I build production AI systems — chatbots, RAG assistants, and automation — and I own the data layer underneath.
Anyone can wire up an API call to a model. The hard part — the retrieval, the data plumbing, and the routing that make an AI assistant reliable instead of a demo that hallucinates — is my specialty. RAG is a data problem before it’s an AI problem. US-based, native English, available for calls.
Featured Project #1
KalEdge — AI Product + Distribution
KalEdge is a full AI product: real-time prediction market intelligence powered by Oracle3, packaged as a public-facing platform with premium subscriptions, video content, social proof, and transparent performance tracking — built to attract users, not just to function.
AI Marketing & Content Automation
Two examples of the full AI content production pipeline — platform intelligence and AI-generated promotional content, built end to end with AI-native tools.
KalEdge Platform Overview
KalEdge.tech Free Pick of the Week
KalEdge.tech Free Pick of the Week
Oracle3 analyzed the Knicks vs Spurs matchup and identified a market inefficiency before tip-off. This video demonstrates how KalEdge surfaces prediction market opportunities using AI-driven probability modeling, market analysis, and quantified edge detection.
- Oracle3 AI — market probability analysis
- Prediction Markets — edge identification
- KalEdge.tech — real-time market intelligence
- AI Narration — automated content workflow
- Market Research — quantified decision support
Demonstration content created using AI-assisted production workflows.
Project #2
Desert Glow — AI Business Operations
Desert Glow is a live AI business operating system — AI concierge automation, lead routing, analytics dashboards, revenue attribution, and operational visibility built end to end on Azure. A complete example of AI integrated into real business workflows, not just a demo.
Complete Desert Glow AI Dashboard Walkthrough
A 25-minute walkthrough of the Desert Glow operational intelligence dashboard, covering AI concierge performance, lead flow, revenue attribution, escalations, reviews, services, and system health.
What I Build
Chatbots, RAG assistants, and agents — grounded in your data, not generic answers.
Anyone can call a model API. The hard part is the data layer underneath — the retrieval, plumbing, and routing that make AI reliable instead of a hallucinating demo. That is my specialty.
AI Chatbots & Assistants
Conversational AI for your website or internal team, grounded in your content — not generic answers. Retrieval-backed so it stays accurate, with guardrails and sourced citations.
RAG Systems
“Chat with your documents and data” — vector search over your knowledge base so your team gets instant, sourced answers instead of digging through files.
Cross-Data AI Agents
Agents that pull from your documents and your live business data in a single answer — routing between retrieval and SQL autonomously with LangGraph and tool-calling.
Automation
Pipelines that connect your tools and remove manual, repetitive work — lead routing, follow-ups, and workflows that run reliably without you in the loop.
Social Media Acquisition System
AI-Powered Customer Acquisition Engine
Most businesses post content constantly without knowing what actually generates customers, drives booked appointments, creates qualified leads, or increases revenue.
This system transforms social media into a measurable acquisition engine — combining AI-generated campaigns, operational analytics, attribution tracking, and automated engagement workflows into one connected growth system.
Instead of treating Instagram, TikTok, paid ads, reviews, and inbound messages as disconnected marketing channels, the platform connects every customer touchpoint into a unified intelligence layer that tracks:
- which campaigns generate real leads
- which creatives convert customers
- which channels waste budget
- where high-intent prospects originate
- which content drives booked appointments
- how engagement connects to operational revenue
The system is designed to help businesses scale customer acquisition intelligently — using automation, attribution infrastructure, AI-assisted content systems, and operational analytics to continuously improve conversion performance over time.
This is not just social media management. It is AI-assisted growth infrastructure built for operational visibility, lead generation, and revenue intelligence.
Each creative is tested against acquisition metrics — booked appointments, lead quality, and revenue attribution — not vanity engagement.
Project #3
Cross-Data AI Agent — Retrieval + Live Business Data in One Answer
A LangGraph tool-calling agent that answers questions spanning two very different data sources — unstructured knowledge (treatments, aftercare, policies, FAQs) through RAG, and structured business metrics (revenue, leads, bookings, conversion) through live SQL — and decides which to use, or both, in a single turn. Built as an isolated module on the Desert Glow backend, deployed on Azure.
