AI & ML development services,
production-ready.
Magora is a London AI and ML development company. We build LLM-powered apps, RAG search over your own data, AI agents and machine learning models, or add AI to software you already run, with UK / EU hosting options and your data kept out of model training. Free working prototype in 5–10 days, AI MVP from £10,000.
What AI development
services cover.
AI development services turn a business problem into working software that uses AI: choosing the model, connecting it to your data, building the product and integrations around it, testing its accuracy, and keeping it running. Magora does all of this with one senior in-house team in London, from a free prototype to production.
Free working prototype
A working prototype of your AI idea on a sample of your real data in 5–10 working days, before any paid work.
First release in 6–8 weeks
After a two-week Discovery from £3,000, a first release typically takes 6–8 weeks. Most AI MVPs land between £10,000 and £25,000.
Your data stays yours
Business-tier model APIs that do not train on your inputs, UK / EU hosting options, and code, prompts and IP transferred to you.
AI & ML development
services.
Production AI, not demos: each system is measured against your real cases and integrated with the software your team already uses.
LLM-powered apps and copilots
Assistants, chat interfaces and structured-output workflows on OpenAI, Anthropic or Google models, with evaluation tests from the first sprint.
Search and Q&A over your data
Retrieval-augmented generation (RAG) over your documents, tickets or knowledge base, with answers that cite their sources.
AI agents
Agents that use your tools and APIs to run multi-step work, with guardrails and human sign-off where it matters. See AI agent development.
Document and workflow automation
Extraction, classification and summarisation that take the manual steps out of intake, review and reporting.
Machine learning and data
Prediction, classification, anomaly detection and computer vision, plus the data pipelines they depend on.
AI inside your existing software
Search, summaries, assistants or automation added to a product you already run, without a rebuild. See bespoke software.
Fine-tuning and open-source models
Llama, Mistral or Qwen adapted to your domain and deployed on your infrastructure, with no vendor lock-in.
Evaluation, monitoring and MLOps
Test sets, accuracy and cost tracking, drift monitoring and model updates: the parts that keep AI working after launch.
AI that keeps your
data safe.
The first question most clients ask is where their data goes. These are the options we design with; the right mix for your project is agreed in Discovery.
Your data is not training data
We build on business API tiers, such as the OpenAI and Anthropic APIs, Azure OpenAI and AWS Bedrock, whose terms exclude training on customer inputs by default. We check the current terms for your use case in Discovery, and switch on zero or limited retention where the provider offers it.
UK or EU hosting options
Application data, vector stores and logs can live in UK or EU cloud regions. Where you need model processing in-region too, we choose a provider and model that offer it.
Private deployments
Models can run through Azure OpenAI or AWS Bedrock inside your own cloud account, or as open-source models self-hosted in your VPC or on your own servers.
Access, redaction and audit
Role-based access to sources, personal data redacted before prompts where needed, and configurable logging so you can see what the system was asked and what it answered.
GDPR and regulated sectors
Data-minimisation review in Discovery, DPIA support, and experience in healthcare, financial services and education, where the rules on data are strictest.
EU AI Act readiness
A risk-tier review before deployment, with the transparency and documentation your system needs if you serve EU users.
Which kind of AI
does your case need?
Most projects combine two of these. The free prototype and Discovery show which one does the job at the lowest running cost.
| Prompting a model API | RAG over your data | Fine-tuning | Custom ML model | |
|---|---|---|---|---|
| Best for | Drafting, summarising, classifying and chat | Answers grounded in your own documents and records | A consistent style, format or narrow domain task | Predictions from your structured data, or vision |
| Data you need | Little or none | The documents the answers should come from | Hundreds to thousands of good examples | A labelled historical dataset |
| Speed to a first version | Fastest | Fast | Slower | Slowest |
| Main trade-off | Knows nothing about your business unless told | Only as good as the content and retrieval | Needs retraining as things change | Needs data work before modelling |
What AI development
costs in the UK.
