AI Solutions:
Strategy to Deployment
I help UAE and global businesses find where AI creates measurable ROI — then build and deploy the solution end-to-end. No demos that never ship. Working systems in production.
AI audit included · Fixed price · Production-ready
Where Most AI Projects Go Wrong
I built and killed a real AI product. Here's what I learned about where the failure modes actually are.
Document processing, customer triage, data extraction, report generation — these eat hours every week. The cost is real and measurable, and it compounds as you scale.
Consultants who've never built a production AI system will show you a polished prototype that breaks on real data. The gap between demo and deployed is where most AI projects die.
Most businesses have 3–5 processes where AI would meaningfully reduce cost or increase throughput. Finding those processes — before writing a line of code — is the work most consultants skip.
A well-designed AI system gets more useful as it processes more of your data. A badly designed one becomes a maintenance problem you inherit. The architecture decision is made at the start.
Most businesses miss where AI actually creates value — and spend budget on integrations that look impressive in a demo and break on real data the first week in production.
What I Build
Six categories of AI systems I've deployed to production. Each starts with understanding your actual data and processes — not a generic AI playbook.
Customer service bots, internal assistants, WhatsApp/Telegram integrations that understand context and escalate correctly.
Automated OCR, data extraction from invoices, contracts, and forms. Classification, summarization, structured output from unstructured documents.
Connect GPT, Claude, or open-source models to your workflows. Build retrieval-augmented systems over your knowledge base — so the AI answers from your data, not its training.
Personalized product suggestions, next-best-action systems, and content recommendations that improve with usage.
Dashboards that surface insights automatically. Anomaly detection, trend forecasting, natural language queries over your business data.
AI-driven routing, classification, and decision support. Deterministic rules where reliability matters; ML models where patterns matter.
How It Works
From the first conversation to a deployed system — here's what happens at each step.
AI audit & discovery (1–2 hours)
I review your business processes, data sources, and current manual work. We identify where AI creates the most measurable value — ranked by impact and what your data can actually support. You get a clear picture before any money is committed.
Strategy & architecture (1 week)
I design the system: which models to use, how to integrate with your stack, what data pipeline is needed, and how to measure success. You get a detailed plan with clear milestones and expected outcomes — locked before development starts.
Build & iterate (3–6 weeks)
I build the solution, test it on your real data, and refine based on actual performance — not assumptions. You see a working prototype on real data by week two. I adjust based on what it produces, not what we predicted it would produce.
Deploy & support
I launch to production with monitoring, error handling, and fallback mechanisms. You get documentation, team training, and a system that handles production conditions. If it breaks on edge cases in the first weeks, that's my problem to fix.
Why This Approach Works
Built on experience from real AI products — including one I discontinued after recognizing the model was fundamentally wrong.
CheckMVP was an AI startup idea analyzer I built in 2024. It was used on 500+ ideas. I discontinued it after concluding that early GPT models were too agreeable — they validated founders rather than challenging them. That failure is where my judgment about where AI actually breaks down in practice comes from.
I start every engagement with a 1–2 hour audit to find where AI creates measurable ROI in your specific operations. Most businesses have 3–5 high-value use cases and a dozen that sound good but would produce marginal results. I tell you which is which before writing a line of code.
A prototype that works on clean sample data is not the same as a system that handles messy real-world input. I build proper error handling, monitoring, and fallbacks. The AI I deploy handles production conditions — which are always messier than the demo.
What to Expect
What well-built AI systems actually produce — not projections, but consistent ranges from production deployments.
10x–100x
85–99%
2–6 weeks
Pricing
Fixed price per project. Agreed before work begins.
Prove value with a single capability
Full AI-powered solution for your business
AI across multiple systems and teams
Frequently Asked Questions
How long does an AI project take from audit to production?
Most projects run 3–6 weeks: 1 week for strategy and architecture, 2–4 weeks to build and iterate, then deployment. A simple AI pilot (single capability) can be live in 2 weeks. I give you a timeline commitment in the proposal — not an estimate.
What types of AI do you build?
Conversational AI and chatbots, document processing and OCR, LLM integrations (GPT, Claude, open-source models), RAG systems over internal knowledge bases, recommendation engines, AI-powered analytics, and intelligent process automation.
Do I need a large dataset to start?
Not necessarily. Many AI solutions work well with existing business data. During the audit I assess what you have and design a solution that works with realistic data volumes — not just enterprise-scale. I'll tell you honestly if the data situation won't support what you want to build.
What does a project cost?
Projects range from $5,000–$8,000 for an AI Pilot (one capability, production-ready) to $12,000–$20,000 for a full AI Product, and $25,000+ for AI across multiple systems. All prices are fixed — agreed before work begins. No hourly billing.
Which AI models do you use — GPT, Claude, or open-source?
Whichever fits the use case. For most production work that means Claude Sonnet 4.6 or GPT-5.1 — both cover the full range of business AI tasks (document analysis, structured generation, conversational AI, summarization) at reasonable cost. The flagship models (Claude Opus 4.7, GPT-5.5) are reserved for genuinely complex reasoning tasks that justify the price difference. For businesses with data sovereignty requirements, I use open-weight models like Mistral or Llama running on your own infrastructure so data never leaves your environment. Model selection is part of the strategy phase — I recommend based on your data, accuracy requirements, and budget.
How do you handle data privacy and security for AI systems?
Every AI system I build uses your data only for the purpose it's designed for. For businesses with sensitive data — financial, medical, legal — I can architect systems that run on self-hosted or private cloud infrastructure, keeping data entirely within your control. Privacy is part of the architecture discussion at the start, not an afterthought.
What if the AI output isn't accurate enough?
Accuracy expectations are set during the audit phase — before any code is written. I give you a realistic range based on your specific data and use case, not a best-case scenario. If a use case can't hit useful accuracy with current models and your data, I'll tell you that rather than build something that disappoints in production. During the build phase I test on your real data and iterate until accuracy is within the agreed range. If it isn't, that's my problem to fix — not a change request.
Where are your clients based?
I work with businesses globally — remote by default, in-person in Dubai when it suits. UAE-based clients receive a proper UAE tax invoice.
Ready to put AI to work?
We start with an audit to find where AI creates measurable value in your business. No commitment required to have that conversation.
AI audit included · Fixed price · Production-ready
Also available: MVP Development for founders going from concept to shipped product, and Fractional CTO for ongoing technical leadership.