AI Integration & Automation Consulting

The problem

Your team spends hours on repetitive tasks that follow patterns: classifying documents, extracting data from PDFs, generating reports, processing invoices. You know AI could help, but the gap between "we should use AI" and "we have a working system in production" is where most projects stall.

Off-the-shelf AI tools don't understand your data. Generic chatbots don't integrate with your systems. And hiring an AI team is a six-figure commitment before you see a single result.

What I do

I build production-grade AI systems that integrate directly into your existing workflows. No rip-and-replace. No science projects. Working software that handles real data from day one.

Document processing pipelines. Hybrid OCR + LLM systems that classify, extract, and validate data from invoices, bank statements, contracts, receipts, and any structured or semi-structured document. I've built these for financial services companies processing thousands of documents per week.

Workflow automation. AI agents that handle multi-step business processes: routing documents to the right department, flagging anomalies, generating summaries, triggering downstream actions. Built on reliable infrastructure with human-in-the-loop fallbacks.

Content generation systems. AI-powered content pipelines with brand voice consistency, source verification, and editorial control. Not generic text generation, but systems that produce content your team actually publishes.

Custom AI agents. Multi-agent architectures where specialized AI agents collaborate on complex tasks. I design the orchestration, manage the context, and build the guardrails that keep agents reliable in production.

How I work

Every engagement starts with a 15-minute call to understand your problem. If there's a fit, I propose a fixed-scope implementation:

1. Audit. I map your current workflow and identify where AI creates the most value
2. Build. I implement the solution, integrated with your existing systems
3. Deploy. Production deployment with monitoring, logging, and observability
4. Handoff. Documentation and knowledge transfer so your team owns the system

I use production-grade infrastructure hosted where your data compliance requires it, including Algeria-hosted AI platforms for companies bound by data residency laws (Loi 18-07).

Technical stack

TypeScript, Python, FastAPI, LangChain/LangGraph, OpenAI, Anthropic Claude, open-source LLMs (DeepSeek, Qwen), OCR pipelines (PyMuPDF, Tesseract), Pydantic validation, Sentry monitoring, Langfuse observability.

Proof of work

I've built and shipped AI systems in production:

• Hybrid OCR + LLM Pipeline. A document classification system that processes financial documents with confidence scoring and human-in-the-loop review. Replaced a fragile OCR-only pipeline with a multimodal approach using GPT-4o vision.

• ZATCA Compliance Wrapper. An AI-powered extraction pipeline that reads invoices from a legacy system, validates them against regulatory rules, and submits them to Saudi Arabia's tax authority, all without modifying the client's existing software.