You will get AI Developer for your custom AI, MVP, SaaS, Web, Mobile, Desktop, Platform
Top Rated
Project details
End-to-end custom AI product development from MVP to production-grade systems across Web, Mobile, SaaS, and Desktop platforms. Built to solve real business problems using scalable architecture, intelligent automation, and AI-driven workflows.
The MVP is delivered as a production-ready foundation, supporting real users, data, and integrations from day one. Includes full-stack development, API integrations, multi-tenant SaaS architecture, dashboards, admin panels, and secure authentication.
Core AI capabilities include AI Agents, RAG pipelines, LLM integrations, workflow automation, NLP, computer vision, and intelligent data processing, tailored to your specific use case and data.
Built on a modular, API-first architecture, enabling seamless scaling to enterprise-grade systems without rework. Includes cloud infrastructure, CI/CD pipelines, monitoring, and performance optimization.
Applicable across fintech, health tech (HIPAA), legal tech, edtech, eCommerce, logistics, and business automation.
Designed for scalability, reliability, and real-world impact.
The MVP is delivered as a production-ready foundation, supporting real users, data, and integrations from day one. Includes full-stack development, API integrations, multi-tenant SaaS architecture, dashboards, admin panels, and secure authentication.
Core AI capabilities include AI Agents, RAG pipelines, LLM integrations, workflow automation, NLP, computer vision, and intelligent data processing, tailored to your specific use case and data.
Built on a modular, API-first architecture, enabling seamless scaling to enterprise-grade systems without rework. Includes cloud infrastructure, CI/CD pipelines, monitoring, and performance optimization.
Applicable across fintech, health tech (HIPAA), legal tech, edtech, eCommerce, logistics, and business automation.
Designed for scalability, reliability, and real-world impact.
AI Algorithms
AdaBoost, Large Language Model, Long Short-Term Memory Network, Multimodal Large Language Model, Regression Analysis, Transformer Model, YOLOAI Applications
AI Chatbot, AI Mobile App Development, AI Text-to-Speech, AI-Enhanced Medical Imaging, AI-Generated Music, AI-Generated Video, Anomaly Detection, Facial Recognition, Image Recognition, Object Detection, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, NVIDIA AI Platform, PyTorch, Replit, TensorFlowAI Models
BERT, ChatGPT, Dolly, GPT-3, GPT-4, GPT-J, GPT-Neo, LaMDA, LLaMA, OpenAI Codex, Stable Diffusion, WhisperWhat's included
| Service Tiers |
Starter
$15,000
|
Standard
$30,000
|
Advanced
$45,000
|
|---|---|---|---|
| Delivery Time | 25 days | 40 days | 70 days |
Number of Revisions | 15000 | 30000 | 45000 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | - | ||
Image Upscaling | - | - | |
MLOps | - | - | |
Model Deployment | - | - | |
Model Documentation | - | - | |
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | |
Natural Language Processing | - | ||
NLP Tokenization | - | ||
Pre-Training | - | - | - |
Prompt Engineering | |||
Setup File | - | - | - |
Source Code |
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MC
Melissa C.
Apr 22, 2026
Website Development
Arslan and the entire AlphaSquad team are extremely punctual and determined to deliver every project with excellence. They're great communicators and kept me in the loop every step along the way. All deliveries exceeded my expectations. I'll be back for sure!
BH
Benjamin H.
Apr 17, 2026
AI Music Compliance Platform (Muzaura)
absolutely the best in the business and a pleasure to work with..looking forward to the next phases with Arslan and his team.
MH
Mike H.
Apr 15, 2026
Phase 1 - Senior Full-Stack SaaS Engineer (Web App & Product Infrastructure)
They built the front end of a web app for me and were professional, responsive, and easy to work with throughout the project. Communication was strong, they were open to feedback, and they stayed engaged when changes or fixes were needed. I appreciated that they were willing to collaborate, work through issues, and keep things moving rather than becoming difficult when details evolved.
They handled the project in a structured way, were generally organized, and did a good job taking direction while also offering helpful input when needed. They were flexible, patient, and committed to getting the project to a solid place.
Overall, I’d say they are a strong team for front-end web app development, especially if you value clear communication, steady progress, and a team that will stay involved through revisions and problem-solving.
They handled the project in a structured way, were generally organized, and did a good job taking direction while also offering helpful input when needed. They were flexible, patient, and committed to getting the project to a solid place.
Overall, I’d say they are a strong team for front-end web app development, especially if you value clear communication, steady progress, and a team that will stay involved through revisions and problem-solving.
