What are LLM development services?
LLM development services are the work of integrating large language models into a business's software and workflows, so they draft, summarise, extract information, classify text or hold a conversation as part of a real process. It covers choosing a model, designing prompts and tests, adding guardrails and running it at a controlled cost.
The model itself is a small part of the work. What makes an LLM feature useful is what surrounds it: the right records sent in, a clear instruction, checks on what comes out, and a person reviewing the output where it matters.
We work with models from OpenAI, Anthropic (Claude) and Google (Gemini), as well as open models that can run on infrastructure you control. We choose by task, cost and where your data needs to stay, not by brand.
How an LLM feature is built
An LLM feature is defined by its task and judged on your own examples. We evaluate it before release and ship it with guardrails, so its behaviour is predictable and reviewable.
1. Define the task
We agree the input, the expected output, who reviews it and what good looks like.
2. Collect examples
We gather real cases from your work, including the difficult ones, to build a test set.
3. Choose and design
We pick a model, write the prompts and decide what context is sent with each request.
4. Evaluate
Each version is run against the test set, so changes are judged on results, not impressions.
5. Release with guardrails
The feature goes live inside your workflow, with checks, reviewer sign-off and usage monitoring.
Why LLM features disappoint after launch
A good demo is easy. A feature your team relies on every day takes more care.
Tested on a few examples
The prompt worked on five cases in a meeting and fails on the sixth in production.
Costs nobody predicted
Every request sends far more text than needed, and the monthly bill surprises everyone.
No guardrails
The model answers questions it should refuse and invents details when information is missing.
Data sent where it should not go
Sensitive records go to an external service without anyone deciding that was acceptable.
Model changes break it
A provider updates a model and output quality shifts with no tests to catch it.
Separate from the workflow
Staff copy text into a chat window and back again instead of seeing the output where they work.
What we build with LLMs
- 01
Drafting
Letters, replies, notes and reports drafted from your records for a person to review.
- 02
Summarising
Long case histories, call transcripts and threads reduced to what the reader needs.
- 03
Extraction
Names, dates, amounts and other fields pulled from free text and documents into structured records.
- 04
Classification
Free-text messages sorted by topic, urgency or sentiment and routed to the right team.
- 05
Conversational assistants
Assistants for staff or customers, connected to your systems and limited to approved topics.
- 06
Evaluation & monitoring
Test sets built from your own examples, run on every change, with usage and quality tracked after launch.
Where the LLM works, and where a person decides
| Stage | What the LLM does | What stays human |
|---|---|---|
| Input | Reads the message, document or transcript | Deciding which records it is allowed to see |
| Processing | Drafts, summarises, extracts or classifies | Setting the instructions and the rules it must follow |
| Checking | Runs automated checks on format and content | Reviewing anything that reaches a patient, customer or record |
| Improving | Logs output and flags low-confidence cases | Deciding what changes when quality slips |
Choosing the right kind of model
| Option | Fits when | Trade-off |
|---|---|---|
| Hosted frontier models (e.g. OpenAI, Claude, Gemini) | Tasks need strong reasoning or writing quality | Data goes to the provider under their terms |
| Smaller hosted models | Tasks are simple and run at high volume | Lower cost, but less capable on hard cases |
| Open models on your infrastructure | Data must stay within systems you control | More set-up and hosting work on your side |
We list these as models we work with, not partnerships. Hosting options are confirmed for each project.
Guardrails and data handling
Decided during planning, not after the first incident.
- Named reviewer before output reaches a record
- Only relevant context sent to the model
- Usage and cost monitoring
- Refusal rules for out-of-scope requests
- Logging of inputs and outputs
- Role-based access to AI features
- Private deployment where data must stay with you
Hosting set-up and the exact data rules are confirmed for each project, based on where you operate and the data involved.
AI integrated into a hospital management system
A 50+ module HMS for a psychiatric and mental healthcare practice, delivered in three months with AI integration.
Hospital OS: Patient Portal & AI
A calm public front door with 24/7 booking, wired straight into the hospital's own system, plus AI where it saves clinicians time.
- 24/7
- Appointment accessibility
- 1
- Schedule shared by website and front desk
- 50+
- Modules in the full system
Where to go next
RAG Development Services
RAG development services that let AI answer from your records, policies and manuals, citing sources, with controlled costs and data kept in systems you control.
Learn moreNatural Language Processing
Natural language processing for healthcare: route complaints and messages, extract data from notes and referral letters, search records in plain language, read calls.
Learn moreClinical Documentation AI
Clinical documentation AI that drafts consultation notes, discharge summaries, letters and referrals for clinicians to review and sign, not type from scratch.
Learn more
Frequently asked questions
It depends on the task. We work with models from OpenAI, Anthropic (Claude), Google (Gemini) and open models, and choose based on quality on your examples, cost and where your data must stay.
Yes. Open models can be hosted on infrastructure you control. It costs more to set up and run, so we discuss whether your data requires it. Hosting options are confirmed for each project.
We give it only the relevant records, instruct it to say when information is missing, check its output automatically and have a person review anything that matters. For question answering we usually use retrieval so answers show their sources.
We send only the context each request needs, use smaller models where they are good enough and monitor usage so costs are visible per feature.
Rarely as a first step. Most business tasks are handled well by a good prompt and the right context. We consider fine-tuning only when testing shows it is needed.
We run the test set built from your examples against the new version before switching, so quality changes are caught before your team sees them.
Usually, yes. We connect through your system's APIs and place the output where your team already works.
Bring us one text-heavy task
Share a few real examples. We will show you what an LLM can do with them and what it should not be trusted with.
Or write directly to support@algonsolutions.com