Large language model expertise
Practical AI solutions that use large language models responsibly to automate workflows and enhance products.
We help you evaluate providers, prepare data, engineer prompts and deploy secure, governed AI services.
What are large language models
Large language models use deep learning to generate and reason over natural language, enabling new product experiences.
They can summarise documents, power chat experiences, support search, automate operations and assist staff.
Our approach covers data privacy, evaluation and human oversight to ensure LLM products deliver measurable value.
Identify high impact opportunities, assess ROI and create delivery roadmaps.
Pipelines for cleansing, grounding and monitoring the data that powers your models.
Automated and human-in-the-loop assessment of accuracy, safety and bias.
Secure infrastructure, observability and governance for production LLM services.
Why LLMs matter
- Unlock new automation and personalisation opportunities
- Reduce support costs with intelligent assistants
- Enhance internal productivity with knowledge retrieval
- Deliver competitive differentiation with tailored AI experiences
LLM projects we deliver
From discovery to production, we support the full lifecycle of large language model solutions.
Customer and employee chat experiences with guardrails and analytics.
Summarisation, extraction and workflow automation across large document sets.
Semantic search over knowledge bases, policies and support material.
Marketing, product and support content generation with brand controls.
LLM powered agents that orchestrate tools and APIs to complete tasks.
Continuous benchmarking, red teaming and feedback loops for AI quality.
When to invest in LLMs
- You have clear workflows or journeys that benefit from automation
- Your data estate can provide trusted context for AI responses
- You need to enhance existing products with conversational interfaces
- You are ready to establish governance and monitoring for AI systems
When to explore alternatives
- Rule based automation may suffice for deterministic processes.
- Traditional machine learning might handle structured prediction better.
- Manual interventions could be safer until data governance matures.
- Third party SaaS tools may offer faster wins before custom AI builds.
Hosted APIs vs custom models
| Criterion | Hosted APIManaged | Custom modelTailored |
|---|---|---|
| Speed | Rapid to launch using provider infrastructure | Longer lead time for training and deployment |
| Control | Limited control over model internals | Full control over training data and behaviour |
| Cost | Usage based pricing | Higher upfront training and hosting costs |
| Data sensitivity | Requires sharing data with provider | Keeps data in your own environment |
| Differentiation | Good for common use cases | Enables bespoke capabilities |
We help you choose between managed APIs and custom models based on compliance, speed and differentiation goals.
Deliver LLM powered products
We provide strategy, design, engineering and MLOps support to launch responsible AI experiences.
No obligation. We protect sensitive information and remove it whenever requested.