Clinical AI Platform

The Intelligence Layer
for Clinical Data

SEQSTER AI transforms de-identified patient records into actionable research intelligence, from protocol ingestion to cohort analysis to patient-level deep dives.

See It In Action

Explore Capabilities

AI Capabilities

From Fragmented EHR to
Longitudinal Intelligence

SEQSTER Data Refinery assembles the complete patient record across every EHR, provider, and payer the patient has ever touched. SEQSTER.AI turns it into insight no one else can generate.

SEQSTER Data Refinery

AI-Powered Chart Abstraction

Unlike platforms that only read structured EHR fields, SEQSTER extracts from the full record: notes, pathology reports, imaging summaries, and discharge documents, regardless of source system or format. Unstructured and image-based clinical files are processed through an AI-powered extraction and de-identification pipeline, producing clean structured output ready for downstream analysis.

SEQSTER Data Refinery

Distilled Clinical Signal

The data refinery distills parsed, de-identified records into precision-organized clinical signal by domain: conditions, medications, labs, vitals, and more. A patient with years of fragmented records across multiple health systems becomes a single, clean longitudinal signal organized by domain. In minutes, not weeks. The AI layer reasons over concentrated clinical meaning, not raw markup noise. This is what separates SEQSTER from pass-through platforms.

SEQSTER Data Refinery

Semantic Search & Clinical Q&A

De-identified clinical signal is indexed to power natural language queries against a patient's actual medical records. Supports contextual reasoning and semantic search across clinical content, with PII suppression enforced at the architectural level as a defense-in-depth layer.

SEQSTER.AI

AI-Powered Cohort Builder

Researchers input study protocol or inclusion/exclusion criteria in natural language. The platform interprets that input and automatically generates the underlying cohort query, running it across the entire patient population. The output is not just a cohort. It's a pre-screened, ranked candidate list with per-patient inclusion rationale. Every qualifying patient comes with an auditable record of exactly which criteria triggered inclusion, giving sponsors and CROs a qualified list, not a database query.

SEQSTER.AI

AI-Powered Cohort Analysis

Designed to study and interrogate data at the population level. Researchers can analyze the clinical characteristics of a cohort or run side-by-side comparisons across multiple cohorts simultaneously. The platform surfaces population-level patterns, trends, and statistically relevant signals, then generates structured research insights and reports.

SEQSTER.AI

️

AI-Powered Patient Record Analysis

Designed for deep-dive interrogation of individual patient data. Researchers query a patient's full medical history in plain language, with the system answering exclusively from that patient's actual records. Supports eligibility screening, longitudinal lab trend analysis, and medication history queries, grounded and source-attributed.

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Real-World Evidence

Unified Patient Journey
Across Every Data Source

A single longitudinal view across EHR, claims, lab, pharmacy, and patient-reported outcomes — automatically harmonized, deduplicated, and time-sequenced for cohort discovery and outcomes research.

PT

Patient · SQ-4471-A

De-identified Cohort Member

Female · 58 · Type 2 Diabetes + CKD Stage 3 · Region: Northeast

5

Data Sources

847

Events

36mo

History

EHR / Clinical Encounters

Claims / Billing

Lab Results

Pharmacy Fills

Patient-Reported

Q1 ’23

Q2 ’23

Q3 ’23

Q4 ’23

Q1 ’24

Q2 ’24

Q3 ’24

Q4 ’24 · NOW

EHR Visits

12 encounters

Claims

214 records

Labs

428 results

Pharmacy

68 fills

Patient-Reported

24 surveys

Cohort Match

12,847 patients

Similar T2D + CKD profiles identified across 4M longitudinal records.

Treatment Response

−2.4% HbA1c

Mean reduction 12 months after GLP-1 + SGLT2 combination therapy initiation.

Adherence

93% PDC

Proportion of days covered across cohort — exceeds industry benchmark of 80%.

Platform Previews

See the Intelligence
in Action

Representative views of SEQSTER AI’s core surfaces.

