Yasir I.

Senior AI engineer specializing in AI integration and AI chatbot development. I put LLMs, agents and automation inside the systems your business already runs on, and I build them to stay accurate, secure and affordable once real users hit them. Anyone can get an AI demo working. The harder part is connecting it to your real data and tools, keeping answers correct, holding costs down at scale, getting through compliance review, and proving quality with tests instead of gut feel. I handle that work from architecture to deployment. What I build - AI integration. I add LLMs and AI APIs to existing software, CRMs, e-commerce platforms, payment systems and internal tools, so AI works inside the workflows your team already uses. - AI chatbots and conversational agents. Streaming chatbots with function calling and tool use that pull live business data, take real actions and send each request to the right model based on its complexity. Quality stays high and the bill stays low. - RAG and knowledge assistants. Retrieval pipelines with embeddings and vector search, so answers come from your documents, policies and protocols instead of guesses. - Business process automation. Multi-step workflows across e-commerce, CRM, email marketing and vendor APIs that take repetitive manual work off your team. - Guardrails and compliance. Production prompts, response checks against live data and rule enforcement for regulated industries such as healthcare. - Evaluation and architecture review. I audit an existing AI system, find what limits its quality, and build repeatable test sets so every improvement is measured. - Private and local AI. Local models through Ollama (Llama, Qwen) and Whisper speech-to-text for projects where data cannot leave your environment, plus fine-tuned classifiers for narrow, high-volume tasks. - Data pipelines and scraping. Scheduled extraction, change detection and syncing between platforms. Stack I work in daily Python, TypeScript, JavaScript, SQL, PHP, FastAPI, Express, Node JS, React, Next JS, Vite, Tailwind CSS, PostgreSQL (Prisma, Drizzle), Supabase, MongoDB, SQLite, Claude through AWS Bedrock, OpenAI API, Ollama, AWS (ECS Fargate, ECR, S3, RDS, CloudFront, Cognito, Secrets Manager, WAF, EventBridge), Docker, Vercel, GitHub Actions CI/CD, Vitest, Zapier and Make. My approach I write agent logic directly instead of stacking heavy frameworks on top. That gives me tighter control over prompts, tool use and guardrails, and fewer surprises in production. When a project already uses LangChain or similar tools, I work with them. How an engagement runs 1. A discovery call to define the goal and what success looks like 2. Architecture and scope agreed before the build starts 3. Working demos on a regular schedule 4. Tested, documented code with CI/CD and a deployment your team can maintain 5. A clean handoff and a practical plan for what comes next Let's talk about your project Send me the problem you want solved, your current setup and what a good result looks like. I'll reply with a straight assessment of the approach, the timeline and whether AI is the right tool for the job.