Skip to content
LavX Managed Systems
HomePlatformSolutions
Case studiesResearchTrust Center
AboutWorkshopContact
EN | HUGet in touch

We use cookies to keep this site working, remember your language, and (if you opt in) measure how it's used. You decide.

LavX Managed Systems

EU-resident AI engineering for European business. RAG, LLMOps, agents, custom software. Model-agnostic, no vendor lock-in.

Budapest · EU · EU-resident AI engineering

Product

PlatformSolutions

Proof

Case studiesResearchTrust Center

Company

AboutWorkshopContact
© 2026 LavX Managed Systems · Budapest · EU
Privacy policyImprintBrandEN | HU
ResearchWith a source and a year
Research

Not a testimonial.Published research,what we build on.

We do not show invented customer numbers. Here is the published, peer-reviewed research and analyst data our approach is built on: with a source, a year, and an honest caveat. Our own live results we show after an NDA, with the client's consent.

See the research ↓What we deliver →
Research

What our approach is built on

Published, peer-reviewed research and analyst data. Each one with a source, a year, and the caveats. Our own live numbers we show after an NDA, with the client's consent.

Live results
28.6%peer-reviewed

That is how much the median resolution time of cases dropped at a real support team once a RAG assistant built on past tickets and reinforced with a knowledge graph stood behind it.

LinkedIn · SIGIR 2024arXiv 2404.17723 →

The authors report on their own internal tool, the improvement is measured against their earlier RAG baseline, not an independent benchmark.

90%vendor

That is how many fewer unfounded (hallucinated) answers a RAG assistant tied to the knowledge base and paired with a two-step check gave, and 99% fewer serious compliance errors.

DoorDash Engineering · 2024Engineering blog →

The vendor's own, unaudited measurement. The baseline and the exact methodology are not public.

Accuracy and grounding
+7.5ptslaboratory

That is how many percentage points more accurate the hits were in an open question-answer test once a structured knowledge graph was fitted to the classic RAG pipeline.

Scientific Reports, Nature Portfolio · 2025nature.com →

A laboratory benchmark, not a live support result. Percentage points, not a relative improvement.

Market context
60%analyst

According to customer reports, this share of agents does not recommend the self-service options: the tool alone is not enough, the rollout needs a process and training alongside it.

Gartner · 2025Gartner survey →

Perception-based data from a survey of 5,801 customers (January, February 2025).

80%forecast

Analyst forecasts say that by 2029 agentic AI may resolve this share of customer service cases on its own, with a 30% cost reduction.

Gartner · 2025Gartner forecast →

A forecast, not a measured result. To be treated as a guideline figure, not today's performance.

Why do we show research, and not customer stories?

Because an invented or unverifiable customer number is worse than nothing. The figures above come from public, citable sources, together with their caveats. LMS is happy to show its own live results, but after an NDA and with the client's consent, not on the website.

Let's talk

Want an estimate tailored to your own system?

The research above is the starting point, not the promise. The realistic estimate tailored to your data and process we give on a call, with an engineer, not a salesperson.

Request a callbackSee the proof →