The workflow lives in spreadsheets
A process that matters, held together by files, inboxes and habit. It works until the person who understands it is away, and it never produces data anyone can analyse.
02 · Custom AI Applications
Software where the intelligence is part of the product
Applications built around your workflow, with AI doing the part of the job it is genuinely better at, and an interface people can use, trust and correct.
Custom software is worth building when the workflow is genuinely yours. If an existing product already does 95 percent of the job, we will say so, and build only the integration that closes the gap.
Interface, logic, model boundary, your systems
01What it is
What this is, concretely
A custom AI application is ordinary production software: authentication, data model, permissions, interface, deployment. Inside it sits model-driven capability at the steps that earn it. Drafting, extracting, ranking, summarising, matching, classifying: work a person would otherwise do slowly and inconsistently.
What separates this from a chat window bolted onto an existing product is the interface. When a model contributes to a decision, the screen has to show what it produced, where that came from, and how a person changes it. That is an interface design problem at least as much as a model problem.
02The interface
The screen is where trust is either built or lost.
When a model contributes to a decision, the interface has to show what it produced, where that came from, and how a person changes it. Confirm, edit and reject are not niceties. They are what turns model output into something an organisation can stand behind.
Every edit a user makes is captured. That record becomes the evaluation set, which is why the review screen matters more than the prompt.
03Where it fits
When bespoke software is the right answer.
Off-the-shelf fits at 70 percent
The remaining 30 percent is the part specific to how you operate, and it is the part being handled manually, in parallel, forever.
The AI feature is a demo
Something impressive was built, then stalled on permissions, data access, auditability, or a screen that gives nobody a way to correct it.
04Examples
Systems of this shape
Build patterns and capabilities, not client projects.
- Knowledge assistant over internal documents
- A permission-aware interface over policies, contracts and manuals. Users only see answers drawn from documents they may read.
- AI-enabled internal platform
- The operational tool a team currently improvises, rebuilt properly, with model assistance at the slow and repetitive steps.
- Custom workflow application
- A queue, a review screen and an audit trail. The model prepares each item, a person confirms, edits or rejects it, and the edits become the evaluation set.
- Structured extraction pipeline
- Documents in, validated records out, with a review screen for the cases the system flags rather than a silent best guess.
05Approach
How we build it.
- 01
The workflow is the specification
We map the path work takes today, workarounds included, before deciding what software to write. The exceptions are usually where the value is.
- 02
Deterministic wherever it can be
Validation, calculation, routing and permissions are code. The model handles the parts that genuinely need language or judgement, which keeps behaviour predictable and cost sane.
- 03
Interfaces that expose uncertainty
Model output arrives as something to confirm, with its source attached, never as settled fact. People trust a system that admits what it is unsure about.
- 04
Corrections feed back
Every edit a user makes is captured. That record becomes the evaluation set and the honest answer to whether this is actually getting better.
06In production
Held to normal software standards
Custom software is worth building when the workflow is genuinely yours. If an existing product already does 95 percent of the job, we will say so, and build only the integration that closes the gap.
It is real software
Typed, tested, version controlled, deployed through a pipeline, with environments and rollback. AI inside does not lower that bar.
Data handling is decided, not assumed
Where data lives, which provider sees it, what is retained and for how long: settled before the build, written down, reflected in the architecture.
The model is replaceable
Provider access sits behind one internal boundary, so changing model or vendor is a configuration change and a re-run of the evaluations, not a rewrite.
It hands over cleanly
Documented architecture, runbooks and access. Operate it yourself, have us operate it, or move it to another team.
07Where and next
Where we do this work
These applications are built mainly for companies in the United Arab Emirates and Saudi Arabia. Where the fit is right we work with clients in the United States and Europe, and where data may live is settled in the same conversation.
Start a projectThe other two lines
- AI Agent DevelopmentGive AI the tools, permissions and context to finish real work inside your systems.
- Managed AI SupportEngineering operations for AI already in production: evaluation, monitoring, and the change that keeps it correct.
- All three, comparedWhat each line is, when it fits, and what it needs from you.
Is the workflow that matters most held together by spreadsheets?
Bring the spreadsheet, the inbox and the person who knows how it really runs. The exceptions in that process are usually the whole specification.