation system reads EHR data (with proper privacy controls), extracts clinical criteria (disease status, medication list, prior treatments), automatically screens against trial eligibility criteria, and routes qualifying patients to research coordinators with a risk score (how likely the patient is to be eligible and interested). For complex trials with many eligibility criteria, this automation can pre-screen hundreds of patient records in hours — work that would take coordinators weeks. The engagement typically runs fourteen to eighteen weeks and costs one hundred to one hundred seventy-five thousand dollars, with significant complexity around HIPAA compliance, IRB review, and clinician trust.
UNC-Chapel Hill enrolls 29,000+ students, and the registrar's office manages course scheduling and enrollment workflows annually. A typical workflow: academic departments submit course offerings for the next term, the registrar's office validates them (checking for duplicate course codes, instructor availability, classroom availability), schedules classes into available rooms and time slots, and publishes the schedule for student registration. Overlaps and conflicts abound: two courses requesting the same classroom at the same time, an instructor double-booked, a classroom booked that is undergoing renovation. Currently, scheduling is partially automated (a legacy system handles some constraints) but many conflicts are resolved manually. An agentic automation system can model the full scheduling problem: read all course requests, apply constraints (classroom capacity, instructor preferences, prerequisite sequencing), and generate an optimized schedule that minimizes conflicts. For UNC with 1,000+ courses to schedule, this automation reduces manual conflict resolution from weeks to days. The engagement typically runs ten to fourteen weeks and costs seventy-five to one hundred twenty-five thousand dollars.
Start with keyword matching (simple, explainable) as Phase 1, then augment with ML as Phase 2. Keyword matching works: read the RFP, extract keywords, match against faculty profiles and prior grant abstracts. This gets to 70-80% accuracy quickly (eight weeks). Then build an ML model trained on historical RFP-to-faculty matches (which faculty actually pursued which RFPs, which were funded) and refine the matching. The two-phase approach lets you launch fast and improve with real data. Faculty also trust keyword matching more than they trust a mysterious ML model, which matters for adoption.
The automation system should not see PHI (patient names, dates of birth, medical record numbers). Instead, work with the clinical trial IRB and privacy officer to design a de-identified screening workflow: the EHR system generates a de-identified patient dataset (age range, diagnoses, medications, no names or dates), the automation system screens against trial eligibility using that de-identified data, and outputs a list of patient record numbers (without PHI) that meet eligibility criteria. Research coordinators then use those record numbers to look up the actual patients. This keeps the automation system out of HIPAA scope and lets it operate efficiently.
Define special requirements explicitly: "Computer Science courses need labs with 30-person capacity and ethernet"; "Music courses need practice rooms"; "Chemistry courses need fume hoods." The scheduling agent reads these requirements, filters available classrooms to those that match, and schedules accordingly. For highly constrained courses (e.g., only one room on campus meets the requirements), schedule those first, then schedule remaining courses around them. This avoids conflict later.
Chapel Hill rates are typically fifteen to twenty-five percent lower than Cary (which is corporate-software-focused) and roughly equivalent to Durham (which has healthcare and some academic presence). A typical Chapel Hill research or healthcare automation engagement runs seventy-five to one hundred seventy-five thousand dollars, versus one hundred fifty to three hundred in Cary. The difference reflects smaller average engagement size and the academic sector's lower IT budget density than corporate tech.
Faculty adoption and trust. Researchers are used to finding funding opportunities themselves (through mailing lists, conferences, colleague tips); an automated RFP routing system can feel like an intrusion or a loss of autonomy. The best Chapel Hill partners front-load extensive engagement with faculty, demonstrate that the automation is a tool that helps them, not a system that decides for them, and involve faculty in refining matching criteria. That adds two to three weeks to the timeline but prevents low adoption post-launch. The automation should empower faculty, not replace their judgment.
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