Chart summarization
Condense a long record into a clinician-ready synopsis with citations.
Generative AI for Healthcare
Generative AI healthcare development
We build retrieval-augmented (RAG) systems over clinical data: summaries, drafting, and Q&A grounded in the actual record, with PHI guardrails, citations back to source, and human review on anything clinical.
HIPAA-ReadyGrounded RAGCited OutputHuman Review
Trusted by global innovators
What is generative AI in healthcare?
Generative AI in healthcare is software that drafts and summarizes clinical text, such as chart summaries, discharge notes and patient messages, from the record itself. Agnotic Technologies builds it as grounded retrieval over your data, with citations, PHI guardrails and clinician review, so output can be checked before it reaches a chart.
A model that invents a medication or a lab value is worse than no model. We build retrieval-augmented generation (RAG) that answers only from permissioned source documents, cites what it used and abstains when the record does not support an answer.
What it is
How is generative AI kept to the record?
Generative AI for healthcare uses large language models to summarize, draft, and answer questions, but constrained by retrieval so the output is grounded in your actual clinical data rather than the model's training set.
The engineering that matters is retrieval quality, PHI guardrails, citation, abstention, and human review. We build all of it, and we measure faithfulness rather than trusting a good demo.
What does a clinical generative AI architecture look like?
Retrieval, PHI guardrails, grounded generation with citations, and a review layer, assembled so you can defend every output to compliance and to clinicians.
What goes into a clinical GenAI system?
Each capability pairs a generative feature with the guardrail that makes it safe to ship.
Retrieval-Augmented Generation (RAG)
Answers are grounded in retrieved clinical documents via a vector store and hybrid search over FHIR resources and notes, so the model draws from the real record instead of its training data.
Clinical Summarization & Drafting
Chart summaries, visit recaps, referral letters, and patient-friendly instructions: each generation carries citations back to the source span so a clinician can verify before signing.
PHI Guardrails & Access Control
Prompt and output filtering, per-user data scoping, de-identification where full PHI isn't needed, and BAA-covered model hosting so no PHI leaks into a public endpoint.
Grounding, Citations & Abstention
The system cites the exact documents it used and abstains when retrieval doesn't support an answer: hallucination is engineered against, not hoped away.
FHIR-Aware Retrieval
Retrieval spans structured FHIR R4 resources and unstructured notes, so a summary reflects both the coded record and the narrative, not one at the expense of the other.
Evaluation & Human-in-the-Loop
Faithfulness, groundedness, and safety are evaluated against curated test sets, and clinical output routes through review before it reaches a chart or a patient.
Where it runs
Where can generative AI help?
Ambient note drafting
Draft structured notes for clinician review and sign-off.
Patient communication
Plain-language discharge and instruction drafts at the right reading level.
Prior-auth support
Assemble and draft supporting documentation from the record.
Clinical Q&A
Answer questions about a patient grounded in their own data.
Coding & documentation assist
Suggest codes and surface documentation gaps for review.
Where does generative AI pay off in healthcare?
Grounded summarization and drafting save clinician time, but only when hallucination is engineered against and a human stays in the loop.
BAA only
PHI goes only to BAA-covered model endpoints
Cited
Clinical generations carry source citations
Review-gated
output before it reaches a chart or patient
Compliance-First Healthcare App Development Services Backed by Global Standards
15-Minute Scoping Call01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Standards we build against
Which standards and controls does our GenAI stack use?
FHIRHIPAAHITECHNISTSNOMED
Our Process
How do we take GenAI to production?
We treat evaluation and guardrails as the hard part of clinical GenAI, because they are.
1.
Use-Case & Risk Framing
We pick a use case with clear value and bounded risk, define what 'correct' means, and decide where a human must stay in the loop.
Risk-framed
2.
Retrieval & Grounding Build
We build the RAG pipeline over your clinical data: chunking, embeddings, hybrid retrieval, and citation tracking across FHIR resources and notes.
Grounded RAG
3.
Guardrails & Evaluation
PHI filtering, abstention behavior, and a faithfulness/groundedness eval harness so quality and safety are measured against real test cases.
