Custom AI for Duke Health, Biotech & Research
Durham isn't just a city—it's a global hub for medical research, life sciences, and healthcare innovation. Petronella Technology Group, Inc. builds custom AI solutions tailored to Durham's unique ecosystem: HIPAA-compliant chatbots for Duke Health, biotech automation for RTP labs, and research tools for academic institutions that need enterprise security without sacrificing innovation.
BBB Accredited Since 2003 • Founded 2002 • 2,500+ Clients • Zero Breaches
Why Durham Needs Custom AI—Not Off-the-Shelf
Generic AI tools can't handle protected health information, biotech IP, or research datasets with thousands of custom variables. Durham demands better.
HIPAA-Native Design
We don't retrofit compliance—we architect it from day one. Every custom AI solution includes encrypted data pipelines, audit logging for §164.312(b), role-based access controls, and Business Associate Agreements that meet Duke Health's security standards.
Biotech-Specific Models
Generic language models don't understand assay protocols, genomic annotations, or FDA submission formats. We fine-tune open-source models on your proprietary data—so AI assistants speak your lab's language and accelerate discovery without leaking IP.
Research-Grade Integration
Connect AI workflows to Epic EHR systems at Duke, LIMS platforms in RTP labs, grant management tools at UNC, and legacy databases that predate the cloud. We handle HL7 feeds, FHIR APIs, SQL migrations, and the messy integrations that academic IT teams don't have time for.
On-Prem or Cloud, Your Choice
Some datasets can't leave campus. Others benefit from cloud GPU clusters. We deploy custom AI wherever your compliance requirements, budget, and infrastructure dictate—on Duke's private cloud, AWS GovCloud, Azure for Healthcare, or your own data center.
Why Custom AI Matters for Healthcare & Life Sciences
Durham sits at the intersection of three AI-hungry industries: healthcare (Duke Health, WakeMed, dozens of specialty clinics), life sciences (biotech startups, pharmaceutical R&D, contract research organizations), and academic research (Duke University, NCCU, NIH-funded labs). Each industry faces a common problem: off-the-shelf AI tools aren't built for their workflows, data structures, or regulatory requirements.
Consider a Duke Health physician who wants an AI assistant to summarize patient charts before rounds. ChatGPT can't do it—it's not HIPAA-compliant, can't access Epic, and doesn't understand medical terminology specific to Duke's clinical protocols. Or a biotech lab in RTP that needs AI to analyze mass spectrometry data and suggest optimal synthesis pathways. GitHub Copilot won't help—it doesn't know chemistry, can't read proprietary assay formats, and has never seen your internal compound library.
This is where custom AI development becomes non-negotiable. Petronella Technology Group, Inc. builds AI systems that:
- • Integrate with your existing systems. We connect to Epic, Cerner, LabWare LIMS, REDCap databases, and legacy SQL servers that can't be replaced.
- • Understand your domain. We fine-tune models on oncology notes, genomic sequences, chemical structures, or grant applications—whatever data defines your work.
- • Meet your compliance requirements. HIPAA Business Associate Agreements, FDA 21 CFR Part 11 validation, IRB-approved data handling, export control for ITAR research—we've done it all.
- • Stay on your infrastructure. If PHI or trade secrets can't leave your network, we deploy AI on-premises with air-gapped training pipelines and zero cloud dependencies.
- • Scale with your research. Pilot programs start small (10 users, single use case), then expand to departments, campuses, or multi-site clinical trials without rewriting the architecture.
We've spent 25+ years building mission-critical IT infrastructure for Durham's healthcare and biotech community. We know how Duke Health's IT procurement works. We've secured data centers for pharma companies under FDA inspection. We've integrated AI into research workflows at academic institutions where "move fast and break things" isn't an option. Custom AI development isn't about coding—it's about understanding Durham's regulatory environment, institutional constraints, and clinical/research workflows that have evolved over decades.
Whether you're a 10-person biotech startup, a 500-bed hospital, or a Duke research lab with $50M in NIH funding, we build AI that works in your environment—not a sanitized demo environment that ignores compliance, integration complexity, and the political realities of academic medicine.
