Cordiant AI · End-to-end AI development for healthcare FDA · NIST AI RMF · ISO 13485 aligned
Cordiant AI · Healthcare End-to-end AI development for healthcare
How we work Frame · Architect · Train · Deliver

We architect and train healthcare AI
with the evidence trail built in.

Most healthcare AI gets delivered twice: once when the model works, once when the documentation gets reconstructed for review. Cordiant develops the AI model end-to-end: architecture, training, validation. The regulatory evidence trail is produced as we work, so the technical file is ready when the model is.

Request a briefing → How we work
An abstract architectural composition of interconnected glass and amber cubes, representing the foundational structure of clinical AI development
How an engagement moves

From clinical problem to cleared submission.

Every model we deliver passes through the same four foundations, in order. The work is done by one team, end-to-end. Skipping a step is the most common reason healthcare AI fails review.

01
Data integration
→
02
Model training
→
03
Semantic layer
→
04
FDA submission
1,016FDA-authorized AI/ML-enabled devices to date
+38%YoY growth in SaMD submissions
PCCPfinal guidance issued · December 2024
NIST AI RMFframework alignment · standard scope
ISO 13485QMS aligned · all engagements
1,016FDA-authorized AI/ML-enabled devices to date
+38%YoY growth in SaMD submissions
PCCPfinal guidance issued · December 2024
NIST AI RMFframework alignment · standard scope
ISO 13485QMS aligned · all engagements

Where healthcare AI breaks down.

Healthcare AI rarely fails because of the model. It fails because of what surrounds the model. The fix isn't a sequence of vendors handing off to each other. It's an integrated build that closes all three failure modes at once.

FAILURE MODE

The data is broken before training begins.

EHRs are fragmented, terminology is inconsistent, lineage is missing. A model trained on messy data inherits every defect.

FAILURE MODE

The model has no clinical grounding.

Without verified medical knowledge behind it, even a high-performing model hallucinates, misclassifies, and fails explainability under review.

FAILURE MODE

The regulatory evidence is incomplete.

Strong technical performance still fails FDA review without dataset lineage, fairness analysis, change control plans, and complete SaMD documentation.

Who we build for.

Cordiant develops AI for healthcare organizations whose products have to clear clinical, regulatory, and real-world scrutiny. We are not generalists, and we are not advisors. We build the model.

Segment

Medical device manufacturers

Class II and III device makers adding AI/ML features that require 510(k), De Novo, or PCCP pathways.

Segment

Digital health companies

SaMD developers building diagnostic, triage, or clinical decision support products.

Segment

Remote patient monitoring

RPM and digital therapeutics platforms scaling AI-driven alerting, escalation, and care pathways.

Segment

Pharma R&D and diagnostics

Drug discovery, clinical trial AI, and companion diagnostics teams needing clinically validated models.

Segment

Health systems & payers

Provider and payer organizations deploying internal AI on clinical, claims, and population health data.

Why healthcare teams choose Cordiant.

Healthcare AI is not a generic software problem. The team building your model has to know the standards, terminologies, and regulatory frameworks the industry actually runs on. Ours does.

01

Healthcare data standards fluency

Working knowledge of HL7 FHIR, SNOMED CT, LOINC, RxNorm, ICD-10/11, and the messy reality of EHR data ingestion across Epic, Cerner, and legacy systems.

02

Regulatory workflows, not regulatory theory

510(k), De Novo, PCCP, and SaMD documentation pathways, with hands-on experience structuring the evidence FDA reviewers actually look for.

03

Clinical data governance

Dataset lineage, provenance, consent management, PHI handling, and the documentation chains required under HIPAA and 21 CFR Part 11.

04

Bias auditing and model validation

Subgroup performance analysis, fairness reporting, and the validation evidence that reviewers and clinical partners require before deployment.

05

Healthcare interoperability expertise

FHIR API design, USCDI alignment, and integration patterns that survive contact with real-world clinical infrastructure.

06

Built by healthcare and regulated-systems leaders

Our team has shipped AI in regulated environments, sat on the submission side of FDA conversations, and built data infrastructure inside healthcare organizations.

Selected engagements.

Three engagements that show what end-to-end AI development with a built-in evidence trail looks like in practice. Client names and identifying details have been removed for confidentiality.

Engagement · Medical device

AI-enabled RPM device, delivered submission-ready

A remote patient monitoring company needed an AI triage capability and a 510(k) submission to support it. We architected the model against the regulatory pathway, trained it with dataset lineage and subgroup performance produced automatically, and delivered the trained model alongside a complete evidence package: predicate strategy, subgroup analysis, and a PCCP algorithm change protocol.

Outcome: 510(k) submission accepted on first review cycle
Engagement · Digital health

A diagnostic model that behaves consistently across customers

A digital health company needed a diagnostic AI that would hold its performance across Epic, Cerner, and two legacy systems. We architected the model with a semantic layer at the data ingest, mapped local clinical terms to SNOMED CT and LOINC, and trained the model on a feature set that survives the trip from any source EHR.

