AI Development & Integration
AI development in Dubai that ships: intelligent search, content-ops automation, recommendations and production assistants built on Claude and OpenAI — measured against cost, not hype.
AI development in Dubai only matters when it ships and pays for itself. Karve integrates large language models and machine learning where they earn their keep — intelligent search, content operations, recommendations and customer experiences that anticipate needs — and we measure every feature against accuracy and cost before we expand it.
Start with the use case, not the model
Most AI projects fail because they begin with a model and look for a problem. We do the opposite. An AI opportunity audit finds the single use case where AI measurably moves a number — usually search relevance, editorial throughput, or support deflection — and we ship that first, instrument it, then grow from evidence rather than ambition.
What we build
Intelligent search and RAG
Retrieval-augmented generation grounds answers in your own content — product data, documentation, structured content in Sanity — using vector embeddings so the model cites real sources instead of guessing. The result is semantic site search that understands intent, not just keywords.
Content operations and personalisation
AI inside the editorial workflow handles drafting, summarising, tagging and translation, freeing small teams to publish at scale. Paired with our SEO and content production work, that means structured-data generation and answer-engine-ready content — plus recommendation engines that personalise the journey in real time.
Assistants, agents and integrations
Custom assistants and agents are wired into your stack through clean APIs and the same engineering discipline we bring to web development. An abstraction layer keeps you model-agnostic, so support copilots, internal tools and workflow automation can swap models as pricing and capability shift.
Built to be measured
Every feature ships with the plumbing that keeps it honest:
- Evaluation harnesses that catch quality regressions before users do.
- Cost and latency monitoring so spend stays predictable as usage grows.
- Guardrails and grounding that keep outputs safe, on-brand and accurate.
We also weigh the footprint of what we build — right-sizing models and caching aggressively — because responsible AI is good engineering, as we argue in our piece on the environmental impact of AI. That is how AI becomes a durable advantage for UAE businesses rather than a line item that quietly balloons.
We integrate LLMs and AI tooling where they earn their keep: site search that understands intent, content workflows that scale editorial teams, and customer experiences that respond in real time — with a clear eye on cost, accuracy and sustainability.
What we do
Intelligent Search & RAG
Semantic, intent-aware site search and retrieval-augmented generation over your own content, grounded in vector embeddings so answers cite real sources rather than hallucinate.
Content Operations Automation
AI inside the editorial workflow: drafting, summarising, tagging, translation and structured-data generation that lets small content teams publish at scale without losing brand voice.
Recommendations & Personalisation
Product and content recommendations that adapt to behaviour in real time, turning browsing into a relevant, conversion-focused journey across web and commerce.
AI Integrations & Assistants
Custom assistants and agents wired into your stack via clean APIs and an abstraction layer — support deflection, internal copilots and workflow automation that stay model-agnostic.
Evaluation & Guardrails
Eval harnesses, prompt regression tests, cost monitoring and safety guardrails so AI features stay accurate, on-budget and trustworthy once they reach production.
How it runs
The same transparent shape every engagement follows — you always know where you are and what it costs.
Discover
A short, fixed-price sprint: audit, stakeholder interviews and the questions that decide the shape of the work.
Define
Scope, architecture and a fixed estimate — you know what's being built, why, and what it costs before we start.
Deliver
Tight design-build loops with weekly preview releases. You watch it become real on a URL, not in a deck.
Grow
Launch is the midpoint. Measurement, iteration and support keep the work earning after day one.
Fair questions
Where should we start with AI?
Not with a chatbot. We run a short AI opportunity audit that finds the single use case where AI measurably moves a number — usually search relevance, editorial throughput, or support deflection — and we scope that one feature end to end.
We ship it, instrument it against accuracy and cost, then grow from evidence rather than ambition. That keeps the first project small, provable, and hard to argue with internally.
What does an AI project cost, and how long does it take?
The audit is a fixed, low-commitment engagement — typically a couple of weeks — and it ends with a prioritised use case, a working-software plan, and a cost model. From there a first production feature such as intelligent search or a grounded assistant usually ships in roughly six to ten weeks.
Running cost is the part most vendors hide, so we surface it up front: every feature ships with cost and latency monitoring, and we right-size models and cache aggressively so per-query spend stays predictable as usage grows rather than ballooning into a surprise invoice.
Which models do you use, and are we locked in?
We run Claude and OpenAI in production, and we put an abstraction layer between your product and any single provider. Model choice is an engineering decision per use case — not a loyalty programme — weighed on accuracy, latency and cost.
Because the layer is model-agnostic, you can swap or mix models as pricing and capability shift, and open or local models are on the table when data residency or unit economics call for them.
How do you stop AI from making things up?
Grounding and evaluation. Retrieval-augmented generation ties answers to your real content — product data and structured content in Sanity, retrieved with vector embeddings — so the model cites real sources instead of guessing.
On top of that, every feature ships with evaluation harnesses and guardrails, built with the same engineering discipline we apply to the rest of your stack — so quality is tested before users see it, not assumed.
Do you build AI search and recommendations for ecommerce?
Yes — intent-aware search over your catalogue and real-time product recommendations are two of the highest-ROI AI features for commerce. Search understands what a shopper means rather than matching keywords, and recommendations adapt to behaviour as the session unfolds.
We build both to plug into your existing storefront and content rather than forcing a re-platform, and we pair them with our SEO and content production work so the same catalogue powers discovery on-site and in search engines.
Where does our data live, and is it safe for a UAE business?
Your content and customer data stay yours. We keep proprietary data out of model-training pipelines, scope each integration to the minimum data it needs, and can keep retrieval and embeddings within infrastructure you control when residency matters.
As a Dubai-based team we build for UAE realities — Arabic and English from the same content model, regional hosting options, and responsible, right-sized AI — so the result is a durable advantage for UAE businesses rather than a compliance or cost liability.