What’s your AI architecture review process?

We review goals and constraints first, then map data sources, grounding strategy, tool boundaries, failure modes, and security. Deliverables usually include: a target architecture diagram, risk register, eval plan, and an implementation roadmap with milestones and metrics.

Can you build tool-using agents with approvals and guardrails?

Yes. For higher-risk actions (payments, deletes, emailing, production changes), we design a human-in-the-loop flow: explicit permission prompts, scoped tools, logging/audit trails, and environment separation. The agent can propose actions; execution happens only after approval.

How do you measure AI assistant quality (beyond vibes)?

We define objective metrics: task success rate, groundedness/citation accuracy, latency, cost per task, and error categories. Then we build an eval set (real queries + edge cases) and run regression tests before releases—so quality improves over time instead of drifting.

What are your React Native production quality standards?

Crash-free sessions, performance on low-end devices, clean navigation/state patterns, offline and sync behavior where needed, secure storage, and release discipline (versioning, staged rollouts, monitoring). We treat mobile as a product surface, not just a UI wrapper.

Do you handle multi-tenant security and data isolation?

Yes. We design tenancy boundaries intentionally (schema vs. DB vs. row-level), enforce them at the query layer, add tenant-aware caching, and include audit logs + admin controls. We also review pitfalls like cross-tenant leaks via background jobs or search indexes.

What engagement models do you offer?

Most work fits one of these: (1) fixed-scope delivery for a well-defined feature, (2) monthly retainer for continuous shipping, or (3) architecture + implementation sprint to unblock a team quickly. We’ll recommend the model that best matches uncertainty and timeline.

What timelines should I expect for a first usable release?

For many products, a vertical-slice release is realistic in 1–2 weeks, with a fuller release in 4–8+ weeks depending on integrations, data complexity, and compliance. We aim for early, working increments rather than a long “big bang” delivery.

How do you handle privacy, sensitive data, and compliance constraints?

We minimize data exposure, avoid sending sensitive fields when not required, and add redaction where appropriate. We also choose provider and deployment options based on constraints (region, retention, logging). The system design includes auditability and access controls from day one.

Bashar Ayyash

AI Engineer & Full-Stack Tech Lead Helping Businesses Grow with AI

I'm a Tech Lead and AI Engineer based in Amman, Jordan, specializing in Laravel, Next.js, React Native, and AI agents (RAG systems) for MENA businesses. Bridging the gap between 20 years of expertise and modern AI Agents.

Resume Get an AI Implementation Plan

Get in touch

hello@yabasha.dev

For consulting, architecture reviews, or project inquiries.

Full contact page

Base

Amman, Jordan 🇯🇴

Available for Consulting

The Arsenal

LaravelNext.js 16BunReact NativeTailwind v4OpenAI APIDockerPostgreSQLTypeScript

Skills & Expertise

20+ years distilled into measurable proficiency. Hover for details.

AI & ML

RAG Systems95%

LLM Integration92%

OpenAI API90%

Vector Databases88%

AI Agents85%

Prompt Engineering90%

Frontend

Next.js95%

React93%

TypeScript90%

Tailwind CSS92%

Framer Motion80%

Backend

Laravel95%

Node.js88%

MySQL/PostgreSQL90%

Redis82%

REST APIs93%

Mobile

React Native90%

Expo85%

iOS/Android80%

App Store Deploy78%

DevOps

Docker82%

CI/CD85%

AWS78%

Git95%

20+Years Experience

50+Projects Shipped

MENARegion Focus

AI + CodeFull-Stack Delivery

What Clients Say

“Bashar architected our AI-powered customer support system that handles 10,000+ queries daily. His understanding of RAG systems and production-grade AI is rare — he doesn't just build demos, he builds systems that work at scale.”

Enterprise Client

VP of Engineering — Banking Sector (MENA)

10K+ queries/day

In one minute

Yabasha (Bashar Ayyash) is an AI Engineer and full‑stack tech lead based in Amman, Jordan. He helps MENA founders, CTOs, and product teams design and ship dependable software, then layer in AI where it drives measurable outcomes. His work spans system architecture, hands‑on implementation, and production hardening—security, performance, and observability—so products stay stable under real traffic. Typical engagements start with a short discovery and a vertical slice, then iterate weekly with documentation and handover. If you need a senior engineer who can own delivery end‑to‑end, he can help.

Key offerings

•RAG assistants and tool‑using agents grounded in your docs, tickets, and databases
•Laravel + Next.js platforms with auth, payments, and dashboards
•React Native mobile app development for offline sync, performance tuning, and reliable app store releases
•Workflow automation and integrations (queues, ETL, notifications)

Differentiators

•Clear scope, fast feedback loops, and senior end‑to‑end ownership
•Practical AI: evals, monitoring, and guardrails (not demos)

Who I Help

CTOs, product leaders, and founders in MENA who need a senior engineer to design and ship AI-powered products end-to-end.