Outcome: model performance recovered to development levels at all sites
Engagement · Pharma R&D

Clinical trial enrichment model, validation done as we built it

A pharma analytics team needed a trial enrichment model whose evidence would satisfy both internal governance and external auditors. We architected the training pipeline so dataset cards, subgroup bias analysis, and version-controlled training artifacts were produced by the pipeline itself, aligned to NIST AI RMF, rather than assembled after the fact.

Outcome: internal model validation timeline reduced by ~40%
/ai-model-training

We train clinical AI with the evidence trail produced as we build it.

CAPABILITY End-to-end model development
VALIDATION Fairness + HITL, continuous
OUTPUT Trained model + technical file
  1. Cordiant develops the model. Architecture, training, and validation, by a team that does only healthcare AI. The buyer doesn't need to have an ML team, and the strongest buyers often don't.
  2. Dataset lineage, subgroup performance, model cards, and algorithm change protocols are produced automatically as the pipeline executes. The technical file grows alongside the model, not after it.
  3. Every prediction traces back to the exact data and decisions that produced it. Reproducible, auditable, and defensible at review by construction, not by retrofitting.
Request a model development briefing →
/data-integration-migrations

We build the data foundation the model trains on. Audit-ready by construction.

SOURCES EHR · Claims · Labs · Imaging
STANDARDS SNOMED · LOINC · FHIR
OUTPUT Clinical-grade dataset, with lineage
  1. A model is only as defensible as the data behind it. Most healthcare data is incomplete, inconsistent, and poorly structured. Fixing that is the first thing we do, not the last.
  2. We unify EHR, claims, lab, and imaging data into clinical-grade training datasets, with terminology mapping across SNOMED, LOINC, and FHIR.
  3. Audit-ready lineage is captured at every transformation, at ingest, so by the time training begins, the documentation a reviewer or auditor will ask for already exists.
Request a data foundation briefing →
/fda-regulatory-submissions

The technical file is ready when the model is.

PATHWAYS De Novo · 510(k) · SaMD
FRAMEWORKS FDA · NIST AI RMF · ISO 13485
SCOPE Submission-ready deliverable
  1. Because we architect against the pathway from day one, the technical file is generated alongside the model, not assembled in a separate project six months before submission.
  2. Dataset lineage, clinical evidence strategy, predicate analysis, and PCCP-ready algorithm change protocols are produced by the build itself, ready for your regulatory lead to take to FDA.
  3. Aligned to FDA's evolving SaMD framework, the NIST AI Risk Management Framework, and ISO 13485 quality management. These are the three pillars reviewers expect.
Discuss your submission pathway →
/semantic-layer-creation

Models grounded in clinical knowledge, defensible line by line.

GROUNDING Verified medical knowledge
OUTCOME Explainable outputs
BENEFIT Defensible at review
  1. AI hallucinates when it lacks clinical context. The model knows the words but not the medicine behind them.
  2. Every model we build sits on a semantic layer linking outputs to verified medical knowledge, relationships, and clinical guidelines. Probabilistic output becomes traceable reasoning.
  3. The result: fewer errors, stronger explainability, and a model whose outputs can be defended line-by-line in regulatory review.
Discuss your grounding approach →

The technical file is the product.

A model that performs in a notebook is worth very little. A model with the dataset lineage, validation evidence, and risk documentation that gets it cleared is worth multiples. Every Cordiant engagement is engineered backward from the submission package. The documentation grows alongside the model, not after it.

"We don't deliver a model, then write the documentation. We design the documentation, then build the model that satisfies it."
01 · Frame

Regulatory framing

Intended use, classification pathway, predicate strategy. Settled before a line of code is written. The classification decision drives everything downstream.

02 · Architect

Evidence-driven design

Architect the dataset, semantic layer, training plan, and validation plan against the specific evidence requirements of the chosen pathway. The risk file opens on day one.

03 · Train

Build with audit-grade provenance

Train the model with every artifact produced under version control: datasets, runs, evaluations, code releases. Evidence accrues continuously, by the pipeline, not by a person.

04 · Deliver

Hand over the model and the file

The trained, validated model is delivered with the technical file already attached: predicate analysis, dataset lineage, subgroup performance, PCCP. Your regulatory lead takes it from there.

Built to the standards regulators use.

Healthcare AI lives or dies by the frameworks it can withstand. Every Cordiant engagement is engineered to map cleanly to the standards reviewers, auditors, and procurement teams already work with.

Frameworks & alignment
FDA SaMD NIST AI RMF ISO 13485 ISO 14971 IEC 62304 HIPAA · BAA-ready GDPR aligned

Start with a confidential briefing.

Bring us a clinical problem you want AI to solve, a pathway you need to clear, or a model you'd rather have built right the first time than rebuilt later. We respond within 48 hours with a scoped point of view and an honest read on fit.

hello@cordiantai.com

All inquiries treated as confidential.
NDA available on request.

Briefing request