LLM Development

The right model, made right for your domain.

Most teams do not need to train a model from scratch — and should not. They need the right base model, adapted to their task and data, measured against their real cases, and deployed at a cost per call the business can live with. That is LLM development.

Talk to us All AI development

Fine-tune run domain-model · v3

Dataset8,200 curated examples

Training

Eval score71%94%

Passed evals · deployed to production

In one paragraph

AGI Software Solutions provides custom LLM development services: selecting the right model for your task (Claude, GPT, Gemini or open-weight models like Llama), fine-tuning where behaviour needs shaping, building evaluation suites from your real cases, and deploying with the latency and cost engineering production demands — including fully private, on-premise deployments where data cannot leave your infrastructure.

What We Build

Selection, adaptation, evaluation, deployment.

LLM work is a series of measured decisions: which model, adapted how, judged against what, served where and at what cost. Teams that skip the measurement end up with a model chosen by headline and a bill chosen by accident.

Selection

Chosen by benchmark

Candidate models tested on your real cases before commitment, because leaderboard rank and your task are different questions.

Adaptation

Fine-tuned when it pays

Fine-tuning applied where prompting and retrieval genuinely cannot reach — with the dataset work done properly, since that is most of the outcome.

Evaluation

Quality as a number

Eval suites built from your data, run on every change, so regressions are caught by the pipeline rather than by your users.

Economics

Cost engineered down

Caching, routing, model-size tiering and token discipline — the difference between an AI feature that scales and one finance quietly kills.

Plain English

What does LLM development involve?

LLM development covers everything between "we should use a language model" and a system running in production: benchmarking candidate models on your actual task, adapting them through prompting, RAG or fine-tuning, building eval suites so quality is a number rather than a feeling, and engineering inference for latency, cost and — where required — full data privacy on your own hardware.

The adaptation ladder matters: prompting is cheapest and usually enough; RAG adds your knowledge; fine-tuning shapes behaviour when the first two cannot. We climb it only as far as your problem requires.

Compared

API models vs open-weight models

Both are the right answer — for different constraints. This is how we decide.

	API models (Claude, GPT, Gemini)	Open-weight (Llama, Mistral, Qwen)
Best quality per effort	Highest, immediately	Competitive with tuning work
Data leaves your network	Yes (with provider agreements)	No — runs on your hardware
Cost shape	Per token, scales with usage	Fixed infrastructure, cheap at volume
Fine-tuning	Provider-dependent	Full control
Choose when	Speed to production, best reasoning	Data residency, high volume, deep customisation

How We Build It

Five stages from problem to production.

The same process across every AI Development project, scaled to the size of the problem.

Discovery

We work out what the system actually has to do, what data exists, and what happens today when it goes wrong.

Design

Architecture, model choice, integration points and failure handling, defined before any of it gets built.

Integration

Connecting to the systems that hold your data, with security and permission boundaries handled properly.

Automation

The system takes on real work, in the workflows your team already uses rather than beside them.

Refine

Tuned against real usage and measured with evals, because how people use a system is never quite how it was designed.

Use Cases

Where llm development pays off.

The patterns we see deliver, across startups, SMEs and enterprise teams.

Domain

Specialist assistants

Models adapted to legal, medical, financial or technical language, evaluated by people who know what wrong looks like.

Extraction

Structured data at scale

Documents in, clean structured records out — tuned and evaluated for your document types, at a cost per document that works at volume.

Private

On-premise LLMs

Open-weight models deployed inside your network for teams whose data cannot leave — with the quality gap engineered down.

Product

LLM features in your product

The model layer behind your own AI features: routed, cached, evaluated and priced for real usage.

Related Work

Systems we have shipped.

AI DevelopmentComputer Vision

Smart Video Chunker

A pipeline that chunks long-form video into searchable scenes, transcripts and entity timelines.

Under 12ms inference at 30 FPS

AI DevelopmentAI Chatbots

Conversational AI Studio

Context-aware multi-channel chat that qualifies inbound leads and routes them to the right person.

3.2× qualified leads, 54% faster replies

AI DevelopmentMarketplace

AI Marketplace Platform

Support chatbot, live SEO analyser and a vector recommendation engine, all sharing one retrieval layer.

+10% GMV, 40% fewer support tickets

See all case studies

What Teams Say

Hear from the teams we work with.

“We needed a voice agent that could actually qualify leads, not a chatbot pretending to be one. The team shipped a sub-700ms pipeline in 6 weeks. It now handles 5k calls a day.”

Priya RajHead of Growth, Ninjatech

“What sold us was their willingness to put AI engineers and product designers on the same call. We got working prototypes by week two and a production rollout in three months.”

James ThorntonCTO, Allindex

“AGI designed a CRM system tailored to our client management process. It is intuitive, reliable, and has centralized all our communication and history in one dashboard. This has greatly improved client retention.”

Carlos MendesProduct Manager, Qilinlab

Common Questions

Before you get in touch.

Should we fine-tune, or is RAG enough?

RAG first, usually. Fine-tuning is for behaviour — format, tone, a specialised task — not for knowledge. If your problem is "the model should answer from our content", that is RAG. If it is "the model should do this task our way, every time", fine-tuning earns its cost.

Open-source model or an API like Claude?

API models win on quality-per-effort and speed to production. Open-weight models win when data cannot leave your infrastructure or volume makes per-token pricing untenable. We benchmark both on your actual task and show you the numbers.

What does LLM development cost?

Model selection and prompt engineering with evals: from $15k. Fine-tuning projects: $25k–$100k+ depending mostly on dataset preparation. Private on-premise deployments add infrastructure work. Ongoing inference cost is a design target from day one, not a surprise at the end.

How much data does fine-tuning need?

For instruction-style fine-tuning, useful results often start at a few hundred to a few thousand high-quality examples. Quality dominates quantity — a curated 1,000 beats a scraped 100,000. Dataset curation is typically the largest single work item.

Can you deploy a model fully on-premise?

Yes. Open-weight models (Llama, Mistral, Qwen and others) deployed on your hardware or private cloud, with quantisation and serving optimisation so the economics work. Nothing leaves your network.

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