Generative AI consulting services. Built around your business.
You have AI tools and a list of ideas. The business still works much as before. We work with your team to decide which agents are worth building, what they need to share, and what the first release must prove.
Dubai-based. Working remotely with teams globally. Meetings by arrangement.
What generative AI consulting gives you
Generative AI consulting is for the point before a build, when several ideas sound plausible but nobody can say which one will survive contact with the way the business actually works.
We work through real cases with the people who own the job: where the information comes from, what a good result looks like, what can go wrong, and where a person needs to approve. You get a scope your team can approve, delay or reject for a clear reason.
When consulting is worth doing first
Start here when the opportunity is real but the first use case is still contested: sales wants faster follow-up, operations wants less manual work, or a product team sees a feature customers may pay for. The question is which one has a clear owner, usable inputs, and a result worth measuring.
If the workflow, source systems, and review boundary are already settled, move straight to implementation. Consulting is there to reduce a bad first bet, not to keep a good one in meetings.
What you leave with
You leave with the agents worth building first and a map of what they share: company knowledge, access to your tools and rules for what each agent can do. The scope names an owner, the examples we will test against and what a useful first release has to do.
That gives the team a practical next move: start the build, narrow the scope, fix a missing prerequisite, or leave the idea alone until the conditions are better.
- The first workflow and the person who owns it
- Required source systems, data, and examples
- Where a person reviews or approves the work
- First-release scope, measurement, and next decision
What affects the cost and timing
The work is smaller and faster when one workflow has an owner, its source systems are known, and people can show us real examples. It takes longer when several teams need to agree, access is unclear, or the first use case touches customer-facing decisions.
We embed engineers into your existing teams: in the standups, delivering the outcome, using the processes you already run. That keeps the advice close to the work it is meant to change.
We embed AI engineers inside your business.
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.
Related services
What buyers ask us first.
What does a generative AI consultant do day to day?
Works with the people running the work, tests the real inputs and exceptions, and turns one useful opportunity into a build decision: what the system should do, what it needs, who reviews it, and what the first release must prove.
How do we evaluate generative AI consulting firms?
Ask how they choose a first use case, what they need to see before recommending a build, and whether the people making the recommendation will stay involved in delivery. They should be able to explain the workflow, its owner, its inputs, review points, and success measure in plain terms.
What is the main output of generative AI consulting?
A clear first build: the workflow or agent to start with, who owns it, the systems and examples it needs, where a person reviews it, and what the first release needs to show.
When should we skip consulting and build?
If the first agents, their company context, tool access, and approval rules are already clear, build-first is usually better.
How is this different from development services?
Consulting helps choose and de-risk the first build. Development and implementation put it into the systems your team already uses. The cleanest engagements carry the operating context from one into the other.
What should buyers avoid?
Avoid open-ended strategy retainers with no named workflow, no owner, and no decision about what happens next. Consulting should make the build clearer, not extend the ambiguity.
What we have built.
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 studiesBring us your hardest problems.
Bring the proposals, the tools you have tried and the decisions you have not made yet. I’ll tell you what I would build first, what it needs underneath it and whether it is worth paying us to do it.
- Show us the work you want AI to handle.
- Walk through your tools, your data and who needs to approve the work.
- Work out where the first agents belong and what they need underneath them.