DS
David S.
Apr 13, 2026
Cross-Platform Endpoint Agent Developer (Go/Rust) – Secure SaaS Project
BH
Benjamin H.
Jan 26, 2026
Lead Developer / Technical Architect — AI Music Compliance Platform (Muzaura)
Great experience working with Arslan and the team. Strong technical execution on a complex architecture, clear communication throughout, and thoughtful handling of edge cases. Milestones were delivered as agreed, and collaboration was smooth and professional. Would be happy to continue working together on future phases.
About Arslan
Full Stack Developer | AI Development | AI Agents, SaaS, EHR, RCM, ERP
100%
Job Success
Kuala Lumpur, Malaysia - 3:15 am local time
I’ve spent the last 18 years architecting & delivering production data-intenstive software systems across diverse business verticals, responsible for the code, data engineering, infrastructure designs, integrations, and architectural decisions behind enterprise platforms.
For greenfield systems, I start with the business model, operational workflows, domain boundaries, data ownership, integrations, infrastructure, expected scale, and how the product needs to evolve.
For brownfield modernization, I first understand what is already running: the codebase, databases, services, infrastructure, deployment processes, technical debt, operational dependencies, and the decisions that shaped the system over time. From there, I determine what should be retained, modernized, decoupled, migrated, automated, or replaced without destabilizing the systems the business already depends on.
I do not force AI into every workflow. Some processes should remain deterministic. Others are better suited to traditional machine learning, retrieval, reasoning, voice, vision, or document intelligence. Agentic execution becomes valuable where workflows require dynamic planning, coordination, tool use, or decisions that cannot be represented efficiently as a fixed sequence.
The real architectural challenge is deciding where AI belongs, how much autonomy is appropriate, and what should remain under deterministic system control.
The same principle applies to AI Models. I do not assume one model or provider should become the permanent foundation of an enterprise platform. A complex reasoning task may require a frontier API model; high-volume workloads may be more economical on smaller private models; sensitive workloads may require private VPC or on-prem inference; computer vision may operate at the edge; and mobile or desktop applications may benefit from on-device models for latency, privacy, or offline operation.
I therefore design model portfolios and inference architectures based on capability, modality, privacy, residency, latency, reliability, risk, and operating cost, so the application remains stable even as models, providers, capabilities, and economics change.
Once an agent can read enterprise information, execute code, update records, call external systems, modify infrastructure, or trigger business processes, it becomes an active participant in the production environment.
I design these systems around bounded autonomy: agents receive only the context, tools, permissions, and execution authority required for their role. High-impact actions can remain deterministic, approval-driven, or isolated behind controlled services. Actions should be observable, auditable, and recoverable, with autonomy expanding only where the architecture has sufficient controls and evidence to support it.
This is particularly important for regulated and security-sensitive businesses. AI models, agents, retrieval systems, enterprise data, tools, and engineering automation must operate inside architectural boundaries aligned with modern security, privacy, auditability, and risk-management expectations.
The same architecture extends to software delivery. AI agents can accelerate codebase analysis, implementation, refactoring, data engineering, testing, migrations, infrastructure automation, CI/CD, documentation, monitoring, incident response, and ongoing maintenance. I use that capability to increase engineering velocity inside governed repositories, isolated execution environments, controlled tools, deterministic pipelines, and reviewed production boundaries.
For many enterprises and startups, this evolves into an AI Operating System; a governed architecture connecting applications, enterprise data, knowledge, workflows, AI models, agents, integrations, and human decision points. I design these systems across Healthcare and EHR/EMR, Fintech and Payments, ERP, CRM, LMS, Ecommerce, Marketplaces, Manufacturing, Construction, Logistics, Real Estate, Professional Services, and other enterprise platforms.
Whether you need to design a new AI-native platform, modernize a legacy enterprise system, introduce agentic workflows, restructure software delivery around an AI engineering workforce, prepare enterprise data for AI, deploy private/on-prem/edge models, reduce inference and operating costs, strengthen AI security and governance, or productionize AI with MLOps, LLMOps, and AgentOps, feel free to reach out!
Steps for completing your project
After purchasing the project, send requirements so Arslan can start the project.
Delivery time starts when Arslan receives requirements from you.
Arslan works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements & AI Discovery
Define use cases, data sources, workflows, and success metrics. Review SOW/PRD or run a short discovery.
AI & System Architecture
Design overall architecture, data pipelines, model/LLM strategy, RAG setup, APIs, and infrastructure.