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.
ExploreAI development services
Three ways to put AI into production
Ai Support is an AI engineering company in Dubai. Our AI development work comes in three independent lines, for businesses across the United Arab Emirates and Saudi Arabia. Take one on its own, or let them compose.
No line requires another. Managed AI Support applies to systems we did not build.
The three service lines
- 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
Where it fits
Not every problem is an AI problem.
The question is whether a model does this step better than the code, rule or person already doing it.
Where a model usually earns its place
Reading what people wrote
Contracts, tickets, applications, invoices, forms. Pulling out the fields that matter and flagging the ones that do not fit the pattern, instead of a person retyping them.
Deciding what happens next
Triage, routing, classification, prioritisation. Work with enough variation that a rules engine turns into a maintenance problem, but enough structure that a person is mostly repeating themselves.
Finding the answer in your own material
Policies, manuals, prior cases, specifications. Answering from approved sources with the passage attached, and saying it does not know rather than filling the gap.
Drafting the version a person edits
Replies, summaries, first-pass reports. The model gets to a reasonable draft quickly. A person still decides what leaves the building.
Build it, or buy something that already does it
Buy it when
A product already does 95 percent of the job.
That is usually the better answer, and you will hear it from us.
Build it when
The part left over is genuinely yours.
Your process, your exceptions, your data, your obligations. That is the line between configuring software and building an application around the workflow.
01Compared
The three lines, side by side.
01
AI Agent Development
- What it is
- A system that decides and acts inside your tools, within a permission boundary.
- When it fits
- Repetitive work that still needs judgement, or work stranded between two systems.
- What it needs from you
- A way in (an API, a database or an export) and someone who knows the exceptions.
- What you have at the end
- The agent, its tools, its evaluation set, and a readable record of every run.
- Can it be bought on its own
- Yes.
02
Custom AI Applications
- What it is
- Production software built around your workflow, with model capability inside it.
- When it fits
- A workflow that is genuinely yours and currently held together by files and habit.
- What it needs from you
- Access to the people doing the work, and a decision on where data may live.
- What you have at the end
- The application, its source, its architecture docs and runbooks, all yours.
- Can it be bought on its own
- Yes.
03
Managed AI Support
- What it is
- Ongoing engineering operations for AI systems that are already live.
- When it fits
- Something is deployed and nobody can prove it still works as well as it did.
- What it needs from you
- A review of the existing system, and agreement on what correct looks like.
- What you have at the end
- A versioned evaluation suite, monitoring, and a known-good configuration to return to.
- Can it be bought on its own
- Yes, including for systems we did not build.
01Execute
AI Agent Development
An agent is only useful once it can reach the systems where work happens. Yours gets real tool access, a scoped identity, and behaviour you can measure.
Agents need somewhere to act. If the systems involved have no API, no database access and no export, opening that door is the first piece of work, and you will hear that before you commit to an agent.
AI Agent Development in fullIllustrative run · try both
01 · Request
A supplier asks to move a delivery
It arrives in the shared inbox.
02 · Context
Finds the order and the contract
OrderContractDelivery terms
03 · Authorised tools
Acts only where it is allowed
Orders: updateEmail: draftPayments
04 · Permission check
Is the change within its limits?
Over the limit
Within limits
Date updated, supplier told
Not this time
Over the limit
Sent to a person
Approved
Run recordRequest, sources read, tools called, decision and approver. Kept for every run.
02Build
Custom AI Applications
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.
Custom AI Applications in fullIllustrative application · try it: edit or confirm
Interface
What people see, correct and confirm
Suggested next step
Application logic
Checks every request on the way in and out
ValidationRoutingPermissions
Model boundary
One controlled place the model is called
ModelSwappable provider
Your information
The business data it works from
RecordsDocumentsWarehouse
Outcome
Waiting for a person
Nothing is written to your systems until someone confirms.
Not yet recorded
03Operate
Managed AI Support
AI systems degrade in ways ordinary software does not. Providers change models, prompts drift, data shifts, edge cases accumulate. This is the discipline that keeps a deployed system right.
