How is an AI MVP scoped in Iraq?
Functional AI MVPs start after discovery — that gets you a real working product with AI integrations, auth, basic admin, and deployment. Full production-grade AI apps with payments, dashboards, and complex workflows run after discovery. We always fixed scope after evaluation.
Can you build a ChatGPT-powered app for an Iraqi business?
Yes — and properly. Most "ChatGPT apps" are thin wrappers that break the moment OpenAI throttles them. We build with proper error handling, multi-model fallback (GPT → Claude → Gemini), rate limiting, caching, and prompt versioning. Same engineering you'd expect from a real SaaS.
Do you handle Arabic prompts and Arabic AI output?
Yes. Arabic prompt engineering is part of every AI build for Iraqi clients. Generic GPT responses in Arabic are mediocre — we tune system prompts, examples, post-processing, and dialect handling to deliver natural Iraqi-flavored Arabic where it matters.
What kinds of AI apps do you build?
AI chatbots (customer service, sales qualification), content generators (Arabic blog posts, ads, descriptions), data analysis tools, voice-to-text and translation tools, document Q&A (RAG over your own data), and workflow automation. If it can be done with the current generation of LLMs, we can build it.
Will my AI app actually scale?
Yes — the MVP is built on the same stack we use for enterprise products (Next.js, Postgres or Supabase, vector databases when needed, modern queue/cache layers). When usage takes off, the code scales without a rewrite. No "MVP code" you have to throw away in year two.
Can Iraqi diaspora-FinTech AI apps comply with CBI rules and KYC norms?
Yes. Diaspora remittance, FX, and lending apps need Central Bank of Iraq (CBI) AML alignment and KYC flows that work for users in Frankfurt, Toronto, and Sydney as well as Baghdad. We integrate Sumsub or Persona for identity verification, add transaction-monitoring hooks, and ship a CBI-ready audit trail from day one. Compliance isn't a retrofit.
How do you handle Arabic and Kurdish NLP constraints?
Arabic LLMs are still uneven on Iraqi dialect (Mesopotamian) and Sorani Kurdish. We test outputs with native reviewers, use few-shot prompting with Iraqi-Arabic examples, and route Kurdish queries through models known to handle it (Cohere Aya, Jais 30B, Claude 3.5+). For mission-critical text, human-in-the-loop review stays in the workflow — we don't pretend the LLM is perfect.