LLM Implementation & Fine-Tuning

A language model trained on your domain

Generic language models don't understand your business terminology, product relationships, or customer context. Public AI services can't access proprietary data. Teams need AI that knows your domain without sending sensitive information outside your infrastructure.

LLM Implementation & Fine-Tuning deploys organisation-specific language models trained or fine-tuned on your data - product catalogues, customer interactions, internal documentation, decision histories. Models stay within your infrastructure, comply with data governance requirements, and improve as they're used.

For organisations ready to deploy AI capabilities that require deep business context and secure data handling.

What you get

A deployed language model that understands your business vocabulary and context, accessible to your teams through applications and interfaces. Our customers use it to power AI assistants, automate responses, and extract insights from internal knowledge.

Timeline:
6-12 weeks
Deliverable:
Deployed model within your infrastructure, usage guidelines, API documentation, monitoring setup, and team training

How it works

Data Preparation

Gather and structure training data, product information, customer interactions, documentation, decision examples.

Model Selection & Configuration

Choose appropriate base model, define fine-tuning approach, configure deployment infrastructure, establish governance.

Training & Fine-Tuning

Train or fine-tune model on your data, validate performance, iterate on quality, establish accuracy benchmarks.

Secure Deployment

Deploy within your cloud environment (AWS/Azure/GCP), configure access controls, integrate with data sources, establish monitoring.

Team Enablement

Train teams on appropriate use cases, usage guidelines, limitations, and how to provide feedback for improvement.

What's required

Access to training data and systems. Cloud infrastructure (AWS/Azure/GCP). IT and security stakeholder involvement. Clear use case definition and success criteria.

"The fine-tuned model understands our product catalogue better than our own documentation. Customer support resolution time dropped 60%."

VP Customer Experience, SaaS Company (Global)

Deploy your own LLM.

Let's discuss how a custom LLM can serve your organisation.

Explore LLM deployment

Common questions about LLM implementation

Common questions about LLM implementation

What does LLM implementation involve?

Choosing the right model for the workflow, connecting it to your data and systems, designing the prompts and controls, and surfacing it in tools your team actually uses.

Which models do you use?

Whichever suits the workflow, including commercial models, open models and ones you already have access to. We are not tied to one provider.

How do you avoid hallucination risks?

Workflows are designed with grounding in your own data, clear input boundaries, human review at sensitive points and measurable outputs you can check.

Can LLMs work with our existing systems?

Yes. They are connected to CRM, ERP, PIM, WMS, ecommerce platforms, reporting tools or spreadsheets depending on the workflow.

Where do LLMs add the most value first?

Tasks that involve drafting, summarising, searching, classifying or extracting information from messy sources, especially when those tasks repeat often.

How is human review built in?

Each workflow has clear review and approval points. People stay in control of anything that touches customers, suppliers, pricing or money. The LLM does the heavy lifting on the repetitive parts, not the final call where it matters.

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