LLM Development and Fine-Tuning Services
Language model applications built on the right model, not the biggest one.
LLM development services cover choosing a large language model, shaping how it behaves and wrapping it in an application people can rely on. NetOfficials decides with you between prompting, retrieval and fine-tuning, then builds and tests the application against realistic questions from your own users. The wider generative AI work is covered by our generative AI development services.
- Building software since
- 2011
- Approved before the build
- Written scope
- Tracked in Jira
- Sprint demos
- Confirmed in the contract
- Your IP
What Is Included in Our LLM Development Services
The pieces that turn a language model from a demo into something a business can depend on.
Model Selection
Comparing candidate models on your own tasks, not on public leaderboards alone.
Prompt and Instruction Design
Structured prompts, output formats and guardrails that keep answers on task.
Retrieval Pipelines
Connecting the model to your documents so answers can draw on current information.
Fine-Tuning
Adapting a model on your examples when style, format or niche language needs it.
Evaluation Suites
A set of test questions and scoring rules you can rerun after every change.
Safety and Usage Controls
Input checks, output filters, logging and limits on what the application may do.
Who Our LLM Development Services Suit
Teams reach for language models for different reasons. We shape the plan around yours.
-
Product companies
Add a language feature to a product
Summaries, drafting, search or an assistant inside software your customers already use.
- Clear scope for the first feature
- Cost per request estimated early
- Release behind a feature flag
-
Regulated or privacy-minded firms
Keep data under tighter control
Weigh self-hosted models against hosted ones when data must stay within your environment.
- Review of what data the model sees
- Self-hosted option assessed
- Access logging designed in
-
Teams with a working prototype
Make the prototype dependable
Replace guesswork with measured tests, cost controls and a plan for model updates.
- Evaluation set built from real use
- Prompt and model comparison
- Monitoring after launch
Hosted Model or Self-Hosted Model?
You can call a model through a provider's API or run an open model on infrastructure you control. The right answer depends on your data, volume and appetite for operating it. We recommend one in discovery and can design the application so the model can be swapped later.
A hosted model is usually the better start when
- You want to reach a working version quickly
- Your request volume is modest or uneven
- You do not have staff to run GPU servers
- Your data is allowed to be processed by an external provider
| Consideration | Hosted model | Self-hosted model |
|---|---|---|
| Time to first version | Short, no servers to set up | Longer, infrastructure comes first |
| Cost pattern | Pay per use, scales with traffic | Fixed server cost, cheaper at steady high volume |
| Data leaving your environment | Sent to the provider under its terms | Stays on your own infrastructure |
| Model choice and tuning | Limited to what the provider offers | Wide choice, deeper tuning |
| Operations effort | Provider runs the model | Your team runs and patches it |
| Best for | Fast delivery and uneven demand | Strict data control and steady heavy use |
How We Deliver Your LLM Application
Every project moves from a written scope to agreed review points, so you always know what is happening and what comes next.
Discovery
The tasks the model must handle, the data it may use and how answers will be judged, written into a scope you approve.
Signed scopeModel and Approach Trial
Candidate models and techniques tried on your own sample questions.
Comparison resultsApplication Build
Prompts, retrieval, interfaces and controls built in sprints, with a demo at the end of each one.
Working applicationEvaluation and Safety Testing
Scored test runs, adversarial prompts and cost checks before release.
Test reportRelease and Tuning
Launch with monitoring, then adjust prompts, data and models as real usage arrives.
Live and monitored
What Affects the Cost of LLM Development
Language model projects have a build cost and a usage cost, and the second one surprises people, so we estimate both for your specific case before you commit.
Tokens per request
Longer prompts, more retrieved text and longer answers all raise the cost of each call.
Request volume
A feature used constantly costs far more to run than one used occasionally.
Model size and choice
Larger models cost more per call, and a smaller model is often enough for simpler tasks.
Fine-tuning and training data
Preparing good examples takes people time, and each tuning run uses compute.
Hosting arrangement
Self-hosting trades per-call fees for servers you pay for and operate.
Evaluation depth
The more rigorous the test set and review, the more upfront effort, and the fewer surprises later.
Why Choose NetOfficials for LLM Development
The working terms below are part of every engagement, not extras.
One Cross-Functional Team
Design, development, testing and DevOps run through a single team, so decisions stay connected.
Senior From Day One
The senior engineer who scopes your work stays involved while it is delivered.
You Own Everything
IP assignment is confirmed in the contract, and you receive the full source code and documentation at handover.
Mutual NDA
We sign a mutual NDA before you share the details of your project.
Your Working Hours
We agree a communication window that suits your time zone at the start.
Visible Progress
Work is tracked in Jira, with a demo and your feedback at the end of every sprint.
How We Choose the Right Stack for Your LLM Application
We recommend technology for your project and your team's future, not for our convenience. Three questions guide every choice.
Will it handle your load?
We size the design for your expected users, data and growth, so it does not need rebuilding when the business grows.
Will it fit your systems?
We check how the stack connects with the tools, databases and partners you already rely on before we commit to it.
Can you support it later?
We favour well-supported technology with a wide talent pool, so your own team or any future partner can maintain it.
Technologies we work with
- LLM APIs
Python
TensorFlow
Node.js
TypeScript
React
Next.js
PostgreSQL
MongoDB
GraphQL
AWS
Azure
Google Cloud
Docker
KubernetesFlexible Ways to Work With Us
Pick the model that fits where your project is today. You can move between them as it grows.
Project Delivery
We deliver an agreed scope, timeline and price.
- Written discovery scope
- Milestone reviews
- Best for a clear first release
- For ongoing work
Dedicated Team
A team that works only on your project, led by a named delivery lead.
- Cross-functional team
- Sprint demos and planning in Jira
- Scale the team up or down
Team Extension
Specialists who join your existing team, tools and processes.
- Works in your process and tools
- Agreed overlap hours
- Best for adding a missing skill
More From Our AI Development Practice
This service is part of our ai development practice. These pages cover the services most often needed alongside it.
LLM Development FAQs
What is the difference between prompting, retrieval and fine-tuning?
Prompting gives the model instructions at request time. Retrieval adds your documents to the request, and fine-tuning changes the model itself using your examples. Many applications need only the first two.
Do we need to fine-tune a model?
Often not. Fine-tuning helps when you need a consistent tone, a strict output format or specialist vocabulary, and we test simpler options first.
Can we run the model on our own servers?
Yes, with an open model on infrastructure you control. We weigh this against a hosted model in discovery, covering data, volume and who will operate it.
How do you stop the model making things up?
The risk cannot be removed entirely, but it can be reduced by grounding answers in your content, limiting what the model may answer and testing against known questions.
Will our data be used to train someone else's model?
That depends on the provider and plan chosen. We read the terms with you in discovery and pick an arrangement that fits what you are willing to share.
What do we receive at the end?
Prompts, pipelines, evaluation sets and any tuned model files are delivered with documentation, and the contract sets out who holds the rights to each piece.
Ready to Build Your LLM Application?
Share a few details. A senior engineer reads every brief and replies with questions and a suggested next step.
- We review your briefAnd reply with questions
- We sign a mutual NDABefore you share the details
- You get a written scopeTeam, timeline and model, before any commitment