We can operate a system we did not build, but only after a review that establishes what it does, how it fails, and what correct looks like. Where no evaluation set exists, building one is the first engagement rather than an optional extra.
Managed AI Support in fullIllustrative incident · system status
Restored
01 · Live
Running in production
Known-good
02 · Monitor
Every run traced
InputsToolsOutcomes
03 · Evaluate
Scored against expected answers
Regression
04 · Review
A person looks at what changed
A model update, a data shift, a new edge case.
05 · Correct
Fix it, or roll back
Roll backFix and re-test
06 · Restored
Known-good version live
Within target
And round againContinuously, for as long as it runs.
What it needs
What we need from you, and when.
Most of what decides whether an AI project works is settled before a model is chosen. These are the parts that are yours.
- What do you need from our side?
- A way into the systems involved, which usually means an API, a database or an export. Access to the person who actually does the work, because the exceptions they carry in their head are most of the specification. And a decision on where data may be processed, which is easier to make early than to discover late.
- How do integrations actually work?
- Through the interfaces your systems already expose, under credentials scoped to the specific records and actions involved rather than a shared administrator key. Where a system has no usable interface, opening one is a piece of work in its own right, and you hear that before it becomes a surprise.
- What has to happen before it goes live?
- An evaluation set is built from your real cases, the awkward ones included, and the system has to pass it. That set is what makes every later change measurable instead of arguable. It is the same gate applied to the agents we build and to the tools they are allowed to call.
- What happens once it is live?
- Something has to watch it. Providers retire models, data shifts, and the failure that matters is the quiet one where the output is still fluent and now wrong. You can run it yourself with the documentation and the evaluation suite we hand over, or we can keep it correct for you.
- How long does it take?
- That depends on how many systems are involved and how clean the way in is, so the first honest answer is which shape of project you have described rather than a number of weeks. One agent working a single queue and an application three teams depend on are different undertakings, and we will tell you which one you are looking at.
Scope
AI systems are scoped before they are priced.
The same sentence can describe two systems an order of magnitude apart. These are what move it.
01 How much of the work it takes on
One step of a process, a whole queue, or several processes that hand work to each other. The first is a project. The third is a programme, and should be sequenced like one.
02 How many systems it has to reach
Every integration carries its own credentials, data model, failure modes and tests. Two integrations are not twice the work of one, and the second is rarely the last.
03 What condition the data is in
Structured, available and reasonably clean is a different project from the same information scattered across exports, shared drives and an inbox.
04 What it has to be right about
A draft a person edits is held to a different standard from an action taken without review. The second needs more evaluation, tighter permissions and a clearer path back.
05 Where it is allowed to run
Constraints on where data may be processed, and on who may see it, change the architecture rather than decorate it. They are cheaper to design for than to retrofit.
06 Who operates it afterwards
Handing the system to your team with its documentation and evaluation suite, and operating it ourselves, are different engagements with different shapes.
We work these out with you before proposing an engagement. If the work does not need a system built for it, that is a useful finding and you will hear it.
Choosing
Questions worth asking anyone who wants to build this.
Ourselves included. The answers separate a dependable system from a demonstration that goes quiet a month after launch.
Ask what it is allowed to do
Not what it can do. Which records it may read, which actions it may take, whose credentials it acts under, and what it has to ask a person about first.
Ask how they know it works
If the answer is testing rather than a runnable set of your real cases with expected behaviour, then quality is an opinion and every future change is a gamble.
Ask what happens when the model changes
Providers retire and revise models on their own schedule. There should be an answer, and it should not begin with the words we would look at it.
Ask what you keep if you stop
Source, infrastructure, provider accounts, documentation. Anything that stays with the supplier is a dependency you did not choose and cannot easily end.
Ask what they would not build
A supplier who has never talked a client out of something is selling capacity rather than judgement. The refusals are where the experience shows.
The same list written as commitments rather than questions is what we hold ourselves to.
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.