I build production AI with RAG and multi-agent systems to automate complex workflows and unlock value from large-scale data.

I’m Okafor Chibuzor Authur, an AI Engineer specializing in LLM systems, Retrieval-Augmented Generation (RAG), and multi-agent AI architectures. I build production-grade AI platforms and scalable automation workflows that help businesses unlock value from large-scale data, streamline decision-making, and operationalize AI reliably.

What you get working with me is a partner who can take an AI solution from concept to production using proven, production-ready patterns. I design systems that are scalable, operationally practical, and aligned to measurable business outcomes.

How I help you increase ROI and reduce cost
1. Automate complex workflows that would otherwise require significant manual effort and headcount.
2. Improve retrieval accuracy and response quality with RAG (document chunking, embeddings, retrieval, and scoped question answering), reducing time spent searching, verifying, and rewriting information.
3. Enable intelligent, scalable operations through multi-agent workflows (including LangGraph-based orchestration), so repetitive and high-volume tasks are handled faster and more consistently.
4. The result is faster turnaround times, reduced operational overhead, and better utilization of your existing business data, translating into stronger output per dollar spent.

What I build and deliver
1. RAG-powered document intelligence systems: upload documents, extract text, chunk and embed into vector stores, retrieve relevant context, and generate accurate AI answers and drafts.
2. Agentic automation workflows: multi-step systems where AI agents can reason, collaborate, and take structured actions (LangGraph-based workflows).
3. Production AI backends and platforms using FastAPI, PostgreSQL (pgvector), Redis, and Celery for asynchronous and scalable processing.
4. Deployable solutions to AWS, GCP, Vercel, and Docker-based infrastructure designed for real-world usage.

Where I’ve delivered value
I’ve delivered high-impact AI solutions across e-commerce, customer support, and internal automation, and I’m comfortable working under strict client confidentiality when needed.

Work Terms

Operating procedure
1. Milestone-based delivery: we align on scope and success criteria, I deliver a first iteration, then we iterate quickly based on feedback until acceptance.
2. I confirm environment and configuration requirements early, especially when runtime behavior depends on secrets, callback URLs, payment gateway configuration, Redis setup, and frontend-backend host alignment.
3. For projects with multiple subsystems (like orchestrated backends plus specialized services), I document operational concepts clearly to reduce operator confusion, particularly when multiple job-handling concepts exist.

Payment preferences
1. Comfortable with milestone payments tied to deliverables (for example: discovery/design - implementation - hardening/hand-off).

Turnaround time
1. Turnaround depends on scope and integration points.
2. Systems with many external services and infrastructure components may require extra time for configuration, validation, and stability checks due to multiple independent failure points (storage, DB, cache, and other service dependencies).

Availability
1. I can commit to scheduled working blocks and planned check-ins, coordinated around the project timeline and delivery milestones.

Communication channels
1. Async-first with clear written updates, plus scheduled calls as needed.
2. I can align with whatever collaboration tools your team uses.

Delivery and handoff
1. Working increments mapped to milestones
2. Configuration notes (key environment and service URL knobs)
3. Operational notes to support maintainers and operators