From residency application coaching to match outcome tracking, ibl.ai deploys purpose-built AI agents that support every medical student through their career journey — at scale and in compliance.
On ibl.ai you own all the code and the data, run it model-agnostic across any LLM, and pay with no per-seat pricing — so you can deploy anywhere, from your own cloud to a fully air-gapped network.
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Medical school career services teams are stretched thin, supporting hundreds of students through one of the most high-stakes career transitions in any profession — the residency match.
Advisors must simultaneously guide students on ERAS applications, personal statements, specialty selection, and interview preparation, often with limited staff and no scalable tools built for clinical career pathways.
Without AI support, personalized coaching becomes inconsistent, outcome data goes uncaptured, and students at critical decision points are left waiting days for feedback that could shape their entire career trajectory.
Career advisors at medical schools support 150–300 students each, with demand spiking dramatically during ERAS season. Personalized guidance becomes impossible at this scale.
1 advisor per 200+ students at most medical schoolsStudents receive varying quality of feedback depending on advisor availability and expertise, leading to uneven preparation and avoidable application weaknesses.
Up to 60% of students report delayed or insufficient application feedbackResidency interviews are high-stakes and specialty-specific, yet most programs can only offer 1–2 mock interviews per student due to time and faculty constraints.
Students who practice 5+ mock interviews match at significantly higher ratesCareer services teams struggle to systematically collect and analyze match outcomes, rotation performance correlations, and employer feedback — limiting program improvement.
Fewer than 40% of medical schools have real-time match outcome dashboardsStudent health and academic records intersect in medical school career workflows. Generic AI tools create serious compliance exposure when handling sensitive student data.
HIPAA violations in education carry fines up to $1.9M per incidentA purpose-built ibl.ai agent guides students through ERAS applications, personal statement drafts, specialty selection, and program list building — available 24/7 with school-specific context baked in.
AI agents provide instant, structured feedback on medical CVs and personal statements aligned to specialty norms, ACGME expectations, and program director preferences.
Students practice residency interviews with AI agents trained on specialty-specific question banks, MMI formats, and behavioral frameworks — with instant performance feedback.
AI agents analyze student competency profiles, rotation evaluations, board scores, and career goals to recommend best-fit residency programs and fellowship opportunities.
Automated dashboards capture match rates, specialty placement, employer feedback, and longitudinal career outcomes — generating LCME and accreditation-ready reports on demand.
AI agents assist career services staff in managing relationships with residency programs, coordinating site visits, and personalizing outreach to expand placement opportunities.
Map existing career services workflows, integrate with student information systems, and configure HIPAA/FERPA-compliant data handling on your institution's infrastructure.
Deploy ibl.ai career coaching agents, ingest specialty-specific interview question banks, CV rubrics, and program matching criteria tailored to your school's student population.
Launch with a cohort of MS3 and MS4 students, train career advisors on AI-assisted workflows, and collect early feedback to refine agent behavior and content accuracy.
Scale to all eligible students, activate outcome tracking dashboards, and configure accreditation reporting pipelines. Continuous agent improvement based on match cycle data.
Students wait days for advisor availability during peak ERAS season, often submitting applications with unreviewed materials.
AI coaching agents provide instant, structured guidance on applications, program lists, and personal statements around the clock.
One or two faculty-led mock interviews per student, with no standardized feedback rubric or specialty-specific scenarios.
Unlimited AI mock interview sessions with specialty-specific question banks, real-time feedback, and performance trend tracking.
Students self-select programs based on informal advice, with limited data on fit relative to their academic and clinical profile.
AI agents analyze competency records, board scores, and rotation evaluations to generate data-driven, personalized program recommendations.
Match outcome data collected manually via email surveys weeks after Match Day, with significant gaps and no longitudinal view.
Automated outcome tracking dashboards capture real-time placement data, enabling accreditation reporting and program benchmarking.
Career services using generic AI tools with no HIPAA controls, creating institutional liability when handling student health-adjacent records.
All AI agents run on institution-owned infrastructure with HIPAA, FERPA, and SOC 2 compliance built in from day one.
Tracks student competencies, clinical rotation performance, and skills assessments to feed accurate data into program matching and generate accreditation-ready outcome documentation.
Provides the infrastructure to build, deploy, and manage all career services AI agents on institution-owned infrastructure — ensuring HIPAA compliance, zero vendor lock-in, and full data sovereignty.
ibl.ai is the agentic AI platform where you own all the code and the data. You self-host the entire stack inside your own perimeter, run it model-agnostic across any LLM and switch anytime, and pay by usage with no per-seat pricing — so you can deploy anywhere: your cloud, on-premise, GovCloud, or fully air-gapped.
Full source code under a perpetual license, running on your infrastructure. Not API access to someone else's platform — the stack itself is yours.
Run any LLM — Claude, GPT, Gemini, Llama, Command, or your own fine-tune — and switch providers without rewriting the platform.
Usage-based billing against a budget cap you set. Cost tracks what your organization actually uses, not how many people you employ.
Your cloud, your VPC, on-premise, GovCloud, or a fully air-gapped network with no outbound connectivity.
1.6M+ users across 400+ organizations run the platform this way, including NVIDIA, MIT, and Syracuse University.
ibl.ai is family-owned and operated from New York, NY — a U.S.-headquartered, domestically-owned long-term partner, not a vendor that sells licenses and moves on.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.