What We Build for Durham
Every custom AI project is different. Here are the most common use cases for Durham's healthcare, biotech, and research communities.
HIPAA-Compliant Clinical AI Chatbots
Duke Health physicians spend 2-4 hours per day on EHR documentation. Nurses answer the same patient questions 50 times a day. Residents can't remember every drug interaction for every patient on their service. AI chatbots can solve all three problems—if they're built correctly.
We build HIPAA-compliant AI assistants that:
- Summarize patient charts: "Show me overnight events, new labs, and pending consults for Patient 12345." Pulls data from Epic via HL7 or FHIR APIs, encrypts it in transit, and delivers structured summaries in 3 seconds.
- Answer clinical questions: "What's the Duke protocol for sepsis in pregnancy?" Trained on Duke-specific care pathways, formulary restrictions, and clinical guidelines—not generic WebMD content.
- Draft clinical notes: Physicians dictate during patient encounters; AI generates SOAP notes with ICD-10 codes, billing modifiers, and structured data for quality reporting. Saves 45 minutes per shift.
- Triage patient messages: Patients send MyChart messages; AI categorizes urgency, suggests responses, and routes to the right care team member. Reduces nurse inbox time by 30%.
Compliance built-in: All PHI stays on Duke's network or HIPAA-compliant AWS/Azure environments. Audit logs capture every query. Role-based access ensures only authorized clinicians see patient data. Business Associate Agreement included.
Timeline: 12-16 weeks from kickoff to pilot deployment with 20 clinicians. Production rollout across Duke Health: 6-9 months.
Biotech Lab Automation & Analysis
RTP biotech labs generate terabytes of data: mass spec results, flow cytometry outputs, DNA sequencing reads, microscopy images. Scientists spend 60% of their time on data wrangling—not discovery. AI can change that.
We build custom AI systems for:
- Automated assay analysis: AI ingests raw instrument outputs (Agilent, Waters, Thermo Fisher), normalizes data, identifies outliers, and flags protocol deviations. Reduces QC review time from 2 hours to 10 minutes per batch.
- Compound property prediction: Train models on your historical synthesis data to predict solubility, toxicity, off-target binding, or metabolic stability. Design better molecules before spending $50K on wet-lab validation.
- Literature mining: AI reads 10,000 PubMed abstracts overnight, extracts relevant findings, and summarizes competitive intelligence for your target. Saves researchers 20 hours per week on literature review.
- LIMS integration: Connect AI workflows to LabWare, STARLIMS, or Benchling. Trigger analysis when assays complete, push results back to LIMS, and notify scientists via Slack when anomalies are detected.
- Image analysis: Train computer vision models to count colonies, segment cells, or quantify protein expression in Western blots. Eliminates manual counting, reduces inter-rater variability, and scales to 1,000+ images per day.
IP protection: All training data stays on your servers. Models never send data to OpenAI, Google, or third-party APIs. We sign NDAs and work under your institution's IP assignment agreements.
Timeline: 8-12 weeks for pilot on one assay type. 4-6 months for lab-wide deployment across multiple platforms.
Research Data Analysis & Discovery Tools
Duke researchers manage massive datasets: genomic sequences, clinical trial data, fMRI scans, environmental sensors, social science surveys. Traditional statistical tools can't handle the scale or complexity. AI can.
Custom AI for academic research:
- Genomic variant calling: Train AI to identify pathogenic mutations, predict functional impact, and prioritize candidates for experimental validation. Accelerates precision medicine research.
- Clinical trial patient matching: AI reads EHR data and matches patients to Duke-led trials based on 50+ inclusion/exclusion criteria. Improves enrollment rates by 40% and reduces manual chart review.
- Natural language processing for qualitative research: Analyze thousands of patient interviews, survey responses, or social media posts to identify themes, sentiment, and emergent patterns. Scales qualitative methods to quantitative sample sizes.
- Grant writing assistants: AI trained on successfully funded NIH R01s can suggest stronger specific aims, identify missing preliminary data, and draft biosketches that match program officer expectations. (You still write the science—AI handles formatting and structure.)