How It Works
Two tools, one agent that knows when to use which — and how to combine them.
Most chat interfaces either retrieve documents or query a database. The hard part is the data layer underneath: an embedding pipeline, a vector store, and structured queries wired into one agent that routes between them autonomously.
Knowledge Retrieval (RAG)
A search_knowledge_base tool runs pgvector semantic search over embedded med-spa documents — treatments, aftercare, policies, memberships. It grounds “what / how / is this allowed” questions in real source content instead of guessing.
Live Business Data (SQL)
A get_business_metrics tool queries live analytics tables for revenue, leads, bookings, and conversion. It answers “how many / how much / how is it trending” from real numbers, not a static snapshot.
Autonomous Routing
The agent inspects each question, picks the right tool — or calls both and synthesizes one answer. A cross-data question like “which treatments are most popular, and how is revenue trending?” resolves in a single turn.
Project #4
Ayuvara — AI-Native Longevity Funnel + Concierge
A full production funnel I designed, built, and deployed end to end — a longevity brand with an owned Stripe checkout, a grounded AI concierge, and an autonomous ops agent. It re-creates, in an owned FastAPI + Azure stack, the capability two funded businesses hired for — a machine that captures, nurtures, and converts — plus an AI layer no no-code platform can ship. Live on ayuvara.com.
How It Works
A funnel that captures, nurtures, and converts — with an AI layer a no-code stack can’t produce.
The same capture–nurture–convert machine a Kajabi or ClickFunnels build gives you, rebuilt in an owned stack — then extended with a retrieval concierge and an agentic ops layer wired into the funnel’s own data.
Vara — Grounded AI Concierge (RAG)
A retrieval chatbot answers only from the brand’s own longevity guide — top-k retrieval feeds a Claude call that synthesizes a cited, on-topic answer under disclosed-AI, non-medical guardrails. Multi-turn, rate-limited, and spend-capped for a public route.
Ora — Autonomous Ops Agent
A Claude Tool-Runner agent over the funnel’s own data — it searches the guide, reads live funnel metrics, and triages leads across read-only tools. On day one it caught a real production data bug by reasoning over the numbers.
The Owned Funnel Machine
Lead magnet → email capture → 4-step nurture → offer → native Stripe checkout → gated fulfillment — with first-party UTM attribution, a conversion funnel showing per-step drop-off, and checkout-abandonment recovery, all in one owned data layer.
Need an AI chatbot, RAG assistant, or automation built?
Tell me what you’re trying to do — I’ll tell you honestly whether I can help and how I’d approach it.
Let’s Talk ↗About
About Abdi Alamir
I’m an AI Engineer who builds production AI systems — chatbots, RAG assistants, and automation — with one difference most AI freelancers can’t offer: I own the data layer underneath. Anyone can wire up an API call to a model. The part that makes an AI assistant reliable instead of a demo that hallucinates is the retrieval, the data plumbing, and the routing that feed it. RAG is a data problem before it’s an AI problem — and I’ve built the full stack: vector search, live database querying, and agents that decide which source to pull from.
I scope tightly, under-promise, and over-deliver — I’d rather ship one thing that works perfectly than promise ten and disappoint. If you have documents, data, or a manual process you want an AI system to handle, I start small, prove the value, and grow from there. US-based, native English, available for calls.
- AI chatbots & assistants grounded in your content
- RAG systems — chat with your documents & data
- Cross-data AI agents (documents + live business data)
- Automation that connects tools & removes manual work
- Production back ends — Python, FastAPI, PostgreSQL
- Vector search & retrieval (pgvector), deployed on Azure
KalEdge, Desert Glow, and Ayuvara are live examples — production AI systems with retrieval, agents, and analytics I built and deployed end to end. Demos on request.
Have documents, data, or a manual process you want an AI system to handle?
I build production chatbots, RAG assistants, cross-data agents, and automation — grounded in your data, deployed, and reliable. I scope tightly, start small, and prove the value before we grow. US-based, native English, available for calls.