LLM Development & Infrastructure

A demo is easy.Production is the hard part.

Fine-tuning, retrieval and inference infrastructure built as one system — so the model that impressed everyone in a prototype survives real users, real data and real cost constraints.

Scope your LLM build Talk to an AI engineer

Overview

What an LLM system actually needs.

The model is one component. Retrieval, evaluation, guardrails and the infrastructure underneath decide whether it holds up in production.

Custom LLM Fine-Tuning

Models adapted to your domain, terminology and tone on your own curated data — with dataset preparation and evaluation baked in, not bolted on afterwards.

Retrieval-Augmented Generation (RAG)

Your documents, tickets and records chunked, embedded and indexed so answers are grounded in your own knowledge base rather than the model's guesswork.

LLM Infrastructure & Hosting

GPU provisioning, model serving and scaling — self-hosted on our own infrastructure or API-based with a managed provider, chosen on workload and data-residency needs.

Prompt Engineering & Evaluation

Prompts versioned and measured against test sets, with guardrails for refusals, injection attempts and out-of-scope questions — behaviour you can regression-test.

Enterprise LLM Integration

Wired into the systems your teams already use — existing applications, ERP, CRM and internal workflows — with authentication and audit trails intact.

Multi-Model, Vendor-Agnostic Architecture

GPT, Claude, Llama and Mistral matched to the use case, behind an abstraction that lets you switch models as pricing and capability shift.

Not sure whether you need an LLM or just AI in your product?

This page is the deeper, LLM-specific specialization — models, retrieval, evaluation and the infrastructure they run on. Our Artificial Intelligence page covers the broader scope: AI features inside your software, intelligent workflows and decision systems.

See Artificial Intelligence

Process

How an LLM engagement runs.

Prove value on a narrow use case, measure it honestly, then harden it for production.

01
Assess
Use case, data sources, accuracy expectations and privacy constraints documented — including an honest read on whether an LLM is the right tool at all.
02
Design
Model selection, retrieval strategy, hosting approach and evaluation criteria agreed up front, with cost per request estimated before build.
03
Build & Evaluate
Pipelines, prompts and guardrails developed against a held-out test set — scored on real examples from your business, not vibes from a demo.
04
Deploy & Monitor
Rolled out with logging, drift and quality monitoring, cost tracking and a clear path to retrain or swap models as the workload changes.

Tech stack

What the work is built on.

Selected per engagement — matched to your data sensitivity, latency budget and cost envelope.

LLMs (GPT / Claude / Llama / Mistral)RAG & Vector DatabasesGPU InfrastructurePrompt EngineeringModel Evaluation & Guardrails

Industries served

Where language models earn their keep.

The same architecture, tuned to very different vocabularies, risk profiles and compliance obligations.

Let's take it past the prototype.

Tell us the use case and the data behind it. We'll come back with the model, retrieval and infrastructure approach that fits — and what it will actually cost to run.

Start a conversation Book an LLM review