Fintech & Mobile

React Native apps with payment integrations, secure flows, and real-device performance tuning.

AI & Data Platforms

RAG assistants, agent orchestration, offline-first sync, and data pipelines at scale.

SaaS & Operations

Laravel + Next.js platforms with multi-DB backends, queues, dashboards, and audit trails.

Dev Tools & Infra

Internal utilities, automation scripts, open-source tooling, and Laravel AI SDK work.

See projects Read the Guides

Web & Mobile Platforms

Robust, scalable platforms using Laravel for backend and Next.js for high-performance frontends.

Mobile App Development

Native-feel iOS and Android applications built with React Native and Expo. View mobile app service details.

AI Chatbots & Assistants

Production RAG assistants and tool-using agents that answer from your docs, tickets, and databases — with citations, access control, and evaluation. Read the RAG guide.

Experience & Proof

Experience across enterprise delivery and fast-moving product teams in the MENA region. References and deeper case studies are available on request.

Enterprise (NDA)Banking (NDA)E‑commerceSaaSSupply chainHR / procurement

Case studies: Projects

Browse selected work, shipped systems, and practical engineering write-ups.

View projects

Trust signals: Guides

Long-form technical guides that show methodology, tradeoffs, and production thinking.

Read guides

How We Work

Clear scope, fast feedback loops, and production-quality delivery—without ceremony.

1. Scope & Success Metrics (2–5 days) — clarify the journey, constraints, risks, and define KPIs/SLAs/acceptance criteria.
2. Architecture & Plan (3–7 days) — choose the approach, map milestones, and lock the first deliverable.
3. Build the Vertical Slice (1–2 weeks) — deliver an end-to-end slice (UI → API → DB → observability) to validate direction early.
4. Iterate to Full Feature Set (2–6+ weeks) — ship weekly increments: features, edge cases, performance work, and stakeholder feedback loops.
5. Hardening, Launch & Ongoing Improvement (1–2 weeks + ongoing) — testing, security review, monitoring, rollout plan, and handover docs.

Production-Grade Standards

I optimize for reliability and maintainability—so the system keeps working when it's under load, audited, or handed to a team.

•Testing by default: unit + integration + e2e where it pays off; realistic fixtures; regression coverage for critical flows.
•CI/CD discipline: automated lint/type-check/tests, preview builds, safe migrations, and repeatable deployments.
•Observability built-in: structured logs, metrics, traces, alerts, and dashboards tied to business KPIs (not just CPU).
•Security-first engineering: least privilege access, secrets management, input validation, audit logs, and threat-aware reviews.
•Performance budgets: latency targets, caching strategy, background jobs/queues, and profiling—not "optimize later."
•React Native quality bar: crash-free sessions, offline handling, slow-device testing, startup performance, and app-store-ready release processes.
•Data reliability: idempotent sync, conflict handling, backfills, and safe reprocessing for large-scale datasets.
•AI safety & evals: prompt/version control, eval datasets, hallucination/grounding checks, tool-use guardrails, and human approval gates where needed.

FAQ

Use RAG when your answer must be grounded in changing or proprietary knowledge (docs, tickets, policies) and you need citations. Consider fine-tuning when you want consistent style/format, domain-specific behavior, or tool-selection patterns—and your knowledge is stable. Many real systems use both: RAG for facts, light tuning for behavior.

Typically by adding an AI service layer (queues, retries, rate limits), a small adapter for providers, and an audited tool/API boundary. We start with 1–2 high-value workflows, add observability and evals, then expand. The goal is incremental adoption: minimal disruption, measurable outcomes.

Skills & Expertise

20+ years distilled into measurable proficiency. Hover for details.

AI & ML

RAG Systems95%

LLM Integration92%

OpenAI API90%

Vector Databases88%

AI Agents85%

Prompt Engineering90%

Frontend

Next.js95%

React93%

TypeScript90%

Tailwind CSS92%

Framer Motion80%

Backend

Laravel95%

Node.js88%

MySQL/PostgreSQL90%

Redis82%

REST APIs93%

Mobile

React Native90%

Expo85%

iOS/Android80%

App Store Deploy78%

DevOps

Docker82%

CI/CD85%

AWS78%

Git95%

What Clients Say

“Bashar architected our AI-powered customer support system that handles 10,000+ queries daily. His understanding of RAG systems and production-grade AI is rare — he doesn't just build demos, he builds systems that work at scale.”

