AI systems. Automation. Software delivery.
I design and build AI workflows, automation systems, and production software for client teams that need real delivery, not slideware. I work directly with founders and product teams who need clean execution, reliable delivery, and an engineer who can move from strategy into shipped software.
Project brief
Describe what your team needs built.
Share the problem, timeline, current stack, and what success should look like. I use this to understand fit, scope the work, and follow up with the right next step.
Three service lanes built for client delivery.
The offer is focused on AI systems, workflow automation, and software engineering so clients can move from idea to implementation without splitting the work across multiple specialists.
- Applied AI systems with strict validation and bounded generation
- Workflow automation using n8n, LangChain, LangGraph, and production orchestration patterns
- Full-stack software delivery across frontend, backend, and cloud workflows
Build AI-powered product features, internal copilots, evaluation flows, and retrieval-backed systems with reliability controls.
Automate lead intake, ops handoffs, internal review, reporting, and multi-step business processes with human-in-the-loop control.
Ship the underlying product surface around the workflow: frontend, backend, admin tooling, APIs, and integration-heavy systems.
- ResBuilder
- Research Assistant
- Teaching and lab design at GUC
Built on shipped systems, serious implementation, and deep hands-on practice.
The work shown here is meant to answer a simple question: can Karim actually deliver the kind of systems he is selling? The answer needs to come from shipped work, technical depth, and repeatable execution.
Hands-on engineering with a delivery mindset.
I focus on projects that need more than prototype energy. The goal is useful software, clean integrations, and work that holds up once a client team starts relying on it.
Translate the business problem into a concrete workflow, delivery scope, integration map, and measurable success criteria.
Implement the orchestration layer, data model, provider integrations, and operator-facing controls before polishing edge automation.
Add validation, observability, auditability, and human-review gates so the workflow can survive real production traffic.
- I build the workflow and the product surface around it
- AI outputs stay bounded by validation and review gates
- Integrations are designed for handoff, retry, and auditability
- Client-facing systems should ship with maintainable operational logic
Send the brief. I’ll review it and get back to you.
Use the brief to describe the problem, current setup, deadlines, and budget range. I use that context to assess fit, shape the next conversation, and recommend the right starting point.
Available for scoped builds, workflow audits, and delivery-heavy client work across AI systems, process automation, and software engineering.