Business problems, solved with technology
We design and build practical solutions for the things that slow your business down - automation, data systems, bespoke applications and AI built on what your business already knows. Tell us the problem. We'll work out the technology.
01Approach
Most technology projects go wrong at the first question. They start with “we need AI” - or a new platform, or an app - and work backwards to find a problem that fits.
We start somewhere else.
The outcome, in plain language. Less admin. Fewer errors. Faster answers for customers. Reports that don't take a morning to build. Decisions made on numbers you trust.
Usually it isn't a missing tool. It's staff answering the same questions again and again. Someone copying information between systems that don't talk. Useful data nobody can easily use. Or an idea for a digital tool, and no technical team to build it.
AI might be the answer. It might be automation, an integration, a data pipeline or a bespoke application. Or you might not need anything built at all. We'll tell you.
You don't need more technology.
You need the right technology.
02Speciality · AI assistants & agents
Your information exists - in documents, policies, spreadsheets and systems - but people can't find it when they need it. A general AI model can't help: it knows nothing about your business.
This is one of the things we're particularly good at. Using retrieval-augmented generation (RAG), the system searches your approved information before it answers, with sources you can check.
Fig. 02 Retrieval, step by stepIllustrative
Approved sources
Knowledge indexready
Response
Waiting for a question…
Knowledge assistants
For when the answers exist but nobody can find them. Staff or customers ask in plain language; the assistant searches your approved documents and data and answers with the sources it used. Fewer interruptions, faster answers.
AI agents
For when finding the answer is only half the job. Agents retrieve what they need and use your systems to get work done - checking, drafting and preparing, with a person approving what matters. Less repetitive work, nothing sent without sign-off.
Controlled information, clear boundaries and a visible trail from every answer back to its source.
Answers are built from retrieved information. If it isn't in the sources, the system says so.
Every answer can point back to the document, record or page it came from.
People only retrieve what they're allowed to see. Access rules apply to the AI too.
Agents can check, draft and prepare. Anything with consequences waits for a person to confirm.
03Selected work
We'd rather show you how something was built than tell you how good we are. The problem: property information spread across pages in inconsistent formats, and buyers who just wanted a straight answer. The solution touches every layer - data, AI, search, application and cloud.
Conversational property search, grounded in real listing data.
The problem: property information was spread across listing pages in inconsistent formats, so buyers have to click through them one by one to find a match. This proof of concept explored whether an assistant could answer their questions directly from real listing data - combining a data pipeline, AI-assisted cleansing, hybrid search and a conversational interface.
Fig. 03 Architecture
01 Collect
02 Clean
03 Index
04 Answer
05 Experience
06 Platform
The AI Property Assistant explored property search, but you don't need to be in property. The pattern underneath - scattered information in, clear answers out - fits problems like these in almost any business.
The same questions, again
Staff answering the same customer questions every day - about stock, services, bookings or policies - when the answers already exist.
Information nobody can find
Policies, manuals, spreadsheets and records spread across folders and systems. The real search engine is the one person who knows.
Copying between systems
Someone re-typing information from emails, forms and documents into another system - every day, by hand.
Reports that take hours
Numbers pulled from several places and rebuilt in a spreadsheet every week, instead of assembled automatically.
04Intelligent automation
Copying information between systems. Checking documents. Updating spreadsheets. Sending the same emails. If someone in your business does that every week, it's worth a look. We use AI where judgement is needed, reliable automation everywhere else - and keep a person in the loop where it matters.
Fig. 04 Supplier invoice, end to endIllustrative
Invoices, forms and applications read, checked and entered - fewer manual hours, fewer keying errors, and exceptions flagged instead of buried.
Emails and enquiries classified, routed to the right person and drafted for review - so customers hear back faster.
Records kept in sync across systems, without anyone copying and pasting - or making mistakes doing it.
Reports assembled from live data, rather than rebuilt by hand every week - the morning back, and numbers you can trust.
05Capabilities
This isn't a menu of things to buy. It's the toolbox we bring to your problem - and we only use the parts it actually needs.
Most suppliers own one of these layers. We take a problem all the way from idea to data, software, cloud, security and production - and join it up.
One accountable thread from problem to production
Assistants and agents that work from your own information, follow your rules and know the limits of what they know.
06Cloud & security
A prototype on a laptop is easy. A system your business can rely on needs identity, networking, secrets management, monitoring and repeatable deployments. We build that part too - on Microsoft Azure, defined as code and secure by default - rather than handing over a demo and wishing you luck.
Fig. 05 Reference architecture · AI knowledge system on Azure
Request pathPrivate endpoint
Private endpoints, network isolation and a web application firewall in front of anything public.
Entra ID, managed identities, RBAC and Privileged Identity Management. Least privilege by default.
Key Vault for everything sensitive. No credentials sitting in code or configuration files.
Environments defined in Bicep: reviewable, repeatable and straightforward to rebuild.
CI/CD pipelines so changes ship consistently, with nothing done by hand in production.
Sensible structure, policy and monitoring - and a platform sized to what you need, not what's fashionable.
07How we work
Five stages, each with something concrete at the end of it. You always know what's being built, why, and what it will take to run.
We learn the problem, the people involved and the systems you already have - talking to the people who do the work, not just the people who sign it off.
Output
We choose the simplest approach that will work, define what success looks like and agree it before anything gets built.
Output
Working software in short iterations, with security, reliability and maintainability designed in from the first commit.
Output
From prototype to something people actually use: secured, monitored and handed over properly.
Output
We watch how it's used in the real world and improve it where that adds value - not just because we can.
Output
08Engineering principles
AI systems touch sensitive information and real decisions. These principles shape every system we design, whatever its size - starting with honest advice about whether it should be built at all.
Identity, networking and secrets are part of the first design conversation, not a checklist at the end.
People, services and AI components get the access they need to do their job - and nothing more.
AI systems work from approved sources. What goes in, what comes out and who can see it are deliberate decisions.
Every system has a defined job, defined inputs and defined limits - including what it should refuse to do.
Automation handles the repetitive work. Decisions with real consequences keep a person in the loop.
Infrastructure as code, documentation and clean repositories - so your system never depends on one person's memory.
What we won't do
Morgan Webb Founder & engineer09About
Morgan Webb Digital is led by Morgan Webb. Morgan works across cloud, data, AI, infrastructure and software engineering - the full path from a business problem to a system running in production.
His background is hands-on: third-line server engineering, then Azure cloud engineering, then data and AI systems. That breadth is the point. The person designing your AI assistant also understands the data feeding it, the network it runs on and who should be allowed to use it.
What he enjoys most is taking a complicated, slightly messy problem and turning it into something practical that people actually use.
You work directly with Morgan.
No account managers and no hand-offs. You explain the problem to Morgan. Morgan designs the solution, builds it, deploys it - and can support it afterwards.
Certifications
10Start a conversation
Tell us what's taking too long, costing too much, creating unnecessary work - or simply not working as well as it should.
You don't need to know what technology you need. That's our job. Tell us what's going on and we'll work out what technology, if any, should do about it.
What happens next