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.
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.
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.
The authors report on their own internal tool, the improvement is measured against their earlier RAG baseline, not an independent benchmark.
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.
The vendor's own, unaudited measurement. The baseline and the exact methodology are not public.
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.
A laboratory benchmark, not a live support result. Percentage points, not a relative improvement.
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.
Perception-based data from a survey of 5,801 customers (January, February 2025).
Analyst forecasts say that by 2029 agentic AI may resolve this share of customer service cases on its own, with a 30% cost reduction.
A forecast, not a measured result. To be treated as a guideline figure, not today's performance.
This is how far per-token cost can diverge on the same H100 GPU, driven purely by utilization. Effective cost ranged from $0.21 to $15.25 per million output tokens: a 2.5–24× penalty at 1–10 req/s, up to 36.3× near idle. Self-hosting is cheap only at high utilization.
Single-author preprint, not peer-reviewed; vLLM-specific measurement on H100/A100 hardware.
In this share of model-pair comparisons, the model with the lower listed price cost more in practice, because reasoning ("thinking") models burn hidden, variable token volumes. The reversal can reach 28×.
Preprint; measured on commercial reasoning APIs. The named example models are illustrative.
This much of the strongest model's accuracy was retained by a "cheap model first, escalate only when needed" router, while most traffic was served by smaller, even self-hosted models.
Preprint; retention measured on the authors' own test sets, not an independent benchmark.
Serving throughput can collapse by this much when GPU memory is oversubscribed: the system falls into an eviction–restart cycle. A right-sized fleet avoids it.
A theoretical worst-case bound under homogeneous, saturated load; not a production benchmark.
A storage layer enforcing verifiable GDPR compliance reached this share of native throughput on untrusted, even non-EU cloud. Compliance rests on controls and attestation, not the server's physical location.
Preprint; measures GDPR compliance for a key-value store, not LLM inference. The confidential VM accounts for 28–32% of the slowdown.
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.
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.