A model that’s an expert, not a generalist.
We design, train, and deploy custom Small Language Models — and private SLM + RAG systems — that run on your own infrastructure. Smaller, faster, cheaper, and more accurate than a general LLM for the work that actually matters to your business.
10–100×
cheaper per query than a frontier LLM
On-prem
your data never leaves your infrastructure
Yours
you own the weights, data, and system
The basics
What is a Small Language Model?
A Small Language Model (SLM) is a compact AI model — typically a few hundred million to a few billion parameters — trained or fine-tuned for a specific domain or task rather than to know a little about everything.
Where a frontier LLM is a giant generalist that’s expensive to run and sends your data to someone else’s servers, an SLM is a focused specialist. Because it only needs to be brilliant at your work, it can be small enough to run on your own hardware — and still beat a general model on the tasks it was built for.
General LLM
Huge · costly · cloud-only · jack-of-all-trades
Custom SLM
Compact · cheap · runs on your infra · domain expert
Why a custom SLM
The benefits, plainly
Smaller isn’t a compromise — for the right job, it’s the advantage.
Dramatically cheaper to run
A specialised SLM is a fraction of the size of a general LLM, so each query costs a fraction as much. For high-volume workloads that gap compounds into enormous savings month after month.
Faster, lower latency
Fewer parameters mean quicker responses — often real-time. Your users get instant answers and your pipelines process far more throughput on the same hardware.
Your data stays private
The model runs on your own infrastructure. Sensitive documents and customer data never leave the building or get sent to a third-party API — critical for regulated industries.
Beats general models in your niche
Trained on your category and your data, an SLM consistently outperforms general-purpose LLMs on the specific tasks it was built for — because it’s an expert, not a generalist.
You own it
You keep the weights, the training data, and the system. No per-token bills, no vendor lock-in, no risk of an API being deprecated or repriced out from under you.
Runs anywhere — even offline
Small enough to deploy on-prem, at the edge, or on a single GPU. It keeps working without an internet connection and without depending on anyone else’s uptime.
SLM + RAG
Give your model your knowledge — without retraining
RAG — Retrieval-Augmented Generation — connects your SLM to a private library of your own documents. Instead of relying only on what it learned in training, the model retrieves the most relevant passages from your data at question time and answers grounded in them.
Ingest
Your documents, manuals, and records are loaded and chunked.
Embed & store
Each chunk is embedded into a private vector database you control.
Retrieve
At question time, the most relevant passages are pulled instantly.
Answer
Your SLM responds grounded in those sources — with citations, not guesses.
Where it shines
Use cases
Anywhere you have a focused task, sensitive data, or high volume, a custom SLM tends to win.
Customer support over your docs
An assistant that answers from your real product manuals, policies, and FAQs — accurately, instantly, and in your brand’s voice.
Internal knowledge assistant
Let your team ask plain-language questions and get grounded answers from your wiki, SOPs, and historical records — no more hunting through folders.
Document extraction & classification
Read, tag, and structure invoices, contracts, and forms at scale — turning piles of unstructured paperwork into clean, usable data.
Regulated-domain copilots
Legal, medical, and financial assistants that stay on-prem for compliance while delivering expert-level help grounded in your own approved sources.
On-device & edge AI
Embed intelligence directly into apps, devices, or kiosks that must work fast, offline, and without sending data to the cloud.
Domain-specific automation
Classification, routing, summarisation, and decisioning tuned to your category — cheaper and more accurate than wiring up a giant general model.
How we build it
From your data to a model you own
Scope
We define the task, success metrics, and whether an SLM is genuinely the right tool.
Data
We curate, clean, and label your domain data — the real source of an SLM’s edge.
Train
We fine-tune and instruction-tune the model for your category and tasks.
Evaluate
We test against real-world cases with an evaluation suite you can trust.
Deploy
We ship it to your infrastructure, document it, and train your team to own it.
Could an SLM work for you?
Tell us about your data and the task you want to automate. We’ll give you an honest answer on whether a custom SLM is the right move — and what it would take.