AI agents that do
the actual work.
Magora is a London AI agent development company. We build custom AI agents that read documents, work across your CRM and ERP, and hand decisions to people when they should.
AI agent development
services.
An AI agent is software that uses a large language model to decide what to do next, calls your tools and systems to do it, and checks the result before moving on. A chatbot answers; an agent acts. These are the six kinds of agent UK businesses ask us for most.
Document agents
Contracts, invoices, claims, reports and application forms read, checked against your rules, summarised and filed — with the source line quoted beside every extracted value.
Workflow & process agents
Multi-step work that crosses systems: reconcile the CRM with the ERP, raise the ticket, update the record, chase the missing field. Built on the same integrations your team already trusts.
Customer service & chat agents
AI chatbot development that goes past FAQs: agents that look up the order, change the booking and escalate to a person with the whole conversation attached.
Internal knowledge copilots
Retrieval-augmented (RAG) assistants over your policies, tickets and manuals, answering with citations — so staff can check the answer instead of trusting it.
Real-time & voice assistants
Agents that listen alongside your people and prompt them live — as in Empath AI, which guides call-centre agents inside Microsoft Teams.
AI inside your product
Generative AI features added to the app or platform you already run — search, summaries, drafting, triage — without rebuilding what works. Broader ML and model work lives on our AI & ML development page.
How we build agents
you can trust.
Same senior engineers from the first workshop to handover. Weekly demos. No offshore rotation.
One workflow, measured
We start with a single workflow and time it as it runs today. The agent is judged against that baseline — hours saved, error rate, turnaround — not against a demo.
Guardrails and a human in the loop
Agents get the narrowest permissions that do the job, an audit log of every action, and a person's sign-off wherever a mistake would cost money, a customer or a regulator's attention.
Model-agnostic, UK data
OpenAI, Anthropic Claude, Google Gemini or open-source models, chosen on accuracy, cost and privacy. Data stays in UK or EU regions, and swapping the model later does not mean rebuilding the agent.
What an AI agent
costs in the UK.
Indicative Magora pricing for 2026, in GBP. Every engagement starts with a free working prototype, and the final figure is fixed after Discovery, when the workflow, integrations and data are known. Adding AI to an existing application typically runs £20,000–£80,000; ongoing support is £3,500–£9,000 a month.
Working prototype
A coded prototype of your agent on your own example documents or data, before you commit to anything.
Discovery sprint
Workflow map, baseline, risks, data-protection review and a fixed estimate — yours to keep, whoever builds it.
AI proof of concept
The agent running on real data in a controlled pilot, with evaluations that show whether it is good enough to scale.
AI agent for internal automation
One production workflow automated end to end — document, process or triage agent — with guardrails, audit log and monitoring.
Knowledge agent (RAG)
Retrieval pipeline, vector database and API over your document estate, answering with citations and access control.
Production LLM application
Customer-facing or multi-agent systems at production grade. Larger multi-agent programmes typically take 3–6 months.
From workflow
to working agent.
A production AI agent for a single workflow typically ships in 6–8 weeks after Discovery.
Workflow audit
1 weekWe sit with the people who do the work, map every step and exception, and record how long it takes today. Some workflows fail this test: a simple automation or an off-the-shelf tool would do, and we say so.
Free prototype
5 – 10 working daysA coded agent running on a sample of your real inputs, so the first conversation about accuracy is about evidence rather than promises.
Evaluations & guardrails
1 – 2 weeksA test set of real cases with known right answers, run automatically on every change. Permissions, approval steps and data-protection rules are agreed and written down.
Build & integrate
4 – 8 weeksThe agent is connected to your CRM, ERP, inbox, document store or Teams, with every tool call logged and every failure alertable. Weekly demos on your data.
Supervised pilot
2 – 3 weeksThe agent works alongside your team, who approve or correct its output. Corrections feed the test set; results are measured against the baseline from step one.
