Healthcare · LLM Reasoning at Scale

Clinical signals exist
in the data no one read.

Logswiz applies LLM reasoning to 100% of your clinical data streams, patient events, operational signals, and compliance records, at the reliability and scale healthcare workflows demand.

No credit card required · Up and running in minutes

CLINICAL RESEARCH · LLM REASONING · COHORT ANALYSIS GENOMIC STREAM ATGCAGTCAGTCATGCAGTCAGTCAGTCAGTAGCATGC PATIENT COHORT · 48,200 RECORDS · FEATURE SPACE biomarker-A response Responders Partial resp. Non-responders TRIAL DATA Records 48,200 Papers 12,440 Biomarkers 847 Coverage 100% Signal Found Conf 0.94 Sampled 0% LLM REASONING OUTPUT Biomarker correlation identified · BRCA2 × IL-6 pathway Present in 94% of Responder cluster · absent in Non-responders Signal buried in long-tail records · invisible when sampled <30% Cross-referenced 847 biomarkers across 48,200 records · conf 0.94 Structured output delivered to your research pipeline LLM REASONING · CLINICAL RESEARCH DATA
The Problem

Healthcare data without reasoning
is a missed opportunity, at scale.

Clinical environments generate vast volumes of structured and unstructured data events every day. Reasoning over all of it was never financially viable, so teams prioritize, filter, and miss the signals that matter most.

80%

Of clinical data never analyzed

Documentation, monitoring events, and operational signals that flow past without LLM reasoning applied. The insight exists. It's just unread.

High

Compliance audit exposure

Regulatory requirements demand full audit trails. Sampled data creates gaps that become liability in investigations.

100%

Logswiz event coverage

Every clinical data event reasoned over at the reliability and consistency healthcare requires.

Case Studies

Real results. Real organisations.

What becomes possible when LLM reasoning runs over 100% of the data.

Pharmaceutical Research
4 years
Duration the signal had been present, undetected

Identifying a trial-altering biomarker signal missed across 4 years of sampled analysis

A global pharma research organization identified a statistically significant biomarker correlation in an oncology trial dataset — a signal that had been present...

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Academic Research
80% → 100%
Historical patient data now under active analysis

Accelerating rare disease research by reasoning over 12 years of unanalyzed records

A research consortium applied LLM reasoning to 12 years of accumulated patient records that had never been fully analyzed — surfacing three novel disease associ...

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Public Health Research
3 weeks
Earlier detection of disease emergence signals

Identifying pandemic early-warning signals 3 weeks earlier through full EHR event coverage

A national health research institute demonstrated that full EHR event coverage with LLM reasoning could identify population-level disease emergence signals 3 we...

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How It Works

LLM reasoning applied to every clinical event, with the reliability healthcare demands.

01

Connect your data sources

Ingest from EHR systems, monitoring equipment, operational platforms, and any HL7-compatible source.

02

Reason over every event

LLM intelligence analyzes each clinical signal, identifies patterns, and surfaces intelligence with auditable reasoning traces.

03

Deliver structured intelligence

Actionable insights with full provenance delivered to clinical dashboards, compliance systems, or operational tools.

04

Integrate with your stack

Connect to your EHR, analytics platform, or compliance tooling. HIPAA-compliant architecture by design.

HEALTHCARE.INFERENCE.COST
// SAME CLINICAL DATA VOLUME.
// SAME LLM REASONING. DIFFERENT COST.
Standard LLM inference

1000× y

Cost to reason over x volume of data

Logswiz

y

Same x volume. Same reasoning. Fraction of the cost.

Inference cost ratio

1000×

less to reason over the same data

Which means

Full coverage
becomes viable

// SAME MODEL. SAME OUTPUT. 1000× THE ROI.

The ROI

Full clinical intelligence
is now
financially viable.

The value Logswiz delivers comes from two compounding factors: the volume of clinical events it reasons over that were previously unanalyzed, and the reliability of the output that makes LLM inference trustworthy in a healthcare context.

Together they produce a return on investment that reframes the question entirely, not "can we afford to do this?" but "what has it been costing us not to?"

Get Started Free →

Stop sampling.
Start knowing.

See what 100% clinical data coverage looks like for your environment, with a model built for the reliability healthcare demands.