Clear, owned outputs.
- Model benchmark report
- Prompt library and tests
- Fine-tuned models (where justified)
- Deployment and routing configuration
- Monitoring and evaluation dashboards
Large language models differ in quality, cost, speed, context length and how they handle data. Choosing and configuring the right model for each task makes a large difference to results. We benchmark models on your tasks, design prompts and pipelines, fine-tune where it helps and deploy with monitoring and cost control.
The problems Large Language Model Solutions solves.
Model selection, prompting, fine-tuning, private deployment and LLMOps.
One expensive model is used for every task.
Prompts are fragile and break with small changes.
Latency is too high for a good user experience.
Data requirements rule out some hosted models.
What Large Language Model Solutions covers.
Model benchmarking
Comparing models on your tasks for quality, cost and speed.
Prompt engineering
Structured, tested prompts with version control.
Fine-tuning
Adapting models to your style, format or domain where useful.
Private and open models
Deploying open-weight models in your environment.
Cost and latency
Routing, caching and smaller models for simpler tasks.
LLMOps
Evaluation pipelines, monitoring, logging and safe updates.
What we test before an LLM goes live.
LLMs fail in ways traditional software doesn't. Each release is checked against an evaluation set built from real tasks.
| What we check | Why it matters |
|---|---|
| Model choice | Quality, latency, cost per request and data-handling terms compared across providers and open models. |
| Accuracy | Answers scored against reference answers and reviewed by subject experts for the cases that matter most. |
| Hallucination | Whether the model states things that aren't supported by its sources, and how often. |
| Safety | Prompt injection, harmful output and leakage of confidential data, tested deliberately. |
| Cost and latency | Token usage, caching and model routing so the system stays affordable at real volumes. |
Why it's worth it.
Better results
The right model for each task.
Lower cost
Routing and optimization reduce spend.
Reliability
Tested prompts and monitoring prevent regressions.
When Large Language Model Solutions is the right move.
- Choosing a model for a customer-facing assistant.
- Reducing the cost of a high-volume LLM workload.
- Running an open-weight model privately.
Technologies & platforms
- OpenAI GPT models
- Anthropic Claude
- Google Gemini
- Llama
- Mistral
- vLLM
- Hugging Face
- LangSmith
- Azure OpenAI
How Large Language Model Solutions work runs.
- 01
Scope
The task, users, content sources, risks and how success will be judged.
- 02
Prototype
A working prototype on real examples, reviewed by the people who will use it.
- 03
Evaluate
Test sets for accuracy, safety and edge cases, with human review.
- 04
Harden
Security, permissions, guardrails, logging and cost controls.
- 05
Launch and improve
Phased rollout, feedback loops and regular evaluation.
Large Language Model Solutions questions.
Should we fine-tune a model?
Only when prompting and retrieval aren't enough — for example to enforce a format or style consistently. Fine-tuning adds cost and maintenance, so we test simpler options first.
Can we run an LLM on our own servers?
Yes, with open-weight models and suitable GPU infrastructure. We compare quality and total cost against hosted options.
How do you evaluate LLM quality?
With task-specific test sets, automated checks and human review, run before launch and whenever prompts or models change.
Talk to us about large language model solutions.
Tell us which decision or process you want to improve and what data you have. We'll reply with whether it's a good fit and how we would test it.