PRIVATE AI + MODEL ADAPTATION
Models that work in your world.
A collection of language-model projects designed around domain context and deployment constraints.
The challenge.
An off-the-shelf model does not automatically know an organization’s documents, tone, or engineering environment. Each use case also carries different requirements for quality, speed, and data control.
What we built.
For Merlin AI in 2023, Telenext prepared a branding-document corpus and used LoRA to tune Llama for an agency’s knowledge and voice. Separate engagements included a LangChain and Gemma document-search pipeline and Devstral quantization and tuning for local agentic coding.
What the work demonstrates.
The branding assistant is described in the supplied material as being used for first-level client queries. The retrieval and local coding work demonstrate distinct approaches to adaptation; they are not presented as a single off-the-shelf product.
Based on Telenext’s company profile and project materials supplied for this website. No independent performance benchmark is implied.
“The best model is the one that fits the task, the data, and the operating boundary.”THE ENGINEERING PERSPECTIVE