AI Cohort Builder: Protocol Ingestion

Generated Criteria from Protocol

Age Range
40 – 75 years

Diagnosis
Type 2 Diabetes (E11)

HbA1c Lab Value
> 7.5% in last 12 mo

Medication
On Metformin ≥ 90 days

Exclusion
No renal failure (N18)

4,217

Qualifying Patients

Auditable inclusions

100%

Patient Record Analysis: Q&A

Was this patient on any ACE inhibitors in the 6 months prior to enrollment?

Yes. Patient records show Lisinopril 10mg prescribed by Dr. Chen at Mercy Health on March 4, 2023, active through September 2023, overlapping with the 6-month pre-enrollment window.

Source: Cerner EHR · Mercy Health · Retrieved Apr 2024

Any renal function labs in that same period?

Two eGFR results on file: 68 mL/min (May 2023) and 71 mL/min (August 2023). Both above the study exclusion threshold of 60 mL/min.

Source: Epic · St. Luke’s Hospital · LOINC 62238-1

Cohort Analysis: Population Insights

Comorbidity Distribution: Cohort A vs B

Hypertension68% · 54%

Hyperlipidemia52% · 47%

Obesity (BMI >30)44% · 61%

CKD Stage 2+18% · 22%

Depression (F32)29% · 31%

AI Insight: Cohort B shows significantly higher obesity prevalence (+17pp), suggesting differential cardiovascular risk profiles between arms.

AI Chart Abstraction: PDF Extraction

Source Document

↓ AI Extraction + De-identification

Structured Output

Dx: Type 2 Diabetes
Dx: Hypertension
Rx: Metformin 500mg
Rx: Lisinopril 10mg
HbA1c: 8.1%
eGFR: 71
Dx: Hyperlipidemia
Rx: Atorvastatin 40mg

PII removed · Source preserved · Ready for AI layer

Data Architecture

PHI-Free by Design.
Not by Policy.

Compliance and security teams across life sciences and healthcare ask the same question: can your AI ever touch raw patient data? At SEQSTER, the answer is structural, not procedural. Patient-specific encryption keys, isolated private subnets, and a de-identification pipeline that runs before any inference begins.

De-identified AI Pipeline

Source Records

→

Parse & Structure

→

De-identification

→

AI-Ready CSV

→

AI / LLM Layer

→

Research Insights

Encrypted PHI
(Private Subnet)

AI LAYER NEVER CROSSES THIS BOUNDARY

Patient-Specific
Encryption Keys

Design Principles

AI Built for Regulated Research

Every architectural decision reflects the demands of regulated clinical research.

PHI Never Reaches the AI Layer

Raw PHI remains isolated in encrypted private subnets with patient-specific encryption keys. The AI infrastructure exclusively consumes from the de-identified data path. By architecture, not by policy.

Grounded Answers Only

AI responses are grounded exclusively in the patient’s actual records. The system never infers from outside knowledge, never guesses, and always surfaces the source evidence behind every answer.

Every Decision is Auditable

From cohort inclusion logic to individual record queries, every AI-generated output is traceable. Sponsors and auditors can inspect individual-level inclusion decisions, a requirement no consumer AI platform is designed to meet.

FDA 21 CFR Part 11 Compliant

The AI layer operates within the same SOC 2 Type II and FDA 21 CFR Part 11 compliance envelope as the rest of the platform, the admission ticket for use as a system of record in regulated research.

Complete Records, Not Fragments

A patient’s health story doesn’t live in one EHR. SEQSTER follows the patient across every provider, payer, lab, and specialist they’ve visited, assembling the longitudinal depth that single-source platforms simply can’t match. Research built on fragments produces findings built on gaps.

Ready to Deploy Clinical AI
That Actually Meets the Bar?

Talk to the SEQSTER team about integrating AI-powered capabilities into your workflows.

SOC 2 Type II Certified · FDA 21 CFR Part 11 Compliant · Typical deployment in weeks, not quarters.

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The Intelligence Layer for Clinical Data

From Fragmented EHR to Longitudinal Intelligence

Unified Patient Journey Across Every Data Source

See the Intelligence in Action

PHI-Free by Design. Not by Policy.

AI Built for Regulated Research

Request a Demo

The Intelligence Layer
for Clinical Data

From Fragmented EHR to
Longitudinal Intelligence

Unified Patient Journey
Across Every Data Source

See the Intelligence
in Action

PHI-Free by Design.
Not by Policy.