Freelance AI Agent Engineers in Houston
Freelance AI Agent Engineers in Houston
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Kokorick AI
Houston, USA
AI Agents | LLMs, Computer Vision & Full-Stack Dev
73
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AI Agents | LLMs, Computer Vision & Full-Stack Dev
7
🚀 Most AI outreach tools stop at writing messages. Warmo.ai (http://Warmo.ai) actually helps you find prospects, personalize outreach, and start meaningful conversations—all from one platform. Whether you're a freelancer, agency owner, recruiter, or founder, it's built to save hours of manual work and help you book more meetings. 🔥 Best part? The first users can use it completely FREE. If you've been waiting for an AI tool that actually helps you grow your business instead of just generating text, now's the time to try it. 👉 Check out Warmo.ai (http://Warmo.ai) and claim your free access while it's available. #AI #Sales #LeadGeneration #Outreach #Freelancer #Agency #Startup #BusinessGrowth #Automation #Contra
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Turning long documents into decisions. Organizations rely on long reports to make critical decisions, but most insights stay buried. This AI Decision Intelligence system turns raw documents into predictions, evidence scoring, and clear insights in minutes. If AI could read your reports for you, what decisions would you automate first? #AI #MachineLearning #DecisionIntelligence #EnterpriseAI #DataAnalytics #Automation
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🚨 Turning CCTV Into a Crime-Prevention AI System We helped build Aegis FaceGuard, an AI-powered security platform designed to prevent armed robberies before they happen—not after. In high-risk regions like Colombia, traditional CCTV only records. This system predicts, detects, and reacts — instantly. Here’s what it catches in real time: 🪖 Helmet detection 🔫 Gun detection 🎠Mask detection 👤 Suspicious posture ⚠️ Threat movement patterns When danger appears, the system automatically: Triggers alarms Alerts police Notifies the owner Saves evidence — all within milliseconds This is security before the crime, not after. If you're building AI for public safety, retail protection, or real-world automation, I’d love to connect. 🔥 #AI #ComputerVision #SecurityTech
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AI Legal Case Analysis — Expert Evidence Prediction This project demonstrates an AI tool that analyzes legal cases and predicts how a judge may view expert testimony. Using structured case data and LLM reasoning, the system compares past rulings involving Dr. Bailey with a new case scenario. By parsing the actual judgment text: “I accept Dr Bailey’s evidence…” The model evaluates patterns in credibility, cross-examination outcomes, and judicial language. It then generates a predicted ruling, concluding that the judge would likely consider Dr. Bailey’s expert evidence credible again. This showcases: Smart case-to-case comparison Expert-evidence credibility scoring LLM legal reasoning Structured JSON analysis #LegalTech #AIforLaw #CaseAnalysis #LLMReasoning #ContraPortfolio
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329
AI Agent Engineer
(6)
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Madison Andrews
Houston, USA
Knowledge systems. Brand governance. Agentic workflows.
New to Contra
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Knowledge systems. Brand governance. Agentic workflows.
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A system design for an AI agent that monitors LinkedIn for engagement opportunities — industry news, thought leadership posts, relevant conversations — and drafts comments and reshares for human review before posting. The architecture includes: topic and account monitoring criteria, daily scan logic, draft generation with brand voice alignment, a review queue delivered via email or Slack, and approval workflows that ensure nothing posts without human sign-off. Designed for marketing teams and executives who want consistent LinkedIn visibility without the daily time investment. Phase 2 extends the system to personalized recommendations for individual employees. My role: System design, workflow architecture, and productization.
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The system prompt that governs an AI content assistant's behavior — ensuring it searches before responding, follows brand voice, respects compliance guardrails, and never hallucinates clinical details. This isn't a simple instruction set. It's a layered governance framework covering: data usage rules, response routing logic, format fidelity, voice and tone calibration, compliance guardrails, and fallback behavior when data is missing. The prompt was refined through iterative testing with the marketing team, balancing strictness (no invented claims) with usability (natural, helpful outputs). The result: an AI that behaves like a knowledgeable teammate, not a generic chatbot. My role: Prompt architecture, iterative testing, and documentation for long-term maintenance.
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A complex behavioral healthcare organization needed a way to produce high-volume marketing content — eBlasts, social posts, blog drafts — without sacrificing clinical accuracy or brand consistency. Manual processes were slow, and the risk of off-brand or non-compliant content was constant. I designed and deployed an AI-powered content assistant that queries the organization's Notion knowledge base before every response. The system enforces brand voice, messaging guardrails, and compliance standards automatically — so the marketing team gets governed first drafts, not generic AI output. The tool handles content generation, content review, and brand training. It's now being expanded to include SEO/AEO-driven content calendar automation. Results: - 4–12 hours/month saved on content creation - Reduced risk of costly reprints and off-brand messaging - Marketing team trained and using the tool independently My role: Discovery, system architecture, Notion database design, prompt engineering, integration, documentation, and team training.
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A structured Notion workspace that serves as the single source of truth for a complex behavioral healthcare organization's marketing team. The system includes interconnected databases for programs, facilities, approved messaging, and content examples — each with properties designed for AI retrieval. When the AI content assistant needs to generate or review content, it queries this knowledge base first, ensuring every output aligns with brand standards and clinical accuracy. Architecture decisions included: relational linking between programs and approved messaging, searchable aliases for program name variations, messaging guardrails stored at the program level, and a content library tagged by format, audience, and goal. My role: Discovery, information architecture, database design, governance logic, and documentation.
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42
AI Agent Engineer
(3)
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Al Kaizar Sappayani
Houston, USA
AI Engineer(AI Agents, LLM Apps, Chatbots, RAG)
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AI Engineer(AI Agents, LLM Apps, Chatbots, RAG)
20
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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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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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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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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169
AI Agent Engineer
(1)
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Anu Raghavan
Houston, USA
AI Chatbots & Voice Agents for Technical Companies
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AI Chatbots & Voice Agents for Technical Companies
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1-Click IG Post Streamlit App Development
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Voiceflow Apartment Agent Chatbot
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Custom Copilot Creation
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Custom GPT Implementation
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8
AI Agent Engineer
(1)
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Bailee Eden
Houston, USA
I turn ambitious ideas into premium digital experiences.
12
Followers
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I turn ambitious ideas into premium digital experiences.
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nir- generative brand infra, automated research pipeline, bespoke branded agent fleet and native tooling integrations; Implemented into a single, beautifully branded command layer
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nir — The Command Layer: Work From the Same World You Ship
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nir- Seasonal Campaign Infrastructure
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nir- Product Design and Mockup
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125
AI Agent Engineer
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Preston Cardwell
Houston, USA
CPA & Financial Consultant | Expert in Automations
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CPA & Financial Consultant | Expert in Automations
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Website Development & Automation
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HighLevel Implementation
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Web Application Development
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Task Management Automation
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6
AI Agent Engineer
(10)
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