Freelancers using pandas in Houston

Freelancers using pandas in HoustonFreelancers using pandas in Houston

Projects People

Full Stack Developer | Project Manager | Executive Assistant

54: Followers

Full Stack Developer | Project Manager | Executive Assistant

Cover image for Full Stack Approach

Full Stack Approach

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Working on AI Face calibration

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Cover image for AI & Full-Stack Developer | LLMs, Machine Learning

AI & Full-Stack Developer | LLMs, Machine Learning

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Cover image for We’re entering a phase where

We’re entering a phase where AI products won’t win just because of the tech… They’ll win because of distribution. At Kokorick, we’re focused on one thing: Turning AI tools into growth engines. With Warmo, it’s not just about sending emails— it’s about building a system that learns: • Who converts • What messaging works • Which audiences actually respond The real moat isn’t the model. It’s the data you collect over time. That’s where things get interesting. #AIGrowth #StartupGrowth #GrowthStrategy #AISaaS #LeadGeneration #EmailOutreach #Automation #BuildInPublic

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Al Kaizar Sappayani

AI Engineer(AI Agents, LLM Apps, Chatbots, RAG)

New to Contra

AI Engineer(AI Agents, LLM Apps, Chatbots, RAG)

Cover image for AI Generated Data Analysis Problem: Weekly call-center ops d...

AI Generated Data Analysis Problem: Weekly call-center ops data needed actionable synthesis; manual analysis was slow and inconsistent. Solution: Built an LLM data analysis agent: ingest call data, run NLP + aggregations, detect anomalies, and generate a weekly executive PDF with insights, metrics, and recommended actions Metrics Automated weekly PDF; similar setups cut analysis time ~90% and improved decision turnaround (one related client saw 17% revenue lift over 7 months) Tech: Python ETL, Pandas, NLP, GPT-4o, Airflow Result: Consistent reports that surface issues fast and drive quicker corrective action.

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As part of

Marketing Assistant Bot As part of my work in AI-driven automation, I built a Marketing Assistant Bot using Relevance AI to address a major challenge in customer engagement: inefficient lead handling and inconsistent follow-ups. Businesses often struggle with manually qualifying leads, responding to competitor comparisons, and guiding potential customers through the sales funnel. The lack of automation resulted in lost opportunities, delayed responses, and high drop-off rates. To solve this, I developed an assistant on Relevance AI

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Cover image for AI Sales Agent Problem: Customers needed fast, accurate answ...

AI Sales Agent Problem: Customers needed fast, accurate answers on courses/licensing/policies from a huge, changing catalog; support handled repetitive questions and compliance is state-specific. Solution: Built RUBI, an embedded RAG purchase advisor in using ETL + multimodal indexing and hybrid retrieval (metadata + vector + structured lookups) with cited, grounded answers, purchase flows, analytics, and tuning loops. Metrics: 695,042 conversations; 2.6 Qs/convo; feedback 6% up / 94% down; ~10s to first token, ~11s complete; KB >100k structured rows.

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Cover image for AI - powered insurance Agent Problem: Prior auth was manual—...

AI - powered insurance Agent Problem: Prior auth was manual—staff reviewed patient docs to predict approvals, making the process slow and resource-heavy. Solution: Built a HIPAA-compliant AI auth system: extract clinical data (OCR + clinical NLP), form a structured EHR-like record, evaluate insurer rules, and generate approval suggestions; added voice input workflows via STT/TTS plus secure storage and audit logging. Metrics: Reduced turnaround time and improved SLA adherence (similar projects reached ~98%). Result: Automated major parts of auth decisioning while staying compliant and auditable. Skills: AI Speech-to-Text AI Agent Development AI Builder AI Audio Generation AI Security

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