Custom LLM systems
Domain-specific language model systems designed from the ground up for businesses whose requirements no off-the-shelf model can meet on accuracy, cost, or control.
LLM Development
LLM Development: Language Models Built for Your Domain
A general-purpose model knows a little about everything and nothing about your business. We build LLM systems grounded in your data and your domain, engineered and evaluated like the production software they are.
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LLM Development
LLM development services we deliver
From model selection to production deployment, every layer of a serious language model system.
Fine-tuning
Foundation models adapted on your proprietary data, so the model speaks your terminology, understands your domain, and produces outputs your team can actually rely on.
Retrieval augmented generation
RAG systems that ground the model in your own knowledge base, so answers are current, sourced from information you control, and far harder to hallucinate.
Integration and APIs
LLM capability embedded into the systems your teams already use, through clean APIs and workflow integrations rather than yet another tab nobody opens.
Prompt engineering
Prompt frameworks designed, versioned, and tested like code, producing consistent high-quality outputs across every use case the system has to handle.
Evaluation and testing
Rigorous evaluation frameworks measuring accuracy, consistency, and failure modes, so you know exactly how the system performs before it touches production.
Generic AI will never know your business
Bring us your use case and a sample of your data. We will recommend the right architecture and quote it in fixed USD terms.
Start a ProjectHow it works
Our LLM development process
- 01
Discover
We identify the use cases, data sources, and success measures, in business terms first and model terms second.
- 02
Assess the data
Your data quality and infrastructure are audited honestly, so gaps surface before development rather than during it.
- 03
Design the architecture
Model selection, fine-tuning versus RAG, data flows, and integration approach are decided and documented.
- 04
Build and fine-tune
The system is built and the model adapted on your data, with prompts and pipelines engineered as versioned assets.
- 05
Evaluate
Structured evaluation against agreed benchmarks and adversarial cases decides readiness, not a good-looking demo.
- 06
Deploy and monitor
The system ships into your environment with output monitoring and a retraining loop that keeps it sharp.
Why AIoptimix
Why serious LLM work comes to AIoptimix
We run LLMs in production
Language model systems power features inside our own SaaS products. We carry the pager for them, which teaches lessons no benchmark paper contains.
Independent on model choice
We are tied to no model provider. Frontier API or open-weight model, hosted or self-hosted, the decision follows your use case, data sensitivity, and unit economics.
Evaluation before enthusiasm
Every system passes a measurable evaluation framework before deployment. Hallucination control is engineered, tested, and monitored, not waved away.
Outcomes defined up front
Success is written down in business terms before development starts: task accuracy, resolution rates, hours saved. Benchmark scores are a means, never the goal.
Tools and stack
The LLM stack we work in
Models
- Frontier model APIs
- Llama
- Mistral
- Open-weight models
Tooling and serving
- PyTorch
- Hugging Face
- LangChain
- LlamaIndex
- vLLM
Retrieval and data
- PostgreSQL
- pgvector
- Qdrant
- FAISS
- Redis
Cloud
- AWS
- Google Cloud
- Azure
- Docker
- Kubernetes
Engagement models
Pick the model that fits.
Staff Augmentation
Vetted engineers who join your team, work your hours, and follow your workflow.
Learn more →Dedicated Teams
A complete unit with delivery management that owns your product end to end.
Learn more →Fixed Price
A written scope, a fixed USD quote, and a committed timeline before we start.
Learn more →FAQ
Frequently asked questions.
What is LLM development?
It is the engineering discipline of turning large language models into reliable business systems: selecting or adapting a model, grounding it in your data through fine-tuning or retrieval, building evaluation frameworks, and integrating the result into real workflows with monitoring attached.
Fine-tuning or building from scratch: which do we need?
Almost always fine-tuning or retrieval on top of an existing foundation model, which delivers domain accuracy at a fraction of the cost. Training a model from scratch is justified only in rare cases, and we will tell you honestly if yours is not one of them.
How much data do we need?
Less than most teams fear, and quality beats volume. Fine-tuning and RAG systems often perform well on modest amounts of clean, well-structured domain data. We audit what you have early and state plainly whether it supports the use case.
How do you control hallucinations?
Through architecture and measurement: retrieval grounding in your own sources, structured evaluation against test datasets, domain validation, and production monitoring of outputs. No system is perfect, but hallucination rates can be engineered down and tracked honestly.
How is our data protected during development?
Training and deployment run in secure, access-controlled environments with encryption throughout, and self-hosted open-weight models are available where data must never leave your infrastructure. Your data and the models tuned on it remain yours, contractually and practically.
How long does an LLM project take?
Typical builds run from several weeks to a few months depending on whether the work is integration, RAG, fine-tuning, or a combination. The scope document you approve before we start includes the timeline, and it is written to be met.
Build a model that knows your business
Tell us the use case and what your data looks like. You will get a candid architecture recommendation, not a sales script.
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