Our 2026 prices in GBP. Our rates start from £49/hour per developer, and £65–£70/hour for a full team with QA and product ownership. In our study of 12 real 2026 project estimates, live AI processing was one of the three things behind the most expensive first releases, while an AI phase added to one existing product cost £7,598. Estimate your own with the cost calculator or see our UK software development pricing.
Two weeks to test the data, choose the model and approach, and cost the build.
A first release in 6–8 weeks. Most MVPs land between £10,000 and £25,000.
Agents that automate internal workflows with tool use and human sign-off.
Llama, Mistral or Qwen adapted to your domain, deployed on AWS, GCP or your own infrastructure.
Retrieval over your documents with a vector database, citations and an API your product can call.
A production-grade LLM application with evaluations, monitoring and UK / EU data residency.
Outside the UK? We deliver AI and ML development for clients in the USA, UAE and Australia from our London team.
From prototype
to production AI.
Same engineers from the prototype to handover. Weekly demos on a staging environment you can log in to.
Free prototype
5–10 daysA working prototype on a sample of your real data, so you see what the model can and cannot do before you commit a budget.
Discovery
2 weeksUse case, data sources, model choice, data-protection review and success measures, turned into a costed scope and roadmap.
Build
6–8 weeksTwo-week sprints with a working release at the end of each: the AI pipeline, the product around it and the integrations with your systems.
Evaluation
Every sprintTest sets drawn from your real cases, measured for accuracy, cost per request and failure modes, so quality is a number rather than an impression.
Launch & improve
OngoingProduction hosting, monitoring for drift and cost, and regular updates as models and your data change.
The AI stack
we work in.
Mainstream, well-supported tools, chosen per project so you are not locked into one model provider.
- OpenAI · Anthropic Claude
- Google Gemini
- Llama · Mistral · Qwen
- Azure OpenAI
- AWS Bedrock · SageMaker
- Google Vertex AI
- pgvector · Qdrant
- Pinecone · Weaviate
- Hybrid search · re-ranking
- LangChain · LlamaIndex
- Tool calling
- Bespoke pipelines
- Docker · Kubernetes
- vLLM · Ollama
- Evals · monitoring
What clients
say.
Verified reviews from our Clutch profile — 4.9 out of 5 across 71 client reviews.
“We were very happy with the overall experience.”
“Partnering with Magora for the development of the messenger was a decision we will never regret.”
“They didn't simply take our requirements and run with them; they truly made an effort to understand everything.”
Three kinds
of partner.
We've teamed up with 500+ businesses — fresh-faced startups, established mid-market and global enterprise.
Startups & MVPs
Production-grade MVP in a calendar quarter. Money-back if we miss the date — see our Startup MVP service.
Small & medium business
Internal tools, customer apps and digital transformation work — the kind that pays for itself within a year.
Enterprise
Compliance-heavy platforms, MDM-managed fleets, audit-ready architecture. SOC 2, GDPR, MHRA passed first time, every time.
AI and machine learning
we've shipped.
Four products with AI at their core: local language models on private data, live captions for events, receipt scanning with OCR and machine-learning analytics on a map. Each card gives the problem, what we built and how it is used.
University of Essex
- Problem
- Sensitive files had to be classified by confidentiality level, and cloud AI services were not allowed.
- Built
- A self-contained AI Orchestrator module running two local models: an LLM (Mistral 7B) that proposes new semantic tags and confidentiality rules for human review, and a fine-tuned entity-recognition model that applies the approved rules and writes a standard YAML file.
- Outcome
- Classification is automated without sending data to third-party cloud APIs, and the model is retrained as experts review new tags.
Interprefy
- Problem
- Professional simultaneous interpreters are expensive, and many conference participants do not speak the shared language well enough.
- Built
- A cloud platform where remote interpreters work from a console, event audio and video are streamed to them, and the translated audio goes to attendees' phones, with AI-powered live captions and subtitles.
- Outcome
- Any event can offer interpretation on attendees' own phones, with interpreters working from wherever they are.
Expensify
- Problem
- Expense reports needed receipt details typed in by hand.
- Built
- An optical character recognition (OCR) engine for Expensify's mobile apps that reads the merchant, date and amount from a photo of a receipt.
