LLM development, integration and fine tuning
The right model, adapted only as far as it needs to be, and measured.
LLM integration, fine tuning and private model deployment, chosen by measured quality, cost and data privacy rather than by vendor.
- Routing: easy → small model
- Fallback provider on errors
- Long contracts stay on large model
- Category
- AI & Automation
- Deliverables
- 04 core
- Core stack
- Large Language Models (LLMs) · Fine tuning · LoRA / QLoRA
- Typical first phase
- Benchmark
LLM Integration, Fine Tuning & Private Model Deployment
We integrate large language models into your products and workflows, and adapt them where prompting is not enough. That covers model selection by benchmark on your task, fine tuning with LoRA and supervised data when it pays off, private deployment of open weight models, and the evals, routing and cost controls that keep an LLM feature dependable after launch.
- A feature works on one provider's model and nobody knows if a cheaper one would do.
- Prompts have grown into pages of rules and still miss the format half the time.
- Your data can't leave your environment, so hosted APIs are off the table.
- A model choice backed by benchmarks on your own task, not a leaderboard.
- Fine tuning only where evals show prompting and retrieval fall short.
- LLM features that run on infrastructure and terms your security team accepts.
Model selection & benchmarking
Frontier APIs and open weight models compared on your real examples for quality, latency, cost and data handling.
- 1Task specific benchmark set
- 2Cost & latency profiling
- 3Data residency review
LLM integration & orchestration
LLMs wired into your product or workflow behind a provider agnostic layer, so models can change without a rewrite.
- 1Structured outputs & tool calling
- 2Model routing & fallbacks
- 3Caching & cost controls
Fine tuning (SFT, LoRA)
Supervised fine tuning and parameter efficient methods such as LoRA, when a narrow task or format justifies it.
- 1Training data curation
- 2SFT & LoRA / QLoRA runs
- 3Before / after evals
Private & open weight deployment
Open weight models served in your cloud or on dedicated hardware, with monitoring, for data that must stay private.
- 1Self hosted inference
- 2Quantisation & serving
- 3Access control & logging
AI is in the room from day one.
We don't bolt AI on at the end of a project. It's part of how we research, design, build, test and document every engagement, which is how a small senior team moves quickly without cutting corners. A person reviews everything the models produce.
Candidate models are benchmarked on your own examples before anything is fine tuned, and every model or prompt change is scored against the same evaluation set.
- A person signs off. No model output reaches your users or your codebase without review.
- Your data stays yours. Zero-retention API settings or private models when your data requires it.
- Model-agnostic. We pick models by measured quality, cost and privacy, not by vendor.
Discover
Requirements mapped with LLMsWith your permission, briefs, tickets, call notes and analytics exports go into a private workspace. LLMs map requirements, edge cases and open questions, so nothing said in a workshop gets lost. People run the workshops; the model keeps the record.
Prototype
Something real to react to, earlyFirst-pass screens, flows and copy variants are generated from the spec, then shaped by our designers. Decisions happen on a clickable prototype instead of a document, and the final design system stays human-made.
Build
AI pair programming, human reviewEngineers work with AI coding assistants for scaffolding, tests and refactors. Every change is read by a person and scanned by Caudilysis, our own static analysis platform, before it merges.
Verify
Evals and generated tests in CIAI features get an evaluation set built from real examples, and every model or prompt change is scored against it. Everything else gets generated test cases that an engineer reviews and keeps in the pipeline.
Ship and learn
Docs, runbooks and monitoringDocumentation, runbooks and release notes are drafted with AI and checked by the team. After launch, monitoring and usage data feed straight into the next iteration.
How a LLM development engagement runs.
The indicative shape of a first engagement. Phases overlap on purpose, so we start building while discovery is still sharpening the plan.
- 01BenchmarkCandidate models scored on your task.
- 02IntegrateProvider agnostic layer, outputs, tools.
- 03AdaptPrompting, retrieval, then fine tuning if needed.
- 04Deploy & operateHosting, evals in CI, cost monitoring.
This is indicative only. Every engagement is scoped with you before it starts.
Is LLM development right for you?
- 01
You need to choose between models and want evidence, not opinion.
- 02
A narrow, repeated task needs consistent output that prompting can't hold.
- 03
Data privacy or cost means you want an open weight model you control.
Not sure it fits? A free assessment will tell you
# Service manifest service: LLM Development & Fine Tuning category: AI & Automation deliverables: • Model selection & benchmarking • LLM integration & orchestration • Fine tuning (SFT, LoRA) • Private & open weight deployment stack: • Large Language Models (LLMs) • Fine tuning • LoRA / QLoRA • Open weight models • Model routing • LLM evaluation phases: 4 next_step: free assessment
LLM development questions, answered.
What do LLM development services include?
Choosing a model by benchmarking it on your task, integrating it into your product or workflow, adapting it with prompting, retrieval or fine tuning, deploying it (hosted API or a private open weight model) and running it with evals, monitoring and cost controls.
When is fine tuning worth it?
When a model must follow a narrow format or behaviour reliably and good prompts plus retrieval still fall short on your evaluation set, or when a smaller fine tuned model can replace a larger, costlier one for a high volume task. It is rarely the best way to add knowledge that changes; retrieval does that better.
Can we run an LLM privately?
Yes. Open weight models can be served inside your own cloud account or on dedicated hardware, so prompts and data never reach a third party. For hosted APIs, we use enterprise and zero retention settings where the provider offers them, and document exactly where data flows.
Do you build your own foundation model?
No. Training a foundation model from scratch is rarely sensible for a business. We select, integrate and adapt existing frontier and open weight models, which is faster, cheaper and easier to keep up to date.
From the Caudify Journal on LLM development.
- AI · 6 min readRAG vs fine-tuning: how to choose for your LLM applicationRAG gives a model the right knowledge at the moment it answers. Fine-tuning changes how the model behaves. Most teams need the first, some need both, and the way to decide is an evaluation set, not a debate.
- Security · 7 min readOWASP Top 10 for LLM applications (2025): the risks and how to fix themThe OWASP Top 10 for web apps doesn't cover what goes wrong when a language model sits in your product. The LLM list does. Here is each risk in plain English, with the controls that actually reduce it.
- AI · 5 min readAI-assisted software development: how we use LLMs from discovery to releaseAI makes the mechanical parts of software delivery faster. It doesn't make decisions, and it doesn't skip review. Here is where LLMs sit in each stage of our delivery, and the rules that keep them honest.
Start with a free assessment.
Tell us what you're building, improving or automating. We'll come back with an honest view of where LLM development fits and the fastest path to production.