AI systems that ship. Not just a demo.

Chatbots, agents, and automation built on your data, wired into your stack, and judged on what they actually save you.

What's included

Nine ways we put AI to work.

01 / 09AI

AI Consulting

Know where AI actually pays off before you spend a dirham building it.

AuditRoadmapVendor-neutral
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02 / 09AI

AI Agent Development

Agents that take real actions in WhatsApp, sales, and support, not just answer questions.

WhatsAppSalesSupport
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03 / 09AI

AI Integration Services

Wire AI into the systems you already run, ERP, CRM, accounting, no rip-and-replace.

ERPCRMInvoicing
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04 / 09AI

RAG Development

Retrieval-augmented systems that answer from your own documents, with citations.

Knowledge baseCitationsAccess control
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05 / 09AI

Custom LLM and Fine-Tuning

Models tuned to your voice and domain, evaluated against your own test set.

Fine-tuningEvaluationDomain-specific
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06 / 09AI

AI Data Analytics

Predictive models and dashboards that say what's about to happen, not just what did.

DashboardsPredictiveAnomaly detection
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07 / 09AI

AI Voice Agents

Voice agents that book, qualify, and resolve, in a voice callers don't hang up on.

InboundOutboundMultilingual
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08 / 09AI

AI Chatbot for Website

A chatbot trained on your docs and products, live on your site in days.

Trained on your contentHandoffMultilingual
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09 / 09AI

Workflow Automation

Automations that connect your tools and move data, so your team works on what needs a human.

No-code visibleError alertsAny API
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The process

How an AI project runs.

Step 01

Discovery & use-case mapping

One call, a shared doc, a ranked list of AI opportunities. We pin down data sources and success metrics before a line of code.

Step 02

Data & architecture design

We map what data exists, what's missing, and the right architecture (agent, RAG, fine-tune, or plain automation) for the job.

Step 03

Prototype & review

A working prototype on a slice of your real data, in front of you within weeks, not a slide deck.

Step 04

Build & integration

Weekly staging deploys wired into your actual systems, so you watch it come together, not wait for a reveal.

Step 05

Launch & monitoring

Production launch with usage dashboards, cost alerts, and two weeks of tuning included.

The stack

What we reach for on an AI build.

OpenAIAnthropic ClaudeLangChainn8nPython

Why us for this

AI done the way it should be.

We don't bolt a chatbot onto a spreadsheet and call it AI.

Every model choice is justified by cost and latency, not hype.

You own the prompts, the data, and the infrastructure.

One fixed quote, no meter running on scope creep.

Questions

AI Services, answered.

No. We use your data only to build your system, on infrastructure you control. Nothing is used to train third-party models, and you can delete it on request.

Usually a few hundred dollars a month in API usage for a typical deployment, billed directly by the provider. We size that with you before build, no surprise bill.

Tell us the problem, not the technology, we'll recommend consulting, an agent, RAG, or plain automation, whichever is the cheapest thing that actually works.

Whichever fits your budget, latency, and data residency needs. We're not locked into one vendor and neither are you.

A chatbot or automation in 1–3 weeks, an agent or RAG system in 6–10, a full custom-trained model in 8–12. We quote a fixed date up front.

A clear, proven process

AI, shipped.
Not demoed.

From prototype to prod. Fixed quote in 48 hours, weekly drops to staging until it ships.

The processbrief → first staging build in ~2 weeks
01Send the briefWhat you’re building, in a paragraph.5 min
02Get a fixed quoteScope, price, and date, locked.48 hrs
03Watch it take shapeA new build to staging every week.weekly
04Ship to productionLive, handed over, and supported.live
team onlineavg reply 1h 47m