Put your people back on the work only people can do.
Skilled people spend their days on repetitive manual work, and the AI pilots keep stalling between the demo and production. Automation removes the repetition, and AI turns the data already in the business into decisions — deployed with the guardrails that get it past the pilot and into daily use.
Understanding the discipline
What AI and automation actually do for the business.
Two different things travel under one banner. Automation takes a rules-based task that a person does the same way every time — moving data, checking a form, routing a request — and lets software do it reliably, all day, without tiring. AI takes a judgement that used to need a person — reading a document, predicting demand, answering a question — and makes a useful call on it from patterns in your data.
The value is rarely in the model itself. It is in choosing the handful of tasks where this genuinely pays, proving it quickly on real data, and then wiring it into the way work already flows — with a person kept in the loop wherever a wrong call would be costly. That last mile, from demo to dependable, is where most projects stall and where the return actually lives.
Repetitive manual work moves from people to reliable software.
Decisions shift from instinct and spreadsheets to evidence in the data.
Support and answers become available the moment they are needed, not the next working day.
AI moves from a stalled pilot to an operating part of the process.
Intelligent automation
Rules-based work runs itself, with AI handling the judgement steps a pure rules engine cannot.
Applied AI & ML
Prediction, classification, and extraction trained on your own data, embedded where the decision is actually made.
Conversational AI
Assistants that answer from your knowledge — grounded in your content and cited, rather than making it up.
The business case
Why businesses invest in ai & intelligent automation.
Returns that show up on the business, not on the engineering backlog.
- 01
Capacity without headcount
Repetitive work runs itself, so the people you already have move to the work that genuinely needs them.
- 02
Faster, evidenced decisions
The data already in the business becomes a prediction or a recommendation rather than a hunch defended in a meeting.
- 03
Answers on demand
Customers and staff get a grounded response in the moment instead of waiting in a queue until the next working day.
- 04
Pilots that reach production
Use cases proven on real data and deployed with guardrails — the last mile most AI projects never cross.
What we build & capabilities
The things you can commission — and what each one ships with.
Defined engagements, one at a time: the thing itself on screen, and the capabilities that come with it.
01 / 06
A use-case shortlist, ranked
Before anything is built, the candidate tasks are ranked by value and feasibility — so effort goes to the two or three that pay, and the fashionable-but-pointless ones are set aside early rather than after the budget is spent.
Capabilities
- Opportunity assessment
- Value-vs-feasibility scoring
- Data-readiness check
- ROI estimate
- Prioritised roadmap
Business outcomes
Where the business is today, and what changes.
The operational difference, in the terms the business already measures itself in.
Automated workflows
Faster, data-driven decisions
Scalable operations
Industries we serve
The same discipline, shaped to your sector.
Regulation, procurement and legacy estate differ by industry — and the engagement is shaped around them, not in spite of them.
Our engineering process
How the engagement actually runs.
Every stage has an owner, an output, and a point where you can change direction.
01
Find the use cases
Candidate tasks ranked by value and feasibility before anything is built.
02
Check the data
Whether what you hold can actually support the use case, assessed honestly.
03
Prove it
A proof-of-concept on your data, fast, with a clear go / no-go at the end.
04
Design the guardrails
Where a human stays in the loop, and what a wrong call must never be allowed to do.
05
Build & integrate
The model or automation wired into the way work already flows.
06
Deploy
Into production with monitoring, access controls, and a fallback path.
07
Monitor
Accuracy, drift, and cost watched, with retraining when the data moves.
08
Improve
The next use case, informed by what the first one proved.
Why Sumago
Why teams choose Sumago.
The technology partner serious businesses build with — and stay with.
Business understanding first
We understand the business before writing a line of code.
Strategic consulting
A consultative partner, not just a development shop.
Multidisciplinary team
Analysts, architects, designers, engineers, cloud & AI specialists, QA.
Transparency
Clear communication in every engagement.
Engineering quality
High standards, scalable and secure architecture.
Long-term partnership
Support and improvement long after delivery.
Technology ecosystem
Mainstream technology, chosen so you can hire for it later.
The stack is a means, not a position. It gets chosen against your constraints — and it stays maintainable by people who aren't us.
- Slack