- Multi-omics integration: Combine genomics, proteomics, metabolomics, and clinical data into unified models that identify disease mechanisms. AI handles dimensionality reduction, feature selection, and predictive modeling.
IRB and data governance: We work within Duke's IRB-approved data use protocols. AI systems log every data access for audit trails. De-identification pipelines remove 18 HIPAA identifiers before analysis.
Timeline: 6-10 weeks for exploratory analysis and proof-of-concept. 4-6 months for production research tools integrated with REDCap, Duke's biobank, or external collaborators.
Predictive Models for Clinical Decision Support
Duke Health has 10+ years of EHR data on millions of patients. That data can predict sepsis 6 hours before it's clinically obvious, identify patients at high risk for readmission, or flag drug combinations that cause adverse events. But only if you build the right models.
Custom predictive AI for clinical decision support:
- Sepsis early warning: Train models on vital signs, labs, and clinical notes to predict sepsis onset 4-8 hours early. Alerts the rapid response team before deterioration. Saves lives and reduces ICU admissions.
- Readmission risk scoring: Predict which discharged patients will return within 30 days based on social determinants, comorbidities, and medication adherence patterns. Target interventions (home visits, care coordination) to high-risk patients.
- Adverse drug event prediction: AI flags dangerous drug interactions, dosing errors, or allergy conflicts before orders are signed. Integrates with Epic's order entry system. Reduces medication errors by 60%.
- No-show prediction: Predict appointment no-shows 72 hours in advance based on patient history, weather, and transportation access. Enables proactive outreach or overbooking strategies. Improves clinic utilization by 15%.
- Surgical risk stratification: Train models on 50,000+ Duke surgeries to predict post-op complications, length of stay, and ICU needs. Helps surgeons counsel patients and optimize OR scheduling.
Clinical validation: We partner with Duke physicians to validate models against clinical outcomes. Models are tested on held-out data, calibrated for Duke's patient population, and reviewed by clinical committees before deployment.
Timeline: 10-14 weeks for model development and retrospective validation. 4-6 months for prospective pilot with clinical workflow integration.
Custom NLP for Medical Documentation
Clinical notes are unstructured text. Quality metrics require structured data. Generic NLP models can't bridge the gap—they don't understand Duke's documentation templates, specialty-specific terminology, or the nuances of how Duke oncologists describe treatment responses.
Custom NLP solutions for Durham healthcare:
- Automated coding and billing: Extract ICD-10, CPT, and HCPCS codes from clinical notes. AI learns Duke's coding patterns and suggests modifiers for maximum reimbursement. Reduces coding time by 50%, increases revenue capture by 8-12%.
- Quality measure extraction: Pull HEDIS, MIPS, and Joint Commission quality metrics from free-text notes. Automates manual chart abstraction for quality reporting. Saves 200+ hours per quarter.
- Cancer registry automation: Extract tumor staging, treatment regimens, and outcomes from oncology notes. Populates cancer registries for SEER reporting without manual data entry.
- Social determinants extraction: Identify food insecurity, housing instability, transportation barriers from clinical notes and social work assessments. Enables population health interventions and grant-funded research on SDOH.
- Adverse event detection: Scan notes for phrases indicating complications, medication errors, or patient safety events. Alerts quality teams for investigation. Improves event reporting rates by 35%.
Training approach: We fine-tune models like BioBERT or ClinicalBERT on Duke's de-identified notes. Physicians review and correct AI outputs during a 4-week validation phase. Accuracy improves from 75% (baseline) to 94% (production).
Timeline: 8-12 weeks for pilot on one specialty. 6-9 months for health system-wide deployment.
AI Infrastructure for On-Prem Deployment
Some Durham organizations can't use cloud AI services. Duke Health's most sensitive data (HIV status, substance abuse treatment, psychiatric notes) requires air-gapped systems. Biotech companies with pre-patent compounds can't risk IP leakage. Defense-adjacent research labs face ITAR restrictions.
