We Better Ai

Founder & CEO

Atilax

Data Scientist & Full Stack Developer

Upwork

As a Top Rated Seller on Upwork, I have a proven track record of delivering outstanding results across various projects, such as Data Science, Machine Learning, and Full Stack Development. My strong communication skills and dedication to meeting deadlines and budgets have led to numerous satisfied clients and a reputation for excellence. I take pride in offering top-notch services and maintaining a high level of professionalism on the Upwork platform, ensuring success for my clients and their projects.

Freelance Data Scientist and Full Stack Developer

Fiverr

As a versatile freelancer on Fiverr, I have successfully completed a wide range of projects, including Full Stack Development, Data Science, and Machine Learning. My in-depth knowledge and skills, along with my commitment to delivering high-quality solutions, have consistently resulted in excellent feedback and long-term client relationships. I continually strive to exceed expectations and provide exceptional service to my Fiverr clients.

Freelance Data Scientist

From private school classrooms in Pakistan to military discipline and AI research labs in Turkey my journey has been anything but linear. But every step shaped who I am: a founder, a builder, a systems thinker.

I’m Abdul, founder and CEO of WeBetterAI. A lean, fast-moving team of developers, designers, and AI engineers helping companies build smarter products. In just 18 months, I’ve grown the team to 10+ and delivered solutions for legal and medtech companies across complex, regulated environments. Our clients trust us because we don’t just ship we maintain, improve, and grow with them.

As a PhD researcher, my work centers on artificial intelligence with a focus on robotics and healthcare applications. I explore how intelligent systems can support doctors in identifying vessel blockages early and making precise decisions that could reduce unnecessary surgeries. At its core, the research is about using AI not just to automate but to assist, to understand, and to care. It’s about enabling technology to serve human life more gently and wisely.

With extensive experience in full-stack development and AI, I’m passionate about creating tools that reduce noise, increase clarity, and scale with purpose. Underneath it all, I care deeply about human organization how teams work, learn, and evolve.

𝐑𝐀𝐆 𝐈𝐬𝐧'𝐭 𝐄𝐧𝐨𝐮𝐠𝐡 𝐀𝐧𝐲𝐦𝐨𝐫𝐞; 𝐇𝐞𝐫𝐞'𝐬 𝐖𝐡𝐚𝐭'𝐬 𝐍𝐞𝐱𝐭.

Traditional RAG pipelines look great in demos; but they often break in production.  Why? Because real users don't ask clean questions or stay within document boundaries.

At WeBetterAI , we've seen it firsthand. Retrieval alone doesn't guarantee relevance. 

Here's what's actually working now: 
 • 🧭 Multi-intent detection — classify the question before retrieval 
 • 🔁 Fallback loops — re-query or re-rank when confidence is low 
 • 🧠 Memory context + behavioral logic — adapt answers over time 
 • ⚙️ Pre/post-processing layers — filters, rules, and structured outputs
RAG was the foundation. 

 But hybrid systems; blending retrieval, reasoning, and memory  are the next leap.
💡 𝐈𝐟 𝐲𝐨𝐮'𝐯𝐞 𝐛𝐮𝐢𝐥𝐭 𝐰𝐢𝐭𝐡 𝐑𝐀𝐆 𝐚𝐧𝐝 𝐡𝐢𝐭 𝐧𝐨𝐢𝐬𝐞, 𝐰𝐡𝐚𝐭 𝐝𝐢𝐝 𝐲𝐨𝐮 𝐝𝐨 𝐧𝐞𝐱𝐭?

Abdul Rehman

Founder & CEO | AI Solutions Delivered in 6 Weeks | PhD Researcher in AI

𝐇𝐨𝐰 𝐖𝐞 𝐂𝐡𝐨𝐨𝐬𝐞 𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐩𝐫𝐨𝐩𝐫𝐢𝐞𝐭𝐫𝐲 𝐦𝐨𝐝𝐞𝐥𝐬 (𝐆𝐏𝐓-𝟓, 𝐂𝐥𝐚𝐮𝐝𝐞, 𝐞𝐭𝐜) 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧.

Everyone's talking about how smart GPT-5 is. But we've learned that intelligence isn't the only metric that matters; fit is.

Sometimes we use GPT-5 for reasoning-heavy, multimodal, or retrieval-augmented tasks. Sometimes we use Mistral , LLaMA 3 , or Gemma 2 for privacy-sensitive or cost-optimized workloads. And sometimes, we blend both, one model reasoning, another grounding.

Our choice isn't driven by hype. It's driven by questions like:
1️⃣ Do we need sub-second responses or deep reasoning? 
2️⃣ Can the data stay on-prem for compliance or client trust? 
3️⃣ Is this conversational, analytical, or generative? 
4️⃣ How much control do we need over fine-tuning and cost? 
5️⃣ Will the user care what runs behind the scenes; or that it works flawlessly?

At We Better AI , infrastructure follows use-case, not trends. That's how we stay flexible, cost-efficient, and future-proof across models.

