Your Industry · LLM Reasoning at Scale

High-volume events.
Unread intelligence.

If your operation generates high-volume data streams where LLM reasoning at scale would create measurable value, Logswiz was built for you.

No credit card required · Up and running in minutes

INFERENCE COST vs TOKEN USAGE GROWTH COST / SPEND TOKEN USAGE VOLUME → $0 10× 100× 1000× 10000× 1B 10B 100B 1T 10T+ the elbow 1000× Standard LLM Logswiz AI usage Standard LLM cost AI usage (tokens) Logswiz cost (1000× lower) cost eliminated Tokens grow. Standard cost explodes. Logswiz cost stays 1000× lower — the gap is your ROI.
The Pattern

Every industry has the same
unread data problem.

Wherever there are high-volume digital events, there is intelligence flowing past unexamined. The constraint has never been the data or the AI, it has been the economics of reasoning over all of it.

1000×

ROI over standard LLM inference

Logswiz makes it economically viable to apply LLM reasoning to 100% of your event stream, regardless of volume.

100%

Event coverage guarantee

No sampling policy. Every event reasoned over. The signal you need is never in a pile we skipped.

<1ms

Per-event reasoning latency

Real-time intelligence that keeps pace with your data stream. Not a batch job that tells you what happened yesterday.

Case Studies

Real results. Real organisations.

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

Logistics & Supply Chain
41%
Reduction in shipment exceptions

Reducing shipment exceptions by 41% through full event stream reasoning

A global logistics network applied LLM reasoning to 100% of shipment events — reducing exceptions, delays, and customer escalations by 41% by surfacing predicti...

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Energy & Utilities
6 hours
Earlier detection of instability signatures

Predicting grid anomalies 6 hours earlier through full sensor event reasoning

A multinational energy company began detecting grid instability signals 6 hours earlier than their existing monitoring system — by applying LLM reasoning to 100...

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Retail
$28M
Annual shrinkage reduction

Recovering $28M in annual shrinkage through full transaction event reasoning

A global retail chain reduced inventory shrinkage by $28M annually by applying LLM reasoning to 100% of transaction events — identifying organized retail crime ...

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How It Works

The same reasoning engine, applied to your domain.

01

Tell us your stream

We start by understanding your event volume, data sources, and what intelligence you need to extract from them.

02

Connect your sources

Kafka, Kinesis, S3, webhooks, or any standard connector. 100% of events ingested, nothing dropped.

03

LLM reasoning at scale

Logswiz applies LLM intelligence to every event, surfacing the patterns, signals, and anomalies your domain demands.

04

Intelligence to your stack

Enriched, structured output delivered to your dashboards, data warehouse, or operational tools in real time.

YOUR.INDUSTRY.INFERENCE.COST
// SAME EVENT VOLUME. ANY DOMAIN.
// 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-coverage LLM reasoning
is now
financially obvious.

The value Logswiz delivers comes from two compounding factors: the volume of events it reasons over that were previously invisible, and the reliability of the output that makes LLM inference genuinely trustworthy at scale.

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 →

High-volume events?
Let's talk.

Tell us about your data stream and we'll show you what LLM reasoning at 1000× ROI looks like for your specific domain.