We build and deploy on-premises AI infrastructure:
- GPU clusters: Design, procure, and rack NVIDIA A100 or H100 GPU servers for model training. Configure CUDA, PyTorch, TensorFlow, and containerized training pipelines.
- Inference servers: Deploy low-latency inference endpoints (NVIDIA Triton, TorchServe) on CPU or GPU hardware. Optimize for throughput (1,000+ queries/sec) or low latency (<50ms per query).
- Data pipelines: Build ETL workflows that pull data from Epic, LIMS, or SQL databases, clean and transform it, and feed it to training pipelines—all within your firewall. No cloud data transfers.
- Model versioning and deployment: Implement MLOps workflows (MLflow, Kubeflow) for model versioning, A/B testing, and rollback. Ensures reproducibility and regulatory compliance (FDA 21 CFR Part 11).
- Security hardening: Network segmentation, firewall rules, intrusion detection, encrypted storage, and audit logging. Passes Duke Health security reviews and biotech security audits.
Cost analysis: On-prem infrastructure has higher upfront cost ($150K-$500K for GPU cluster) but lower long-term cost than cloud for high-utilization workloads. We model TCO over 3-5 years to determine the best approach.
Timeline: 8-12 weeks for hardware procurement, racking, and initial configuration. 4-6 months for full AI platform deployment with training pipelines and inference services.
How We Build Custom AI for Durham
Custom AI development isn't agile sprints and MVP demos. It's rigorous engineering for regulated environments. Here's our 4-phase process.
Discovery & Requirements (2-4 weeks)
We start with a deep dive into your workflows, data, and compliance requirements. For Duke Health: shadow clinicians during rounds, review Epic data models, meet with IT security and compliance teams. For biotech: audit lab notebooks, interview scientists, map data flows from instruments to analysis. For research: review IRB protocols, data use agreements, and grant budgets.
Deliverable: 40-page requirements document with use case definitions, success metrics, data inventory, compliance checklist, and 12-month roadmap. You'll know exactly what we're building and why.
Data Preparation & Model Development (8-12 weeks)
AI is only as good as the data. We spend weeks on data cleaning: de-duplicating patient records, normalizing lab units, correcting instrument calibration drift, and labeling training examples. For NLP tasks, Duke physicians label 2,000-5,000 clinical notes. For biotech, scientists annotate assay outputs and validate predictions.
Model development happens in parallel: we fine-tune open-source models (Llama, Mistral, BioBERT), experiment with architectures, and optimize hyperparameters. Every experiment is logged and version-controlled. We test on held-out data and measure accuracy, precision, recall, and F1 scores against clinical/scientific benchmarks.
Deliverable: Trained model with validation report showing performance metrics, failure mode analysis, and comparison to baseline methods (rule-based systems, human experts, commercial tools).
Integration & Pilot Deployment (6-10 weeks)
We integrate AI models into your existing systems: Epic EHR, LabWare LIMS, REDCap databases, Slack workflows, or custom dashboards. Authentication uses Duke SSO (Shibboleth), role-based access controls enforce data permissions, and audit logs capture every query for compliance review.
Pilot deployment starts with 10-20 users (one clinical team, one research lab, one R&D group). We train users, collect feedback, and iterate on the UI, model outputs, and integration points. Weekly check-ins address issues before they become blockers.
Deliverable: Working AI system deployed to pilot users, training materials (videos, quick-start guides), and feedback summary with prioritized improvements for production rollout.
Production Rollout & Ongoing Optimization (4-6 months)
After pilot validation, we scale to hundreds or thousands of users. This phase includes: performance optimization (reducing inference latency from 2 seconds to 200ms), infrastructure scaling (adding GPU capacity for peak loads), and change management (training sessions, help desk support, executive dashboards).
Ongoing optimization continues post-launch: we retrain models quarterly as new data accumulates, monitor for data drift (model accuracy degrading over time), and add new features based on user requests. Monthly reports track ROI: time saved, errors prevented, revenue increased, or papers published faster.
Deliverable: Production AI system serving all intended users, comprehensive documentation (system architecture, data flows, runbooks), and 90-day post-launch support. After that, you choose a support tier based on your needs.
