01
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.
Production AI engineering · United Arab Emirates
We build AI agents and applications, then keep them working.
Everything we build runs inside your own systems, on your data, and within the permissions your team already uses.
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.
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.
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.
02What we build
Agents that act, applications that hold the workflow, and the operation that keeps both working.
Three independent lines. Take one on its own, or let them compose.
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.
02
Modular by design
Take one agent, one application, or one operating engagement without inheriting a platform you did not ask for.
03
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.
04
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.
05
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.
04Example 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.
Agent
Document processing
Fields extracted and checked against your rules. Anything ambiguous goes to a person, with the reason attached.
Agent
Internal research
Answers drawn only from approved sources, each one tied back to the passage it came from.
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
05How we work
Understand the work. Then build for it.
Five steps, in this order, every time. No discovery theatre.
- 01
Understand the work
Week 1 to 2We follow the path a task actually takes and find the exceptions, then say honestly whether AI belongs in it.
- 02
Build the system
IterativeTools, permissions and data access first. Ordinary code wherever the job does not need a model.
- 03
Evaluate it
Before launchAn evaluation set built from your real cases, including the awkward ones. It is what makes every later change measurable.
- 04
Deploy it
ControlledInto your environment, through a pipeline, with staging, rollback, budgets and kill switches.
- 05
Operate and improve it
OngoingMonitoring on the indicators that move first, and recurring failures fixed at the cause.
06After 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.
None of this is measurable without an evaluation suite. Where one does not exist, building it is the first engagement.
How Managed AI Support worksWhat 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
07Where we work
Our address is in Dubai. Your systems can be anywhere.
Ai Support is a UAE-based AI engineering company. We work mostly with businesses across the United Arab Emirates and Saudi Arabia, and take on selected projects in the United States and Europe.
The engineering does not change with the postcode. Being here means comfortable overlap with Gulf, European and North American hours, and data residency decided early rather than discovered late.
More about the companyOffice
Meydan Grandstand, 6th floor
Meydan Road, Nad Al Sheba
Dubai, United Arab Emirates
Direct
+971 58 626 8786hello@aisupport.aeHave 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.