We build the AI — and host it on our own network

AI software development,built and run in one place

Assistants, AI-powered mobile apps, micro products and fine-tuned models — engineered by our team and deployed on RackCloudSpace infrastructure, with your data kept in the UK or EU.

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Where to start

Not sure where AI fits? Start here.

Before anyone writes code, we look at your actual workflows and tell you honestly where AI pays off, where it doesn't, and what it would take. You get a costed plan — not a sales pitch.

Workflow audit

We map where your team loses time — manual reviews, approval queues, data that's re-keyed between systems — and where AI could take the load.

Use-case ranking

Every idea scored on ROI, feasibility, data quality and time-to-value. The weak ones get killed early, before they cost you a build.

Data readiness

An honest look at whether your documents, records and history are good enough to support a reliable pilot — or what it takes to get there.

Build & rollout plan

A costed path from pilot to production, including where it runs — with our own infrastructure on the table when data residency matters.

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What we build

AI that solves a real workflow

Once we know where AI fits, we build it — production software aimed at a specific job, not a demo. These are the shapes that work most often.

AI assistants & chatbots

Retrieval-augmented assistants grounded on your own content, deployed across web, app and Slack — so answers come from your data, not a generic model.

AI mobile apps

Cross-platform apps with intelligence built in — on-device inference where it counts, cloud models where it doesn't.

Micro products & MVPs

Small, focused AI tools shipped in weeks, not quarters — the fastest way to put an idea in front of real users.

Model fine-tuning

Domain-specific tuning of open models on your data, with evaluation and MLOps, when a general model isn't accurate or economical enough.

AI agents & automation

Multi-step agents that take actions across your systems, with the guardrails and audit trail to run them safely.

AI integration

Wiring LLMs and ML models into the software you already run, behind your own APIs and access controls.

AI MVP builds

Take your idea to a working MVP

Fixed scope, a defined timeline, and real software your users can touch — the fastest honest way to find out if an AI idea is worth building further.

Scope

One week to agree exactly what the MVP has to prove — and what it doesn't.

Build

Working software on your real tools and data, not a prototype in a sandbox.

Ship

Live with real users in weeks, hosted on our own infrastructure.

Decide

Measure it, then decide together: scale it, change it, or stop. No sunk-cost trap.

Example builds

~6 weeks

Inbox operations assistant

Triages your operational email, extracts the actionable data, and drafts replies for approval — connected to one system of record to start. Never asks anyone to leave their inbox.

~6 weeks

WhatsApp collections assistant

Chases overdue invoices over WhatsApp from your accounting system, parses the replies ("we'll pay Friday" vs. a dispute), and tracks promises — so days-sales-outstanding actually drops.

~5 weeks

Meeting-to-tickets pipeline

Turns commitments made on a call into tracked tickets in your tool of choice, then nudges the owner a week later if nothing happened. Closes the loop instead of just summarising it.

These are examples of what a first build can look like — we scope yours around the problem you're actually trying to solve.

Scope an MVP

Case Studies

The kind of work we do

Four projects across the areas we're asked about most — assistants, mobile, micro products and fine-tuning.

AI AssistantSample

Support assistant that answers from your own docs

SaaS / Customer support

~60%of routine tickets deflected

Challenge: A growing support team was buried under repetitive questions whose answers already lived in the product docs and past tickets.
Solution: We built a retrieval-augmented assistant grounded on the company's documentation and knowledge base, deployed on the web widget and in Slack, with escalation to a human when confidence was low.
Impact: Routine questions were resolved instantly, first-response time dropped from hours to seconds, and agents were freed for complex cases.

Claude
RAG
pgvector
Node.js

Mobile ApplicationSample

Field app with on-device AI and offline fallback

Field services / Logistics

2×faster task completion

Challenge: Field staff needed to scan, classify and get recommendations on-site — often with no signal — and the existing manual process was slow and error-prone.
Solution: A cross-platform mobile app with an on-device model for instant classification, backed by a cloud inference API that syncs when connectivity returns.
Impact: On-site tasks completed in half the time, fewer data-entry errors, and steady adoption across the field team.