Measured safety
4.
Deploy with Review
Human-in-the-loop review for clinical output, BAA-covered hosting, and monitoring for drift, cost, and hallucination signals in production.
Audit-ready
Facts and sources
Which rules apply to clinical GenAI?
Short facts we design around, each linked to its primary source.
A HIPAA covered entity may share protected health information with a business associate, such as a software vendor, only under a business associate agreement that commits the vendor to safeguard it.
Source: 45 CFR 164.502(e) and 164.504(e) (eCFR)HIPAA defines a business associate as a vendor that creates, receives, maintains or transmits protected health information for a covered entity, so a cloud service that maintains ePHI falls inside the definition.
Source: 45 CFR 160.103 (eCFR)The NIST AI Risk Management Framework is a voluntary framework that organizes AI risk work into four functions: Govern, Map, Measure and Manage.
Source: NIST AI 100-1The HIPAA Security Rule requires administrative, physical and technical safeguards for electronic protected health information.
Source: 45 CFR Part 164 Subpart C (eCFR)
Featured case study
Read Case StudyLera Health: compliant women's health platform
Lera Health demonstrates our approach to turning clinical inputs into trustworthy, personalized output within a privacy-first data layer: the same foundation that makes generative AI safe to deploy in a regulated setting.
Why Partner With Us
15-Minute Scoping CallWhy build GenAI with Agnotic?
We build for grounding, safety, and audit: the properties that separate a healthcare GenAI product from a risky demo.
Guardrails-First
PHI filtering, grounding, and abstention are designed in from the first prototype, not patched after a bad output.
Grounded, Not Generic
RAG over your real clinical data with citations, so answers reflect the patient in front of you, not the internet.
Human-in-the-Loop by Design
Clinical output routes through review, and we build the workflow that makes that fast rather than a bottleneck.
Clinical-Domain Depth
We understand where generative AI helps and where it must never be trusted unattended.
Our relevant experience
Generative AI that survives a compliance review
Frequently Asked Questions
We use retrieval-augmented generation so the model answers only from retrieved source documents, cites the spans used, and makes the system abstain when the record doesn't support an answer. We then evaluate faithfulness against a curated test set. Hallucination is engineered against and measured, not left to chance.
Only under the right controls. We run models on BAA-covered infrastructure, apply prompt and output filtering, scope retrieval to what each user is permitted to see, and de-identify where full PHI isn't required. No PHI is ever sent to a public, non-BAA endpoint.
Off-the-shelf tools work for generic tasks, but grounding on your data, your workflows, and your compliance posture is where they fall short. If the value depends on your record and your process, a custom build pays off. We often start with a narrow, high-value use case so you can prove ROI before committing to a platform.
Yes. Retrieval spans FHIR R4 resources and unstructured notes, and we integrate via FHIR APIs, HL7 v2 feeds, or direct connections to your data stores. Output can be written back as structured resources or surfaced in your existing UI.
Data integration and evaluation, more than the model. A single use case with retrieval, guardrails and a review workflow is the usual first release; more workflows and EHR write-back add scope. We scope it on a discovery call.
It depends on the use case and your data. We start with one grounded, evaluated use case with guardrails and a review workflow; production hardening, EHR write-back and broader coverage follow. We give you a timeline after the scoping call.
Yes. Prompts, retrieved records and model outputs can all contain PHI, so Agnotic signs a business associate agreement before any PHI is shared with our team, and the model hosting you use must be covered by a BAA too.
Yes. We scope the first release to one workflow, such as grounded summaries of one note type with citations and clinician review, and evaluate it on your own test cases. More use cases and EHR write-back follow once that one holds up.
AI tools help our engineers draft code and tests, but a person reviews every change, and it passes the same automated tests, model evaluations and security checks as any other code. Which tools may see your code or data is agreed with you at the start.
Ready to deploy generative AI you can defend?
Tell us the workflow you want to accelerate. We'll return a grounded, guardrailed design with an evaluation plan and a human-in-the-loop approach.
contact@agnotic.com
Partnerships
contact@agnotic.com