WHAT WE BUILD
AI agents we develop
Internal helpdesk agents
Answer IT, HR and policy questions in Microsoft Teams from SharePoint and your knowledge base, and hand the rest to a person with the conversation attached. Ours resolve 30–50% of requests without a human.
Document and invoice agents
Read invoices, purchase orders and forms from inboxes, extract the fields, check them against your rules and post them into the ERP. A person reviews only the exceptions.
Customer-facing assistants
Chat assistants for healthcare, car rental and university enquiries that answer from approved content, collect the details staff need, and know when to hand over.
Back-office workflow agents
n8n and LangGraph agents that move work between systems: qualify leads into the CRM, reconcile records, chase missing information and update tickets.
Agents on enterprise platforms
Agents built inside Copilot Studio, ServiceNow, SAP and Salesforce when your users already live there, extended with custom tools where the platform stops.
Agents over your data
Assistants that answer questions from your databases, warehouse or documents with sources cited, using retrieval-augmented generation (RAG) instead of guesswork.
ARCHITECTURE
How our AI agents are put together
The model plans the steps, but it can only act through tools it has been allowed to use, inside guardrails, with a person approving anything risky. The same pattern works on Copilot Studio, LangGraph, Semantic Kernel or n8n.
CASE STUDY
A Teams helpdesk agent that resolves 30–50% of staff requests
A UK professional services firm wanted its people to stop waiting on IT and HR for answers that already existed in policy documents. We built a Copilot Studio agent in Microsoft Teams that answers from SharePoint, using an Azure AI Search index for documents the built-in connectors could not handle well.
When the agent cannot resolve a request, it opens a ticket with the conversation attached, so nobody has to ask the same question twice.
- Client Professional services firm, United Kingdom
- Built with Copilot Studio, SharePoint, Azure OpenAI, Azure AI Search, ticketing hand-off
- Result 30–50% of requests resolved without a human
- Time to production 8–12 weeks
CASE STUDY
An invoice agent that removed about 80% of manual entry
An Australian business was keying around 1,000 supplier documents a month into SAP Business One by hand. We built a LangGraph agent that reads each document with Azure Document Intelligence, extracts and validates the fields, and posts the entry to SAP Business One.
Documents that fail a check, such as an unknown supplier or a total that does not add up, go to a review queue with the reason shown, so the team only handles the exceptions.
- Client Australian business, accounts payable team
- Built with LangGraph, Azure Document Intelligence, Azure OpenAI, SAP Business One
- Volume About 1,000 documents a month
- Result About 80% less manual data entry
PLATFORM OR CUSTOM
Choosing where your agent should live
We build on the platforms your teams already use when they fit, and write custom agents when they do not.
| If… | Usually the best fit |
|---|---|
| Your staff work in Microsoft 365 and the answers live in SharePoint | Copilot Studio, extended with Azure AI Search |
| The agent must act in ServiceNow, SAP or Salesforce | That platform's own agent tooling, with custom actions |
| The workflow crosses several systems and needs exact control of each step | LangGraph or Semantic Kernel, hosted in your cloud |
| It is mostly moving data between SaaS tools on triggers | n8n workflows with an LLM step where judgement is needed |
| You need to switch models or keep data in one region | A custom build with the model behind your own API layer |
Comparing the big platforms? Read our architecture review of Copilot Studio, ServiceNow, SAP Joule and Agentforce.
SAFETY BY DESIGN
How we keep agents safe in production
An agent that can act needs tighter controls than a chatbot that can only talk. These are built into every agent we ship.
- Least privilege. The agent gets a service identity with only the permissions its job needs, never a shared admin account.
- Your tenant, your data. We deploy into your Azure or AWS account and use model endpoints that do not train on your data.
- Human review where it matters. Payments, record deletions and customer-facing messages above a risk threshold wait for a person.
- Every step logged. Each tool call, input and output is traced, so any answer can be explained after the fact.
- Measured before release. Changes are scored against a fixed test set of real examples, so a prompt tweak cannot quietly make the agent worse.
HOW WE WORK
From one job to an agent in production
We start with one task that has a clear owner and a measurable result, prove it with real users, and only then add more tasks or systems.
Pick one job (week 1)
We choose a task with a clear owner, a measurable result and data you can share: for example, staff questions in one department or invoices from one inbox.Assessment and test set (1–2 weeks)
We collect real examples, write down what a correct answer or action looks like, and agree the hand-off rules and the permissions the agent will have.Build with guardrails
Orchestration, tools and prompts, built and scored against the test set on every change, with logging and the human review queue in place from the start.Pilot with real users
A small group uses the agent while we watch accuracy, hand-off rate and cost per task, and fix what they find.Production and handover (6–12 weeks in total)
Rollout, monitoring dashboards and runbooks, then either handover to your team or a monthly arrangement where we keep improving the agent.
TECHNOLOGY STACK
The frameworks, models and systems below are ones we have used on client agents. We pick the model per task and keep it swappable.
Agent platforms and frameworks
Copilot Studio
LangGraph / LangChain · Semantic Kernel · n8n · Model Context Protocol (MCP)
Models
Azure OpenAI / GPT
Claude
Gemini
AWS Bedrock
Knowledge and documents
Azure AI Search · SharePoint · Azure Document Intelligence · Neo4j
Enterprise systems
Microsoft Teams
ServiceNow · SAP Business One · Salesforce
Engineering
Python
.NET
Node.js
Azure
AWS
Have a task you would like an agent to take over?
Describe the job, the systems involved and roughly how often it happens. On the first call we will tell you whether an agent fits, and which platform we would build it on.
Discuss Your AgentQUESTIONS & ANSWERS
If you don't see an answer to your question, you can send us a message from our contact form.
A chatbot answers questions. An AI agent also takes actions: it decides which steps a task needs and carries them out through tools such as your ticketing system, ERP or document store, within the permissions you give it. Many of our agents start as chat assistants and gain actions once their answers are trusted.
A first agent for one well-defined job usually goes from assessment to production in six to twelve weeks. The assessment itself takes one to two weeks and ends with a test set, a scope and a fixed quote.
We do not publish a price list, because cost depends on the number of systems the agent touches, how much human review it needs and the volume it handles. The assessment ends with a fixed quote, and we estimate the monthly model and hosting cost per task before you commit.
If your users and data already sit inside one of those platforms, its own agent tooling is usually the fastest route, and we extend it with custom actions where it stops. A custom build with LangGraph or Semantic Kernel fits workflows that cross several systems or need exact control of each step.
We deploy into your own Azure or AWS tenant, use enterprise model endpoints that do not train on your data, mask personal data before it reaches a model where required, and log every action the agent takes.
It hands the case to a person with the full context instead of guessing. We set the confidence and risk thresholds with you during the assessment, and track the hand-off rate after launch.
Yes. If you have a prototype that works in a demo but not with real users, see our PoC to agentic production service.
Reviewed by Roy Shah, Director, Software Engineering at Metwaves. Roy has more than 14 years of experience engineering enterprise applications and has led more than 15 enterprise projects. Last reviewed 12 November 2026.
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