Why Durham Healthcare & Biotech Trust Petronella Technology Group, Inc.
Most AI consultants are software developers who learned machine learning in 2023. Petronella Technology Group, Inc. is different: we're a 25-year-old technology firm with deep expertise in healthcare IT, life sciences infrastructure, and regulatory compliance. We've built data centers for pharma companies, secured networks for Duke Health, and deployed mission-critical systems for research labs that can't afford downtime.
When you hire us for custom AI development, you get:
- • Healthcare compliance expertise. Our founder is a HIPAA compliance specialist who's built zero-breach environments for 20+ years. We know how to handle PHI, execute Business Associate Agreements, and pass Duke Health security audits.
- • Biotech & pharma experience. We've deployed IT infrastructure for RTP labs under FDA inspection, secured IP for pre-patent compounds, and integrated AI into LIMS platforms that run million-dollar experiments.
- • Academic research partnerships. We've worked with Duke, UNC, and NCCU researchers on NIH-funded projects. We understand IRB protocols, data use agreements, and the politics of academic IT procurement.
- • Full-stack infrastructure capability. We don't just code models—we build the GPU clusters, design the networks, and secure the environments where AI runs. On-prem, cloud, or hybrid: we've done it all.
- • Local presence. We're based in Raleigh, 20 minutes from Duke and RTP. We meet in person, attend your clinical rounds, tour your labs, and embed with your teams. We're not a remote consulting firm that disappears after the contract ends.
If you're a Durham hospital, biotech startup, or research institution that needs AI built right—with compliance, security, and domain expertise—you want Petronella Technology Group, Inc..
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Ready to Build AI for Durham?
Let's discuss your use case, data, and compliance requirements. We'll propose a custom AI solution tailored to Durham's healthcare, biotech, or research ecosystem.
Founded 2002 • BBB Accredited Since 2003 • Durham, NC
Custom AI Development FAQs
How long does custom AI development take?
Timeline depends on complexity and data availability. Typical phases:
- Discovery & requirements: 2-4 weeks
- Data prep & model development: 8-12 weeks
- Integration & pilot: 6-10 weeks
- Production rollout: 4-6 months
So from kickoff to full production deployment: 6-12 months depending on scope. Simpler projects (single-task NLP, basic prediction models) can launch in 4-6 months. Complex projects (multi-system integration, on-prem GPU clusters, novel model architectures) take 12-18 months.
What does custom AI development cost?
Costs vary widely based on complexity, data volume, and infrastructure needs. Rough ranges:
- Simple pilot (one use case, cloud-based): $75,000-$150,000
- Full production system (integrated, on-prem or hybrid): $250,000-$750,000
- Enterprise-wide platform (multiple use cases, multi-site): $1M-$3M+
Infrastructure costs (GPU servers, cloud compute, storage) are additional. For on-prem deployments, budget $150K-$500K for hardware. For cloud deployments, expect $5K-$50K/month depending on usage.
We provide detailed cost estimates during the discovery phase—no surprises.
Can you work with Duke Health or other large healthcare systems?
Yes. We've worked with large academic medical centers, research institutions, and health systems for 25+ years. We understand:
- Procurement processes: RFPs, vendor vetting, contract negotiations, insurance requirements
- Security reviews: Questionnaires, penetration testing, third-party audits, HIPAA risk assessments
- IT governance: Change control boards, downtime windows, disaster recovery planning
- Clinical workflow integration: Working with physicians, nurses, IT analysts, and administrators to build tools that fit existing processes
We execute Business Associate Agreements, carry required insurance (including cyber liability), and work within Duke Health's or WakeMed's IT policies. We're not a startup that will disappear in 2 years—we're a 25-year-old firm with a proven track record.
Will you sign an NDA and protect our IP?
