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Generative AI — Dubai, UAE

Get generative AI out of the demo and into production

The pilot worked. The demo impressed everyone. Then it met real customer queries, real documents, and a real compliance question, and it stopped. We do the architecture, model selection, and guardrail work that decides whether an LLM project ships.

From demo to production

Your documents and dataWhere the data may live, and go

Source

Retrieval layerAnswers from your content, not training data

Model, chosen against a test setCheapest one that clears the bar

Guardrails and validationOutputs checked before they reach anyone

Your team's toolsWhere the output actually lands

Shipped

Illustrative production architecture.

The problem

The GenAI trap most businesses fall into

Most companies pilot a model, see an impressive demo, then find the gap between demo and production is where all the actual work lives.

Chatbot demos that fail on real customer queries
No basis for deciding which workflow to apply a model to first
Outputs that need a human checking every one, which cancels the saving
Data privacy questions that stall adoption before procurement
A model stack chosen before anyone tested whether it fits

What we do

What we do for you

We come in when the question has moved from should we use AI to which model, on what data, with what guardrails. If you’re still at the first question, start with an AI readiness assessment instead — it’s a different engagement and cheaper.

Which modelOn what dataWith what guardrails

Service areas

Our generative AI service areas

LLM selection and architecture

We evaluate the current model families against your actual requirements — cost per task, latency, accuracy on your data, and where the data is allowed to go. The right answer changes by use case, and frequently the cheapest model that clears the bar is the correct one.

RAG and knowledge systems

Retrieval-augmented generation so your model answers from your documents, wikis and databases rather than its training data. This is the pattern behind most successful enterprise deployments, and it's covered in depth under AI-powered knowledge base.

Prompt engineering and evaluation

Structured prompt frameworks and — more importantly — an evaluation harness. Without a test set you cannot tell whether a prompt change improved the system or moved the failures somewhere you weren't looking.

Content and workflow automation

Generative AI applied to report drafting, internal knowledge, and document-heavy processes, wired into the tools your team already uses via AI integration.

Responsible AI and governance

Guardrails, output validation, and data-handling controls. For UAE businesses this carries a jurisdictional dimension — companies operating in DIFC or ADGM sit under data protection regimes separate from onshore UAE, which changes where your data can be processed and therefore which architectures are available to you. Worth settling with counsel before model selection, not after.

Our process

How a generative AI engagement runs

A consulting engagement that ends in a defensible architecture, not an open-ended build.

01 — Strategy session

Free 30 minutes. What you've tried, what broke, and what the compliance constraints are.

02 — Use-case and data assessment

Which workflow, what data it needs, and where that data is permitted to go.

03 — Architecture and model evaluation

Structured comparison against your requirements, with the test set that justifies the choice.

04 — Governance design and handover

Guardrails, validation, and documentation so your engineers can maintain and extend it.

Your GenAI decision pack

Model choice with its evaluation

Retrieval architecture on your data

Guardrails and data-handling rules

Engineer-ready documentation

The outcome

What you walk away with

Production generative AI, not another demo
A model choice you can defend, with the evaluation behind it
Answers grounded in your own data rather than training data
Guardrails and validation protecting your brand and your obligations
Documentation your engineers can build on
A measured before-and-after on the workflow you chose, so the saving is a number you produced rather than one we asserted

Toolkit

Tools we work with

The platforms where generative AI output lands in your business. Last reviewed September 2026.

OpenAI

Make.com

n8n

Zapier

HubSpot

Slack

Google Workspace

Airtable

Stripe

Notion

Monday.com

FAQ

Common questions about generative AI consulting

Which LLM should we use?

It depends on the use case. We run structured evaluations against your own data and recommend the model that best balances accuracy, cost, latency and data privacy. Frequently the cheapest model that clears the accuracy bar is the right answer.

How do we handle sensitive company data with LLMs?

We design privacy-first architectures — private deployments, anonymisation, and retrieval patterns that keep sensitive data off public model APIs. UAE businesses operating in DIFC or ADGM sit under data protection regimes separate from onshore UAE, which affects where data can be processed, so this is worth settling with counsel before model selection.

How long before we see ROI from generative AI?

Workflow automation use cases typically show measurable time savings within sixty to ninety days of deployment, measured against a baseline taken before the build.

Can your team work alongside our in-house developers?

Yes. We often work as an embedded AI team — building, documenting and transferring knowledge so your engineers can maintain and extend the system after handover.

What if we have already built something that is not working?

That is most of the engagements we take. The usual causes are a retrieval layer returning the wrong context, no evaluation set so nobody can tell whether changes help, or a model chosen before the use case was defined. We identify which before proposing a rebuild.

Where this fits

Related engagements

Process firstAI consultingStart with an AI readiness assessment if the question is still which process to automate.Start with the assessment →ValidateAI proof of conceptValidate the use case first with a time-boxed PoC.See how a PoC works →ConnectAI integrationWire generative AI into the tools your team already uses.Explore integration →

Ready to build generative AI that actually ships?

Book a free strategy session. We’ll assess where your current attempt stands, identify the top opportunities, and give you a realistic path to production.