Illustrative run
- Request received
- Context retrieved
- Permission checked
- Action taken
Done within limits
01
AI Agent Development
Give AI the tools, permissions and context to finish real work inside your systems.
ExploreProduction AI engineering · United Arab Emirates
AI agents and applications.Built for real work.
Designed around your workflows, data and permissions. Engineered to keep working after launch.
One run, start to finish
Illustrative workflow
01 · Request
A customer disputes an invoice
By email, in their own words.
02 · Context
Reads the business behind it
AccountContractPayments
03 · Controlled action
Drafts the credit note
Within permissionsIssue refunds
04 · Approved outcome
A person signs it off
ApprovedLogged
01The fit
Between a capable model and useful work, there is engineering.
01 · Capability
The model is not the hard part.
Foundation models arrive fluent and completely unaware of your customers, your approval chain, and the four systems your work actually lives in.
Business context
CustomersDocumentsSystemsHistoryModel
Fluent, and general
Knows language, not your business.
Result
Useful work
Done inside the business, not beside it.
02 · Context
Your reality is specific, awkward, and non-negotiable.
Data, permissions, a legacy system nobody wants to touch, and three exceptions everyone knows and nobody wrote down. Whatever gets built has to work inside all of it.
Business context
CustomersDocumentsSystemsHistoryModel
Fluent, and general
Knows language, not your business.
Result
Useful work
Done inside the business, not beside it.
03 · Operation
Launch day is the easy part.
Providers retire models, data shifts, new edge cases arrive. Evaluations run on every change, and there is always a known-good version to go back to.
Business context
CustomersDocumentsSystemsHistoryModel
Fluent, and general
Knows language, not your business.
Result
Useful work
Done inside the business, not beside it.
In operation
Evaluate every changeMonitorRoll back if it slipsKnown-good version
02What we build
Agents that act, applications that hold the workflow, and the operation that keeps both working.
Three independent lines of AI development work. Take one on its own, or let them compose.
- Supplier invoiceSupplierReferenceDue date
Suggested
Approve for payment
Nothing recorded yet02
Custom AI Applications
Production software where AI is a working part of the product, not a chat box bolted on the side.
Explore - Restored
03
Managed AI Support
Engineering operations for AI already in production: evaluation, monitoring, and the change that keeps it correct.
Explore
03How we engineer
What you should be able to expect from anyone who builds this for you.
A fair checklist to hold any AI engineering partner to.
Built around your reality
We start from the work as it is actually done, including the exceptions. The generic version of your process is not your process.
Modular by design
Take one agent, one application, or one operating engagement without inheriting a platform you did not ask for.
Traceable by default
Prompts, tools, model versions and evaluation sets are versioned. Every run keeps its steps, so nobody has to guess why it did that.
Clear about limits
Where a claim rests on an assumption, we say so. Where something will not work, you hear it before the contract, not after the build.
Reasoning you can inspect
You get the thinking as well as the deliverable: what we rejected, and why this architecture. Decisions should survive being questioned.
04After launch
A deployed AI system is not a finished one.
The failure that matters is not the crash. It is output that stays fluent and quietly becomes wrong.
How Managed AI Support works- Known-good line
- Caught, rolled back
- Restored
What an operator watches
- Task success rate
- Against the evaluation suite, per release
- Escalation rate
- Moves before users start complaining
- Tool error rate
- Integration failures, kept separate from model failures
- Latency, p95
- Per task type, not averaged into meaninglessness
- Cost per task
- Attributed per feature so it can be cut on purpose
- Provider deprecations
- Tracked with a migration date ahead of the cutoff
None of this is measurable without an evaluation suite. Where one does not exist, building it is the first engagement.
05How we work
Understand the work. Then build for it.
Five steps, in this order, every time. No discovery theatre.
01 · Week 1 to 2
Understand the work
We follow the path a task actually takes and find the exceptions, then say honestly whether AI belongs in it.
02 · Iterative
Build the system
Tools, permissions and data access first. Ordinary code wherever the job does not need a model.
03 · Before launch
Evaluate it
An evaluation set built from your real cases, including the awkward ones. It is what makes every later change measurable.
04 · Controlled
Deploy it
Into your environment, through a pipeline, with staging, rollback, budgets and kill switches.
05 · Ongoing
Operate and improve it
Monitoring on the indicators that move first, and recurring failures fixed at the cause.
06Selected work
Selected work, live on the App Store.
Three products from our development experience. Download them and judge the craft for yourself.
07Example systems
The shapes these systems usually take.
Build patterns, not client projects. Ai Support is early stage, and we would rather say that than invent a case study.
01 · Agent
Document processing
Fields extracted and checked against your rules. Anything ambiguous goes to a person, with the reason attached.
02 · Agent
Internal research
Answers drawn only from approved sources, each one tied back to the passage it came from.
03 · Application
Operations workflow
A trigger, a lookup across systems, and the next step taken inside a permission boundary.
Also built
Knowledge assistant, permission aware
AI-enabled internal platform
Custom workflow application
Monitored agent orchestration
08Where we work
Our address is in Dubai. Your systems can be anywhere.
Ai Support is a UAE-based AI engineering company. Most of our work is for businesses in the United Arab Emirates and Saudi Arabia, with selected projects in the United States and Europe.
Office
Meydan Grandstand, 6th floor
Meydan Road, Nad Al Sheba
Dubai, United Arab Emirates
Comfortable overlap with Gulf, European and North American working hours, and data residency decided early rather than discovered late.
Have a workflow AI should be doing?
Bring the real one: the workflow with the exceptions, the four systems, and the person who knows how it actually runs. You will get an honest answer on whether AI belongs in it, and what it would take to build.