AI that reaches production.
An AI-first development practice building agents, retrieval systems, and integrations for businesses that want AI doing real work, not demos.
Most AI pilots never leave the demo.
A chatbot that answers wrong. A prototype no one wired into the CRM. A model nobody is monitoring. The gap is not the AI. It is the engineering around it.
Tool access, human approval points, grounding in real data, and someone who owns it in production.
Four things, done properly.
Agents & workflow automation
Multi-step agents with tool access and human approval points, running the repetitive work your team does by hand today.
Grounded answers over your data
Retrieval over your documents, tickets and records, with citations that can be audited. No hallucinated policy.
Integration with what you already run
Payments, CRMs, calendars, cloud APIs. AI wired into the systems in place, not a new tool nobody opens.
Production hardening
Evals, monitoring, cost controls and a clean handoff, so the system keeps working after launch.
Have a workflow in mind? Bring it to a 30-minute call.
Book a callOne workflow. Two weeks. Real data.
Find the workflow that costs the most time.
A 30-minute conversation to map where hours go today and pick one process worth automating first. No deck, no proposal theatre.
A working agent against your real data.
Not slides. A prototype your team can try, wired to a copy of your actual documents or records, with the failure cases visible.
Wired into the tools already in place.
CRM, payments, email, calendar. Human approval points where the stakes are high. Nothing new for staff to learn.
Monitored, measured, handed over.
Evals on every change, cost and escalation tracking, documentation, and a clean handoff or a light retainer. Your choice.
What changes when the work runs itself.
Invoice follow-up, by hand
- Finance checks Stripe every Monday
- Drafts 40 chasing emails from a template
- Tracks replies in a spreadsheet
- Misses the ones that slip past week two
Invoice follow-up, with an agent
- Checks overdue invoices daily
- Drafts each reply with context from the CRM
- Waits for a one-click approval
- Logs the outcome, schedules the next check
Backend engineer, turned AI-first builder.
Ibrahim Bashir builds and runs every system described on this page. Backend and API work came first: payments, event platforms, cloud integrations. Then an AI product that put conversation, retrieval and agents in front of real users, and the conviction that most AI projects fail on engineering, not on models.
I take on a small number of engagements at a time, so the person on the discovery call is the person writing the code and answering the phone after launch.
- Base
- Remote · worldwide clients
- Focus
- Agents, RAG, integrations
- Engagements
- Few at a time, by design
Things people ask before the call.
Does this need an in-house ML team?
No. The systems are built on hosted models and standard infrastructure. What your side needs is one person who knows the workflow and can approve outputs during the first weeks.
Which models are used?
Whichever fits the task and the budget. Claude and OpenAI models for reasoning, smaller or open models where cost or privacy calls for it. Model choice is a decision made with you, not for you.
What happens to company data?
Data stays in your accounts wherever possible. Retrieval runs over your own storage, keys are yours, and nothing is used to train anything. A short data-handling note is part of every engagement.
How does an engagement start?
A 30-minute call, then a two-week prototype scoped to one workflow. If it earns its keep, integration follows. If not, you keep the findings and there is no next invoice.
What if the workflow is unusual?
Most are. The process is built around one specific process at a time precisely so it doesn't depend on a template.
Start with one workflow.
Thirty minutes. No deck, no pitch. Bring the process that eats the most hours and leave with a plan.
Prefer email? ibrahimbashir370@gmail.com