Viithiisys helps teams fix the workflow, product or operating system that is creating unnecessary friction.
Some problems need an AI capability. Some need a stronger software system. Some need a workflow repaired before either decision is useful.
Building and running software since 2007Several of these pages argue against buying that service
- The workIt stalls between two teams
- The systemEvery change is slow and risky
- The decisionYou are asked for a plan you do not have
Picking a service on a hunch is the most common way this work gets spent in the wrong place.
Start with the path that matches the problem.
Four entry points. If none of them is obviously yours, the section below is the one to read.
- One failing workflow diagnosed and the first fix identifiedBroken Workflow AssessmentFree, and small on purpose.
- An AI decision and implementation pathAI Consulting and StrategyThe decision before the build is funded.
- A defined AI capability builtAI services belowEleven of them, each with its own boundary.
- A product, application or operating system built or improvedSoftware services belowEleven more, from a first build to keeping one running.
How to tell which of these you actually need.
Most teams arrive knowing something is wrong and not which category it falls into. The distinction that matters is whether the problem is the work, the system, or the decision.
Work stalls between two teams or two systems, and a person bridges the gap
Usually. A broken handoff, not a missing feature.
AI Workflow AutomationNot the obvious one. Building new software adds a system to maintain without removing the handoff.
The same information is re-typed from documents that already contain it
Usually. A document problem with a defined input and output.
Intelligent Document ProcessingNot the obvious one. A general AI assistant is harder to control and harder to check.
Someone answers the same questions all day from sources they trust
Usually. A conversation problem with an answer-quality requirement.
AI Chatbot DevelopmentNot the obvious one. An agent that can take actions is a larger commitment than you need.
The software works but every change is slow and risky
Usually. A system problem, not a workflow one.
Legacy System ModernisationNot the obvious one. Automating on top of a fragile system moves the risk rather than removing it.
An AI feature is live and nobody can tell whether it is still correct
Usually. An operating problem.
MLOps and LLMOpsNot the obvious one. More model work does not answer the question of whether it is working.
You are being asked for an AI plan and do not yet have one
Usually. A decision problem.
AI Consulting and StrategyNot the obvious one. Buying a build commits you before the diagnosis exists.
You can see the pain but not the cause
Usually. Unclear, and that is normal.
Broken Workflow AssessmentNot the obvious one. Any service chosen now is a guess.
If two rows fit, the honest answer is usually the assessment. Picking a service on a hunch is the most common way this work gets spent in the wrong place.
What is true of every engagement here.
Whichever route you take, four things hold. They are the reason the service pages read the way they do.
We will tell you when the answer is not the thing you asked for
Several of the service pages argue against buying that service in specific situations. That is deliberate, not modesty.
The work is meant to leave your team able to run it
Code in your repository, decisions written down with their reasons, and no dependency on us for routine change.
Where a claim is not ours to make, we do not make it
No certification we have not been audited against, no result attributed to work we did not do, and no figure we cannot source.
Senior people do the work, including the first conversation
The person who scopes it is the person accountable for it.
The full service directory.
Twenty-six routes, grouped by what they do rather than by how we are organised, with one line each on what the page is actually for.
Fixing and automating work
AI services5- AI Workflow AutomationRepeat handoffs, approvals and system-to-system work.
- AI Agent DevelopmentContextual multi-step work with controlled actions.
- Agentic AI DevelopmentCoordinated multi-agent systems when one agent is not enough.
- AI Chatbot DevelopmentCustomer and team conversations with trusted answers and handoffs.
- Intelligent Document ProcessingDocuments, extracted information and exception paths.
Building and connecting AI capabilities
AI services6- Generative AI DevelopmentControlled AI features and applications.
- Generative AI ConsultingDecide whether a generative-AI build is the right intervention before committing to one.
- LLM DevelopmentLanguage-model systems with an explicit source, evaluation and operating boundary.
- AI Integration ServicesAI inside product, information and operating systems.
- MLOps and LLMOpsEvaluation, change and operating controls for production AI.
- Data EngineeringReliable source information for reporting, automation and AI.
Build the system
Software and product services4- Custom Software DevelopmentOne system, one organisation, built around how the work is done.
- Enterprise Software DevelopmentWork that crosses departments, with approvals and integration as scope.
- Software Product DevelopmentFor software you intend to keep selling and keep changing.
- SaaS DevelopmentThe multi-tenancy decisions that set what customer ten costs to onboard.
Deliver, improve and operate it
Software and product services7- Mobile App DevelopmentPhone-first products, and the release process behind them.
- Web App DevelopmentThe tool a business runs on daily, not a website.
- UI/UX and Product DesignDesign delivered as decisions engineering can build from.
- QA and Software TestingWhat to automate, what to test by hand, and what to leave alone.
- DevOps ConsultingFixing the stage where finished work waits.
- Cloud Migration ServicesEach system decided separately, and the ones that should not move named.
- Legacy System ModernisationChanging software you already run, without stopping it.
When a focused service is not enough
Wider engagements4Published operational context.
Nineteen years of delivery since 2007, across six countries. What is published, and therefore what we will point to, covers four documented routes.
Conscious Chemist
A customer-facing product experience, with a documented 38% faster product-question response.
Beauty and e-commerce.
Milo
Time-critical operational software, with a documented 70% faster check-in journey.
EV fleet and mobility.
Fitelo
An AI product running in production with evaluation and cost controls in place.
Health and wellness.
Vizitor
A platform in daily multi-site use, across 500+ workplaces in 15+ countries.
Workplace operations.
Deliberately absent
Two things to read carefully.
- Those percentages are engagement-specific results, not service promises. They describe what happened in one client system and are not a forecast for yours.
- Four published case studies are not the limit of where we have worked, only the limit of what we can point at publicly. If your sector is not listed, ask.
Tell us what is not working today.
Show us the workflow, product or system that is creating delay, repeat work or an unreliable customer experience. We will discuss the direct service path or diagnose the broken workflow first.