💡 𝐈𝐟 𝐲𝐨𝐮'𝐫𝐞 𝐜𝐡𝐨𝐨𝐬𝐢𝐧𝐠 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐨𝐩𝐞𝐧-𝐬𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐩𝐫𝐨𝐩𝐫𝐢𝐞𝐭𝐫𝐲 𝐦𝐨𝐝𝐞𝐥𝐬 𝐭𝐨𝐝𝐚𝐲, 𝐰𝐡𝐚𝐭'𝐬 𝐲𝐨𝐮𝐫 𝐝𝐞𝐜𝐢𝐝𝐢𝐧𝐠 𝐟𝐚𝐜𝐭𝐨𝐫, 𝐢𝐬 𝐢𝐭 𝐜𝐨𝐧𝐭𝐫𝐨𝐥, 𝐜𝐨𝐬𝐭, 𝐨𝐫 𝐜𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲?

𝐓𝐡𝐞 𝐇𝐢𝐝𝐝𝐞𝐧 𝐂𝐨𝐬𝐭 𝐨𝐟 𝐔𝐬𝐢𝐧𝐠 𝐏𝐮𝐛𝐥𝐢𝐜 𝐃𝐚𝐭𝐚.

Training on public data helps you move fast until someone else moves faster with the same model. Most teams don't realize how fragile their product becomes when their only edge is prompts + scraped data.

  There's no moat. No defensibility. No reason for clients to stay. At WeBetterAI , we shifted focus to: 
 • Building with private operational data unique to each client 
 • Designing workflows tied to their specific knowledge and processes
  • Structuring information no general model can replicate without context
That's how we've created docbots, pricing tools, and evaluation systems that actually stand apart; because they're built on what makes each business unique.

💼 𝐈𝐟 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦 𝐫𝐞𝐥𝐢𝐞𝐬 𝐨𝐧 𝐩𝐮𝐛𝐥𝐢𝐜 𝐝𝐚𝐭𝐚, 𝐲𝐨𝐮'𝐫𝐞 𝐨𝐧 𝐛𝐨𝐫𝐫𝐨𝐰𝐞𝐝 𝐭𝐢𝐦𝐞. 
 𝐋𝐞𝐭'𝐬 𝐭𝐚𝐥𝐤 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐭𝐨 𝐭𝐮𝐫𝐧 𝐲𝐨𝐮𝐫 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐝𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐲𝐨𝐮𝐫 𝐜𝐨𝐦𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐚𝐝𝐯𝐚𝐧𝐭𝐚𝐠𝐞.

CEO at WeBetterAI | AI Solutions Delivered in 6 Weeks | PhD Researcher in AI

𝐖𝐡𝐚𝐭 𝐀𝐈 𝐓𝐞𝐚𝐦𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐃𝐞𝐯𝐎𝐩𝐬.

AI systems today look a lot like software in 2005 plenty of innovation, but very little discipline. LLM pipelines are getting more complex, yet most teams still lack testing, monitoring, and structure.

And when you skip those, reliability becomes luck; not design. At WeBetterAI, we’ve started borrowing from DevOps to make AI production-ready:
 • Structured evals = unit tests for language
 • Latency thresholds = performance budgets
 • Fallback flows = error-handling logic
 • Prompt versioning = git for conversation logic

It’s not flashy. But it works. Because predictable systems outperform clever demos; every single time.

💼 𝐈𝐟 𝐲𝐨𝐮 𝐰𝐚𝐧𝐭 𝐀𝐈 𝐭𝐡𝐚𝐭 𝐬𝐜𝐚𝐥𝐞𝐬 𝐢𝐧 𝐫𝐞𝐚𝐥 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬, 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝 𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 𝐦𝐨𝐝𝐞𝐥𝐬. 𝐋𝐞𝐭’𝐬 𝐭𝐚𝐥𝐤 𝐚𝐛𝐨𝐮𝐭 𝐡𝐨𝐰 𝐭𝐨 𝐦𝐚𝐤𝐞 𝐲𝐨𝐮𝐫 𝐀𝐈 𝐢𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐚𝐬 𝐫𝐞𝐥𝐢𝐚𝐛𝐥𝐞 𝐚𝐬 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬.

𝐅𝐫𝐨𝐦 𝐧𝐢𝐜𝐞 𝐝𝐞𝐦𝐨 𝐭𝐨 𝐧𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐩𝐫𝐨𝐝𝐮𝐜𝐭.

A thin wrapper can impress in a demo. But only a thick wrapper survives in production. Here's the difference I've seen: 
Thin wrappers show you what's possible. Thick wrappers show you what's dependent.

When your product: 
 ✅ Embeds into real workflows 
 ✅ Respects business logic and policy 
 ✅ Grounds outputs in verified data 
 ✅ Remembers context over time
…it stops being “cool AI.” It becomes infrastructure. That's the line between novelty and necessity. 

The market doesn't reward clever demos; it rewards software that stays useful after the applause fades.

(Full blog in the first comment 👇) 

#LLMProducts #AIProductLeadership #ThickWrappers #WorkflowAutomation #WeBetterAI

Hi there! I’m the CEO and Founder of We Better AI, where our mission is simple, helping startups and small to medium-sized businesses thrive with AI-powered solutions. We’re passionate about turning complex challenges into innovative opportunities, whether it’s through AI research-based products, web and mobile app development, or integrating smart AI solutions into existing platforms.

At We Better AI, we believe in collaboration and building meaningful relationships. Whether you’re looking for an AI solutions or exploring ways to make your business more efficient and impactful, let’s connect! I’m always excited to discuss new ideas and work together to create something remarkable.

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