Run & improve
OngoingMonitoring of accuracy, cost per task and drift; a runbook for your team; and a plan for when the process, the data or the model changes.
The stack
behind our agents.
Chosen per project for accuracy, latency, cost and where your data is allowed to live.
- OpenAI GPT-4o family
- Anthropic Claude
- Google Gemini
- Llama, Mistral, Qwen
- LangChain
- LlamaIndex
- OpenAI Assistants
- Bespoke orchestration
- Tool / function calling
- pgvector
- Pinecone
- Weaviate
- Qdrant
- Hybrid search & re-ranking
- Microsoft Teams
- CRM & ERP APIs
- Email & document stores
- Custom API integration
- AWS Bedrock
- GCP Vertex AI
- vLLM / Ollama self-hosting
- UK / EU regions
- Evaluations in CI
- Guardrails & approvals
- Audit logging
- GDPR by default
- EU AI Act readiness review
AI we've
put into production.
Four projects where AI does real work inside somebody's operation.
Empath AI
Vocal-biomarker platform that prompts call-centre agents live in Microsoft Teams — model and API live in 12 weeks.
See case study → Enterprise · AIKMA 9x9 AI
AI-powered data platform that maps every data point to a 9 m × 9 m grid across several sources.
See case study → Clinical research · MLAnova
Clinical research platform with ML-driven participant matching and document classification.
See case study → Enterprise · DataRollio
Enterprise data automation — moving records between systems without losing them.
See case study →What clients
say.
Independently verified on Clutch — 4.9 out of 5, from clients including Danone, Unilever, Toyota, Cisco, AstraZeneca and Grant Thornton.
Rather than choose the flattering ones, we point at all of them. Every review on our Clutch profile is verified with the client, including the projects that ran harder than expected.
AI agents:
the honest questions.
Before you commit, read our checklist is your business ready for an internal AI agent?
At Magora, an AI agent that automates one internal workflow starts from £20,000. An AI proof of concept starts from £15,000 (4–6 weeks), a knowledge agent with a RAG pipeline from £30,000, and a production LLM application from £40,000. Adding AI to an existing application typically costs £20,000–£80,000.
Every project starts with a free working prototype and a Discovery sprint from £3,000, after which the price is fixed.
A chatbot answers questions. An AI agent takes actions: it plans the steps, calls your systems through their APIs — CRM, ERP, email, document store — checks what came back and either finishes the task or hands it to a person. Many projects start as a chatbot and become an agent once the answers are trusted.
A coded prototype takes 5–10 working days and is free. A production agent for a single workflow typically ships in 6–8 weeks. Multi-agent systems and agents spanning several departments run 3–6 months end to end.
High-volume, rules-heavy work that people do by reading and re-typing: document intake and checking, data entry between systems, first-line triage of emails and tickets, and internal questions answered from policies and manuals. Work that needs judgement on every case, or where one mistake is very costly, comes later — with a person approving each decision.
Design, not hope. The agent gets only the permissions its task needs, anything irreversible or high-value waits for human approval, every action is written to an audit log, and an evaluation suite of real cases runs on every change so a regression is caught before release rather than by a customer.
If the job is drafting and summarising for one person at a time, an off-the-shelf assistant is usually enough, and we will tell you so. A custom agent pays off when the work runs across your own systems, has to follow your rules, needs an audit trail, or has to run without someone typing a prompt.
OpenAI, Anthropic Claude and Google Gemini, or open-source models such as Llama, Mistral and Qwen when data must stay on your own infrastructure. Deployments default to UK or EU regions on AWS or GCP, GDPR compliance is built in, and an EU AI Act readiness review is part of Discovery.
You do. Source code, prompts, evaluation sets and IP transfer to you on payment, and the agent runs in your cloud account. Because it is model-agnostic, you are not locked to one AI vendor or to us. We sign an NDA on day one.
Have a workflow
an agent could run?
30-minute call with a senior AI engineer. Bring one process your team repeats every day — we'll tell you whether an agent is worth building, and what it would cost.