Enterprise Client

VP of Engineering — Banking Sector (MENA)

10K+ queries/day

In one minute

Key offerings

•RAG assistants and tool‑using agents grounded in your docs, tickets, and databases
•Laravel + Next.js platforms with auth, payments, and dashboards
•React Native mobile app development for offline sync, performance tuning, and reliable app store releases
•Workflow automation and integrations (queues, ETL, notifications)

Differentiators

•Clear scope, fast feedback loops, and senior end‑to‑end ownership
•Practical AI: evals, monitoring, and guardrails (not demos)

Who I Help

CTOs, product leaders, and founders in MENA who need a senior engineer to design and ship AI-powered products end-to-end.

Fintech & Mobile

React Native apps with payment integrations, secure flows, and real-device performance tuning.

AI & Data Platforms

RAG assistants, agent orchestration, offline-first sync, and data pipelines at scale.

SaaS & Operations

Laravel + Next.js platforms with multi-DB backends, queues, dashboards, and audit trails.

Dev Tools & Infra

Internal utilities, automation scripts, open-source tooling, and Laravel AI SDK work.

How We Work

Clear scope, fast feedback loops, and production-quality delivery—without ceremony.

1. Scope & Success Metrics (2–5 days) — clarify the journey, constraints, risks, and define KPIs/SLAs/acceptance criteria.

2. Architecture & Plan (3–7 days) — choose the approach, map milestones, and lock the first deliverable.

3. Build the Vertical Slice (1–2 weeks) — deliver an end-to-end slice (UI → API → DB → observability) to validate direction early.

4. Iterate to Full Feature Set (2–6+ weeks) — ship weekly increments: features, edge cases, performance work, and stakeholder feedback loops.

5. Hardening, Launch & Ongoing Improvement (1–2 weeks + ongoing) — testing, security review, monitoring, rollout plan, and handover docs.

Production-Grade Standards

I optimize for reliability and maintainability—so the system keeps working when it's under load, audited, or handed to a team.

•Testing by default: unit + integration + e2e where it pays off; realistic fixtures; regression coverage for critical flows.

•CI/CD discipline: automated lint/type-check/tests, preview builds, safe migrations, and repeatable deployments.

•Observability built-in: structured logs, metrics, traces, alerts, and dashboards tied to business KPIs (not just CPU).

•Security-first engineering: least privilege access, secrets management, input validation, audit logs, and threat-aware reviews.

•Performance budgets: latency targets, caching strategy, background jobs/queues, and profiling—not "optimize later."

•React Native quality bar: crash-free sessions, offline handling, slow-device testing, startup performance, and app-store-ready release processes.

•Data reliability: idempotent sync, conflict handling, backfills, and safe reprocessing for large-scale datasets.

•AI safety & evals: prompt/version control, eval datasets, hallucination/grounding checks, tool-use guardrails, and human approval gates where needed.

FAQ

Bashar Ayyash

AI Engineer & Full-Stack Tech Lead Helping Businesses Grow with AI

The Arsenal

Skills & Expertise

AI & ML

Frontend

Backend

Mobile

DevOps

What Clients Say

In one minute

Key offerings

Differentiators

Who I Help

Fintech & Mobile

AI & Data Platforms

SaaS & Operations

Dev Tools & Infra

Web & Mobile Platforms

Mobile App Development

AI Chatbots & Assistants

Experience & Proof

How We Work

Production-Grade Standards

FAQ

When should I use RAG vs. fine-tuning?

What’s your AI architecture review process?

How do you measure AI assistant quality (beyond vibes)?

Do you handle multi-tenant security and data isolation?

What timelines should I expect for a first usable release?

How do you integrate AI into an existing Laravel app without rewriting everything?

Can you build tool-using agents with approvals and guardrails?

What are your React Native production quality standards?

What engagement models do you offer?

How do you handle privacy, sensitive data, and compliance constraints?

Bashar Ayyash

AI Engineer & Full-Stack Tech Lead Helping Businesses Grow with AI

The Arsenal

Skills & Expertise

AI & ML

Frontend

Backend

Mobile

DevOps

What Clients Say

In one minute

Key offerings

Differentiators

Who I Help

Fintech & Mobile

AI & Data Platforms

SaaS & Operations

Dev Tools & Infra

Web & Mobile Platforms

Mobile App Development

AI Chatbots & Assistants

Experience & Proof

How We Work

Production-Grade Standards

FAQ

When should I use RAG vs. fine-tuning?

What’s your AI architecture review process?

How do you measure AI assistant quality (beyond vibes)?

Do you handle multi-tenant security and data isolation?

What timelines should I expect for a first usable release?

How do you integrate AI into an existing Laravel app without rewriting everything?

Can you build tool-using agents with approvals and guardrails?

What are your React Native production quality standards?

What engagement models do you offer?

How do you handle privacy, sensitive data, and compliance constraints?