- Outcome
- Receipt scanning no longer needs manual data entry in the expense report.
KMA 9x9
- Problem
- Large volumes of data from many sources had piled up with no way to relate them to a place or process them.
- Built
- A React.js platform for KMA Tech that divides the map into 9 m by 9 m squares and links data from many sources to each one, with machine-learning analytics to detect events, find relationships and flag anomalies.
- Outcome
- Users see data and AI analytics on one map. The first version is live and the team keeps adding features.
Also: Empath AI, BodySync, Rollio. All projects →
AI for
your sector.
Where our clients use AI first, sector by sector, with the data rules each one runs on.
Healthcare
Clinical summaries, triage support and document automation.
AI for healthcare → SectorFintech
Document checks, fraud signals and customer support.
AI for fintech → SectorProperty and real estate
Listing data, valuations and tenant queries.
AI for property and real estate → SectorTransport and logistics
Forecasting, routing and paperwork.
AI for transport and logistics → SectorEducation
Tutoring assistants, marking support and content search.
AI for education → SectorConstruction
Drawings, specifications and site reports.
AI for construction → SectorHospitality
Bookings, guest messaging and demand.
AI for hospitality → SectorRetail and e-commerce
Product search, recommendations and support.
AI for retail and e-commerce →AI development
questions.
Everything needed to take an AI idea to a product people use: a free working prototype, Discovery, the AI pipeline (LLM prompts, retrieval, agents or a custom model), the application and integrations around it, evaluation, launch and support. We build LLM apps, RAG search over your data, AI agents, document and workflow automation, machine learning models, and AI features inside existing software.
At Magora a working prototype is free, Discovery is from £3,000 and an AI MVP from £10,000, with most MVPs between £10,000 and £25,000 (2026 prices, GBP). Larger builds start higher: AI agents from £20,000, a fine-tuned model from £25,000, a RAG pipeline with an API from £30,000 and a production LLM application from £40,000. Our rates start from £49/hour per developer, and £65–£70/hour for a full team with QA and product ownership. See what 12 real 2026 projects cost or try the cost calculator.
A working prototype takes 5–10 working days and Discovery two weeks. A first release typically takes 6–8 weeks after that. Larger agent systems and multi-model platforms are delivered in phases.
Not by us, and not by the model providers we build on by default. We use business API tiers, such as the OpenAI and Anthropic APIs, Azure OpenAI and AWS Bedrock, whose terms exclude training on customer inputs by default, and we check the current terms for your project in Discovery. For the most sensitive data, open-source models can run entirely on your own infrastructure.
Application data, vector stores and logs can be hosted in UK or EU cloud regions on AWS, Azure or Google Cloud. If model processing must also stay in-region, we choose a provider and model that support it, or deploy privately through Azure OpenAI, AWS Bedrock or a self-hosted open-source model.
Yes, and it is one of our most common projects: search and summaries over data you already hold, a RAG assistant over your documents, AI agents for internal workflows, or a copilot inside your admin system. We start by reviewing your code and data, then add the AI feature without a rebuild.
OpenAI, Anthropic Claude and Google Gemini models, plus open-source models such as Llama, Mistral and Qwen for private or on-premise deployments. We choose on accuracy, latency, cost and data-protection needs for your use case, not on brand.
Most business use cases start with RAG: the model answers from your own documents and records, which stay up to date without retraining. Fine-tuning fits when you need a consistent style, format or narrow task and have good examples to train on. Discovery tells you which one, or which combination, your case needs.
Yes. We build agents that call your tools and APIs to complete multi-step work, with guardrails, logging and human approval for the steps that matter. More on our AI agent development page.
You do. We sign an NDA on day one, the code lives in your repository as it is written, and source code, prompts, evaluation sets, fine-tuned model weights, designs and IP transfer to you on payment.
Pick a slot below
and let's talk.
No commitment, no charge. We'll review your project, scope the next step, and tell you the realistic price + timeline. Free working prototype + senior PO analysis before any paid engagement.
AI idea
worth shipping?
30-minute call with a senior software architect. We'll review your use case, data and constraints — and tell you what we'd actually build if it were us.