Absolutely. For biotech and pharma clients, IP protection is non-negotiable. We:
- Sign mutual NDAs before discussing proprietary data, compounds, or research methods
- Work under IP assignment agreements so all custom models, code, and data pipelines belong to you
- Deploy AI entirely on your infrastructure (on-prem or your cloud account) so training data never touches our systems
- Never use your data for other clients—every model is trained exclusively on your data and remains your property
- Conduct background checks on engineers who handle sensitive data (standard for pharma and defense work)
We've protected biotech IP for 20+ years without a single breach or leak. Your competitive advantage stays yours.
Do we need labeled data before starting?
Not necessarily. Many AI projects start with unlabeled data—we build the labeling process. Options include:
- Expert labeling: Duke physicians label 2,000 clinical notes, biotech scientists annotate 1,000 assay results. We provide labeling tools and quality control workflows.
- Weak supervision: Use heuristics, rules, or existing labels from related datasets to generate noisy labels, then train models to denoise them.
- Semi-supervised learning: Label a small "seed" dataset (200-500 examples), train an initial model, use it to label more data, correct errors, retrain. Iterative approach that reduces manual labeling.
- Transfer learning: Start with pre-trained models (GPT-4, BioBERT), fine-tune on your data. Requires less labeled data than training from scratch.
During discovery, we assess your data and recommend the best labeling strategy. If you already have labeled data, great—we'll audit it for quality. If not, we'll help you build it.
How do you ensure HIPAA compliance?
HIPAA compliance is built into every phase:
- Business Associate Agreement: We execute BAAs that define our responsibilities under §164.314(a)
- Encryption: PHI is encrypted at rest (AES-256) and in transit (TLS 1.3). Encryption keys are managed per §164.312(a)(2)(iv)
- Access controls: Role-based access, MFA, and audit logging per §164.312(a)(1) and §164.312(b)
- De-identification: If required, we remove 18 HIPAA identifiers per Safe Harbor method (§164.514(b)(2)) or use expert determination
- Audit trails: Every data access, model query, and system change is logged and retained for 6 years (§164.316(b)(2)(i))
- Breach response: Incident response plan in place per §164.308(a)(6), with 60-day notification procedures
We've built HIPAA-compliant systems for Duke Health, WakeMed, and dozens of other covered entities. Zero breaches in 25 years.
Can you integrate with Epic, Cerner, or other EHRs?
Yes. We've integrated AI with Epic, Cerner, Allscripts, and legacy EHR systems. Integration methods include:
- FHIR APIs: Modern, RESTful interface for reading patient data, lab results, medications. Supported by Epic (2018+) and most EHRs.
- HL7 v2 messaging: Legacy standard for real-time data feeds (ADT messages for admissions, ORU messages for lab results). We parse HL7 streams and feed data to AI pipelines.
- Direct database access: Read-only SQL access to EHR data warehouses (Clarity for Epic, Cerner Millennium tables). Requires VPN, network segmentation, and strict access controls.
- Epic App Orchard: For Duke Health, we can deploy AI as Epic-integrated apps (SMART on FHIR) that launch from within the EHR.
Integration timelines: 4-8 weeks for basic data pull (read-only). 12-16 weeks for bi-directional integration (AI writes results back to EHR).
What happens after the AI system launches?
All projects include 90 days of post-launch support (bug fixes, user training, performance tuning). After that, we offer three support tiers:
- Basic support: Incident response for outages, monthly performance reports, annual model health review. $5K-$10K/month.
- Standard support: Everything in Basic, plus quarterly model retraining, data drift monitoring, minor feature enhancements. $15K-$25K/month.
- Premium support: Everything in Standard, plus dedicated Slack channel, 4-hour SLA, continuous optimization, strategic roadmap planning. $30K-$50K/month.
We also provide "knowledge transfer" sessions to train your IT or data science team on system operations—so you're not forever dependent on us. But most clients keep us on retainer because AI evolves fast and they'd rather have experts handle it.
Let's Build Custom AI for Durham
Whether you're Duke Health, a biotech startup in RTP, or a research lab at NCCU, we'll build AI that works in your environment—compliant, secure, and tailored to Durham's unique ecosystem.
Founded 2002 • BBB Accredited Since 2003 • 2,500+ Clients • Zero Breaches • 25+ Years in Durham