Custom LLM Development
Fine-tuned models, RAG pipelines, or custom architectures for domain terminology, proprietary data, and specific accuracy needs.
LLM Development
Custom LLM Development Built for Production
We ground language models in your data, connect them to real workflows, and engineer the product, guardrails, and infrastructure required to survive real usage.
- Grounded in your data
- Designed for real workflows
- Operated in production
Beyond the demo
An API key and a prompt are not a production system
The obvious path can work in a controlled demo and still hallucinate in front of a client, return an answer from stale data, or fail the first time an input varies from the happy path.
A dependable LLM system needs deliberate data access, retrieval, application logic, evaluation, safety controls, and operational visibility. We design those pieces as one system instead of treating the model as the whole product.
The model, product layer, and production infrastructure
We choose the simplest architecture that can meet your accuracy, privacy, latency, and cost requirements, then engineer the complete system around it.
LLM Application Development
The interface, workflows, business logic, guardrails, and infrastructure that turn a capable model into a useful product.
LLM Integration
Connections to your data, authentication, internal systems, and existing platform using managed or self-hosted model infrastructure.
See how we handle AI automationRAG and Knowledge Grounding
Retrieval pipelines that produce accurate, traceable answers from your documents and data, backed by evaluation and observability.
Production evidence
Quality is measured before the system reaches users
Every LLM delivery includes concrete quality controls tied to the task, the data, and the cost of a wrong answer. The goal is not a convincing demo. It is a system whose behavior can be inspected, measured, and improved.
- Eval
- a task-specific quality benchmark before launch
- Trace
- source-aware retrieval and permission checks
- Ops
- quality, latency, and cost visibility after launch
Architecture decisions
RAG, fine-tuning, or both - chosen for the problem
Not every LLM problem needs a custom-trained model, and most do not. We assess the job, the information the system must use, and the cost of a wrong answer before recommending an architecture.
The decision is based on performance, cost, privacy, and maintainability rather than defaulting to the most complex option.
Use RAG for changing knowledge
Retrieval is usually the right starting point when answers must come from a large, current body of documents and remain traceable to a source.
Use fine-tuning for behavior
Fine-tuning becomes valuable when a model must consistently follow a specialized tone, format, task pattern, or domain behavior.
Production constraints shape the system from day one
- 01
Define the business problem
We clarify who uses the system, what inputs it receives, what it must produce, and what a wrong answer costs.
- 02
Design data access and evaluation
We plan retrieval, permissions, benchmarks, and feedback loops before committing to a model architecture.
- 03
Build the complete application
We implement the model layer, product workflows, integrations, guardrails, and observability as one tested system.
- 04
Deploy, measure, and improve
We monitor quality, latency, cost, and drift after launch and iterate against real production behavior.
How we approach LLM development
Start with the business problem, not the model
Architecture follows the job, users, inputs, and risk profile the system must support.
Treat data access as a core decision
Retrieval quality and permissions shape accuracy, latency, and cost as much as the model itself.
Keep your data under your control
We build on infrastructure you own or control, including private and self-hosted deployment patterns.
Build for production from day one
Evaluation, failure handling, security, scaling, and monitoring are part of the architecture, not a launch-day patch.
Stay engaged after launch
Changing models, data, and requirements require ongoing measurement and deliberate iteration.
Learn how our consulting works
What teams ask before we start
Clear answers on scope, architecture, data, and delivery.
What models do you work with?
We work across leading foundation models including GPT, Claude, Llama, and other open-source models, with infrastructure such as AWS Bedrock, Google Vertex AI, and self-hosted environments.
Do I need a custom-trained model?
Usually not. A strong existing model combined with RAG or targeted fine-tuning delivers most of the value at a fraction of the cost of training from scratch. We assess the use case before recommending the approach.
What is the difference between RAG and fine-tuning?
RAG gives a model relevant information at query time, so knowledge is easier to update and answers can cite sources. Fine-tuning changes model behavior through training and is better for specialized tone, format, or task patterns. Many systems use both.
How do you reduce hallucinations?
We combine retrieval grounding, prompt and application design, output validation, evaluation, and human review where the cost of a wrong answer requires it.
Can you integrate an LLM into our existing product?
Yes. We connect LLM capabilities to an existing platform's data, authentication, and workflows without replacing what already works.
Will our data be used to train shared or public models?
No. We build on infrastructure you control and do not use your data to train systems for other clients.
Can you build private or self-hosted LLM systems?
Yes. For strict data sovereignty or compliance requirements, we can deploy in a cloud account or on-premise environment you control.
What does LLM development typically cost?
Cost depends on whether the system needs RAG, fine-tuning, a custom application, and substantial data preparation. We provide a clear fixed-price estimate after discovery.
Related expertise
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Built for real usage
Book a Discovery CallReady to build an LLM system you can trust in production?
Bring us the workflow, data, and accuracy requirement. We will map the architecture that can support them without unnecessary complexity.