An agent should be able to read the client record, find the missing information and prepare the next action. We embed AI engineers inside your team to build that into the systems you already use, with a person approving the decisions that need one.
Dubai-based. Working remotely with teams across the US, UAE and worldwide.
A new enquiry arrives. Someone checks the client record, finds the right information, drafts a reply and updates the CRM. We build agents to handle those steps inside your tools, with your team deciding what they can do alone and what needs approval.
Your customers may need to search a document, ask a question about their account or complete a task inside your product. Our engineers build that into your existing codebase and data model, respecting what each customer is allowed to see and change.
Start with a job people already recognise. For example:
Engineers in your standups, working in your codebase, using the processes you already run. Your team helps shape the build and sees it handle real work as it takes shape.
The first agent might research a client. The next might prepare an update for the account owner. Both need to know who the client is, where the records live and who can see them. We build that foundation once, so each agent can use it.
The people doing the work help shape the build. They show us the exceptions, check the results and see what changes as each part goes live.
We sit with the people doing the job and follow real examples through your systems. Where does the work wait? What gets copied by hand? Which decisions need experience?
We make the records and documents the agents need accessible, with their sources and dates attached. The connections are shared, so the next agent does not have to start again.
Every agent gets a name and a limit. We agree what it can read, what it can change, and when it must hand the decision to a person.
Our engineers work in your codebase and your processes. Your team sees the agent handle real cases as it is built, including the awkward ones that a demo can skip.
We start with the agreed work and access limits. An owner can inspect what happened, approve the steps that need it, and take over when the agent cannot finish.
We test before and after release. If an answer loses its source, a record is missing or the wrong next step is chosen, that becomes a case the agent must handle better.
Scope and cost depend on the engineering capacity you need, the workflow, data quality and connected systems. We discuss those together before agreeing the engagement.
Whether an agent drafts work for review or takes action also changes the build. The AI agent development cost breakdown explains those decisions and what a useful quote should include.
Ask how the engineers will work with your team, then use these four questions to assess the build. Ask them of us too.
Ask them to walk through one case from start to finish: what starts it, what information is needed, what gets changed and who takes over if it fails. You should recognise your team's work in the answer.
Ask to see the source behind an answer and what happens when that source is old or missing. Also ask whether one client could ever see another client's information. Those are build questions, not details to leave until launch.
Reading a record, changing it and sending it to a customer are different permissions. Ask who sets them, where approvals appear, and how your team stops or reverses an action when possible.
Ask to see tests for difficult cases, the record of what an agent did, and the process for fixing a mistake. Agree what useful performance means before the build: better follow-up, less rework or a task completed reliably.
Start with a real task your team owns. Ask the company to explain how an agent would get the information, use your tools, handle exceptions and hand a decision back to a person. Then ask who will write the code and work with your team. A polished demo does not answer those questions.
How the work starts, which people and tools it passes through, what a correct result looks like, and what happens when something is missing. Bring a few ordinary cases and a few difficult ones. They reveal more about the build than a list of desired features.
Only the records, documents and tools needed for its job. A research agent may need read access; an agent preparing CRM updates also needs a controlled way to write. We scope that access with your team and account for missing, stale or conflicting records before release.
Before the actions your business decides are too consequential to run alone. That may mean sending a client message, approving an invoice or changing an important record. The agent can prepare the work and show its sources; the person responsible keeps the approval.
Check both the output and the job it was meant to improve. Does the answer have the right source? Did the CRM update correctly? Did the enquiry reach the right person sooner? We test real cases before release and keep checking as your information and processes change.
Agents for enquiry qualification, client research, CRM updates, company knowledge, support triage and document checks. We also build AI into customer-facing products. Each agent has a specific job and uses the company knowledge and tool connections that job requires.
Timing depends on the workflow, available data, integrations and review requirements. We scope a useful first release with your team and agree the work and dependencies before setting a delivery schedule.
Use an agent when the work requires reading context, making a judgment, choosing the next step, or coordinating across tools. Use ordinary automation when the task is deterministic and rule-based. Paying agent prices for a rule that never changes is a waste.
A platform gives your team tools to build with. You still need someone to connect your data, set access rules, test the behaviour and keep it working. We provide the engineers to do that inside your organisation. If your team already has the capacity, a platform may be enough.
Scope and cost depend on the workflow, engineering capacity, connected systems, data quality and approval requirements. We discuss those together before agreeing the engagement.
The hub: compare consulting, implementation, integration, and agent development routes.
The operator-led model behind the build, and how to evaluate the person doing the work.
Use this when the workflow is already chosen and the priority is getting it running in your stack.
What changes the price of an agent build, and what a useful quote has to include.
The wiring that connects an agent to the CRM, inbox, documents, and messaging tools.
Use this when the AI belongs in the product your customers use, not the back office.
The systems we have built, what they were meant to fix, and what changed once they ran.
Client delivery, cash and account ownership were spread across separate systems. The whole-company view brings them together, with each view using the same definitions.
Owners needed to see cash move before the monthly accounts were ready. The daily cash visibility system uses live bank feeds while the accounting platform keeps the official books.
Invoices had to fit a legacy system the business was keeping. The invoice batch build reads, matches and prepares the batch, then leaves final import and approval with the executive.
See the case studiesA pilot, a tool somebody bought, two proposals waiting on a decision. I’ll tell you which belong on one foundation, and whether it’s worth paying us to build it. If it isn’t, I’ll say so.