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AI Research · Small Language Models Domain-tuned models, at a fraction of the cost.

Customised small language models (SLMs) that match — or surpass — much larger general-purpose models on your task, while cutting inference cost, latency and data-exposure risk.

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8 weeks
Model optimisation engagement
RLHF · DPO · GRPO
Alignment methods

Why customise SLMs

General-purpose models miss what specialised industries demand.

Healthcare, law and finance need more than general knowledge. A small language model fine-tuned on domain-specific data can deliver it.

  1. 01Large language models (LLMs) are trained on broad, general-purpose data.
  2. 02They often lack the domain knowledge, tone and reasoning patterns that specialised industries require.
  3. 03Fine-tuned SLMs can match or surpass much larger models on the task, at a fraction of the inference cost.
  4. 04Customising SLMs balances performance with cost efficiency, latency requirements and data-privacy concerns.

What we do

Deep expertise in training and serving.

We work across both the training and the serving sides of model customisation.

Post-training customisation

Adapt the model to your domain, tone and task.

Instruction tuningRLHF · DPO · GRPO alignmentTask-specific fine-tuning

Inference optimisation

Serve the customised model faster and at lower cost.

QuantisationSpeculative decodingDistillationModel parallelismKernel optimisation

How we deliver

Model optimisation in 8 weeks.

We run model optimisation engagements over 8 weeks, in the following steps.

  1. / 01

    Discovery and scoping

    Define business objectives, success metrics and performance benchmarks.

  2. / 02

    Data preparation

    Curate, clean and label domain-specific data.

  3. / 03

    Model selection and baselining

    Establish baseline performance metrics before customisation begins.

  4. / 04

    Iterative training and optimisation

    Run training and optimisation cycles, measuring against the benchmarks at each iteration.

  5. / 05

    Evaluation and SME review

    Metric-based evaluation, followed by review from subject-matter experts (SMEs).

Where it fits

The research and customisation tier of our engagement model.

Our Agent Evaluations practice evaluates customised models, Responsible AI controls govern them, and the GenAI Delivery Factory ships them to production. Pair them with MODELSTACK to route each query to your customised model at the right cost.

Related guides

Keep reading.

All guides

AI Engineering · 8 min

SLM vs LLM — When Small Wins.

Smaller models. Lower cost. On-premise feasibility. The cases where small language models match or beat frontier LLMs in production.

Read the guide

AI Engineering · 6 min

RAG vs Fine-Tuning

When to use retrieval augmented generation versus fine-tuning for enterprise LLM applications.

Read the guide

AI Engineering · 7 min

GenAI Cost Optimisation.

The levers that actually move enterprise GenAI spend — model routing, context discipline, caching, agent budget caps, and FinOps for AI.

Read the guide

Next step

We are an intent away.

Bring the task you are overpaying a general-purpose model to do. We’ll scope an 8-week engagement — objectives, benchmarks, data and baseline — before training begins.

Talk to an engineerBook a free demo