React Native
On-device ML
Cloud inference
REST API

Micro ProductSample

A single-purpose AI SaaS, live in six weeks

Professional services

6 wksfrom idea to paying users

Challenge: A team had an AI idea worth testing but no appetite to fund a full build before knowing whether anyone would pay for it.
Solution: We shipped a focused micro product — one AI tool that did one job well (document summarisation and extraction) — as a lightweight, serverless web app.
Impact: The MVP was live in six weeks, validated demand with its first paying users, and set the roadmap for what to build next.

LLM API
Astro
Serverless
Stripe

Model Fine-tuningSample

A tuned open model that beat a generic one on niche data

Legal / Regulated

+22 ptsaccuracy on domain tasks

Challenge: A general-purpose hosted model was too generic and too costly at volume for a client's specialised, regulated document workflow.
Solution: We curated a labelled dataset, fine-tuned an open model with LoRA, built an evaluation harness to prove the gains, and deployed it behind an API on dedicated GPU infrastructure.
Impact: Meaningfully higher accuracy on the client's own tasks, lower per-call cost than the hosted alternative, and full control over where the data lived.

Llama
LoRA / PEFT
Evaluation harness
GPU hosting

How we work

From problem to production

A short path from a real problem to software running in production — and staying there.

Discover

We start with the problem, not the model — where AI genuinely helps, and where it doesn't. You get an honest scope.

Prototype

A working proof of concept on your real data, fast, so we're deciding from evidence rather than a slide deck.

Build

Production engineering — the app, the pipelines, the guardrails and the evaluation to know it actually works.

Deploy & run

We ship it and, because we own the infrastructure, we can host and operate it too — on our own UK/EU network.

Why RackCloudSpace

The only AI partner that also owns the datacentre

Most AI shops hand you code and leave hosting to someone else. We run our own Tier-certified facilities and GPU infrastructure — so the model we fine-tune for you can run on hardware you control, with your data never leaving the UK or EU.

The AI practice is new; the company behind it isn't. RackCloudSpace has run production hosting for businesses across the UK, US, Canada and the Middle East for years — that's the infrastructure and the support team your AI runs on.

Dedicated GPU compute for training and inference
Private cloud and colocation for data that can't leave your control
UK / EU data residency for regulated workloads
One team accountable for the build and the infrastructure

See our infrastructure

Tools we work with

Anthropic Claude
OpenAI GPT-4
Meta Llama
Google Gemini
Mistral
RAG
LoRA / PEFT
Vector databases
React Native
MLOps

We're not tied to one provider — we pick the model and framework that fit the job, whether that's a hosted frontier model or an open one we run for you.

FAQs

Common questions

If yours isn't here, ask us — we'll give you a straight answer.

What makes you different from a pure AI agency?

We build the software and we own the infrastructure to run it — private cloud, dedicated GPUs and colocation on our own network. Most agencies hand you code and leave hosting to someone else; we can do both, with your data staying in the UK or EU.

Do we need a huge dataset to get started?

No. Retrieval-augmented approaches work with the content you already have — docs, tickets, a knowledge base. Fine-tuning needs more, but we help you scope whether it's worth it before you invest in labelling.

Where does our data live, and who can see it?

Wherever you need it to. Because we run our own Tier-certified facilities, we can keep training data and inference on infrastructure you control, with UK/EU data residency — which matters for regulated work.

Can you start small?

Yes — a micro product or a proof of concept is often the right first step. It puts something real in front of users in weeks and tells you what's worth building next.

What does an AI project cost?

TODO(client): confirm real pricing bands before publishing. Industry proof-of-concept work in the UK typically starts in the low tens of thousands; production systems with MLOps run higher. We'll scope yours before you commit.

Have an AI idea worth building?

Tell us the problem. We'll tell you honestly whether AI is the right tool, and what it would take to build and run it.

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