Multi-agent clinical-data platform
Turn clinical records into queryable, source-backed data.
Salutera uses specialist agents for NLP, Vision, Speech, Reasoning, and Provenance to read notes, PDFs, scans, pathology reports, imaging text, and transcripts. The platform structures every extraction into clinical ontologies and links each answer back to the exact source document, page, and line.
Validated across
// Clinical query
“Show me NSCLC patients with PD-L1 ≥ 50% on first-line immunotherapy in 2024.”
1,243 patients matched · 6 sites · 0.42s
// Specialist agents · in parallel
// Cohort extraction matrix
FHIR · OMOP · CSV
| Patient | TNM | Biomarker | Line | Last visit |
|---|---|---|---|---|
| P-1042 | T3N1M0 | ALK+ | 1L | 2024-07-30 |
| P-1043 | T1N0M0 | PD-L1 ≥50% | imm | 2024-09-04 |
1,243 records matched · showing 2 · every cell source-traceable
// Provenance · P-1043 · biomarker
Grounded“Tumor immunohistochemistry showed PD-L1 expression 62% (Tumor Proportion Score), consistent with high-expressor classification.”
oncology_consult_240904.pdf · pg 3 · line 27
The product in action
A patient's full clinical story.
Each event below was extracted from a different source document — biopsy report, pathology consult, oncology note, imaging report — and grounded against SNOMED-CT, LOINC, RxNorm. One row per encounter; one citation per claim; one audit entry per extraction.
- 2023-11DxNSCLC · biopsy-confirmed adenocarcinoma · stage T2N0M0
- 2023-12BiomarkerEGFR wild-type · ALK negative · PD-L1 TPS 62%
- 2024-01Tx-1LPembrolizumab monotherapy initiated · 200mg q3w
- 2024-04ResponsePartial response · target lesion -38%
- 2024-07Tx-contContinued pembrolizumab · no new toxicities
- 2024-09ImagingStable disease · CT chest + PET
- 12:04:18extractagent.nlpdischarge_summary_240821.pdf→ 27 variables · grounded
- 12:04:18extractagent.cvct_chest_240821.dcm→ 4 measurements · grounded
- 12:04:19fuseagent.mmencounter:240821-103→ 1 row · ontology-validated
- 12:04:21queryuser:scarter@…cohort:nsclc-pdl1-1L→ 1,243 matches · cited
Unified Patient Record
Outputs are instantly available as structured JSON arrays, FHIR resources, or tabular exports. Every variable is cryptographically tied to its source PDF coordinates.
The data problem
Information is trapped.
Locked in PDFs, notes, and scans.
Clinical data sits in PDFs, handwritten notes, faxed scans, and fragmented systems. Teams waste weeks manually searching, abstracting, validating, and rechecking the same records a third time.
Manual extraction fails
Keyword search misses context. Manual abstraction is painfully slow, inconsistent across abstractors, and nearly impossible to scale across large disease cohorts.
AI without citations is dangerous
Salutera reads what those records actually say — and cites the exact bounding box in the source PDF for every single claim it returns. Hallucinations are structurally impossible when every output must be grounded.
How it works
Structure. Search. Reason. Cite.
Eight pipeline stages. Four specialist extraction agents (NLP · Computer Vision · Speech · Multimodal). An extensible reasoning layer on top. Every cell, every claim, traces back to its source document.
Instantly available formats:
- ✓ Relational Tables & JSON
- ✓ FHIR R4 Resources
- ✓ OMOP CDM Mappings
Cohort comparison
Apples-to-apples across sites.
Eligibility screening
Trial criteria, per patient.
Signal detection
Adverse-event scanning.
Decision support
Cited answers per question.
Formulation & CMC
Pharma R&D precedent.
Custom agents
Bring your own rules.
08 Stages Clinical Pipeline
End-to-end parallel multi-agent processing, clinical ontology mapping, and de-identified mega-structure outputs designed for infinite scalability.
Data Anonymization
Local compliance de-identification (HIPAA/GDPR).
High-Perf Storage
Scalable cluster indexing raw multimodal formats.
Intelligent Routing
Dispatches content to optimal downstream agents.
Parallel Processing
NLP, CV, and Speech agents fuse all claims simultaneously.
Ontology Mapping
Forces terminology to match LOINC, SNOMED, RxNorm.
Vector Embeddings
Encodes semantic relationships for reasoning models.
Variable Extraction
Pulls exact patient properties with absolute traceability.
Mega-Structure Output
Generates typed tables, FHIR bundles, and knowledge graphs.
Semantic Clinical Query
Search across records. Returns cited patient cohorts, not document links.
Longitudinal Timeline
Aligns diagnoses, biomarkers, and visits sequentially per patient.
Registry Extraction
Pre-fills oncology (NCDB) and cardiovascular (STS) fields directly.
Traceable Auditing
Every cell includes a one-click provenance jump to the source offset.
Zero-Trust Local Engine
Strips PII locally, preserving 100% privacy constraints under HIPAA.
Standardized Ontologies
Grounds messy free-text into LOINC, SNOMED-CT, RxNorm schemas.
Powered by Salutera
Unified Clinical Data Platform
Connect vastly fragmented medical systems and unstructured data formats directly to our secure, high-precision AI reasoning core.
Clinical data mega-structures of specific institutions into AI-ready datasets
99% accurately mega-structures clinical data from vastly fragmented sources
Real-time AI decision support and scalable insights
Innovative Salutera Algorithms
Fully secured and easy to use for non-IT professionals
WEB and Hand-held apps
Multimodal AI/ML cross-talks medical variables of billions of data points for precision health – tailored to specific populations.
Our platform helps doctors identify the most appropriate approved treatments. It is fully secure and user-friendly.
What users can do
Ask clinical questions. Build cohorts.
Ask clinical questions
Semantic search across the corpus. Returns matching patients, not just documents.
Build precise cohorts
Inclusion + exclusion logic in clinician language with clear audit trails.
Extract variables
Pre-fill NCDB, STS, NSQIP registries directly from underlying records.
Create timelines
Diagnosis, biomarker, and treatment assembled into one longitudinal view.
Trace every claim
One-click jump to the source document, page, and exact passage.
Export structured data
Typed tables, FHIR bundles, OMOP CDM mappings for downstream analytics.
Validation & Extraction Accuracy
Evaluated under strict exact-match criteria. Pinned manifestations ensure clinical stability across mid-sized and government air-gapped deployments.
Colorectal Cancer Registry
Prostate Cancer
PSA, Gleason score, margins
COPD (Pulmonary)
98.89%Asthma Registry
98.12%Evidence & validation details
Methodology & Framework
Structuring across 16 diseases · 7 categories on a synthetic patient corpus modeled on CDC- and NIH-sourced statistics.
// Headline benchmark on synthetic cohort
0.00%
overall accuracy
<0s
retrieval time
0
variables
0k
files processed
16,000 synthetic patients
Synthea + Qwen-2.5 enrichment. Evaluated strictly under exact-match accuracy metrics across 16 diseases including oncology, respiratory, immunology, and neurology.
Deployment footprint
Runs on commodity infrastructure. Scales linearly with the cluster you give it.
// Grounded ontology domains & categories
Verticals
One clinical engine. Three regulated surfaces.
Security & deployment
Every claim should survive review.
Records stay in your perimeter
Deploy on-prem, in your VPC, or air-gapped. When Salutera runs in your environment, patient files never touch our infrastructure. Zero egress.
Tenant isolation
Per-customer compute, storage, and agents. No shared inference or cross-training.
BYO KMS + Encryption
AES-256 at rest, TLS 1.3 in transit. Customer-controlled KMS natively supported.
Audit Log Per Claim
Every extraction, query, and export is logged with operator, timestamp, and scope. Easily exportable to your enterprise SIEM.
Model Governance
Pinned model manifests and staged updates. Customer sign-off is strictly required for tenant-level model changes.
Pilot offer
Bring us your hardest clinical dataset.
Pick the dataset that's been blocking your team. We'll structure 1,000 records inside your perimeter — or ours — in seven business days. You decide if it's good enough.