I build production AI for small and mid-sized teams. Chat assistants that actually know your product. Data agents that answer questions your dashboards can’t. Automation for the work your team keeps doing by hand.
A support and pre-sales assistant grounded in your own docs, tickets, and product data. Cites its sources. Escalates when it should. Deploys to your site, your Slack, or your app.
Ask in plain English. Get a real answer from your actual database. Useful when the question is one-off, the shape keeps changing, or building another chart is not worth it.
The unglamorous work your team repeats every week. Classifying inbound leads, extracting fields from PDFs, drafting first-pass replies, matching records across systems.
Every time a new supplier came in, no one really knew if that vendor was already in the system. The same company kept getting onboarded twice under slightly different names, creating duplicate payment risk and forcing procurement to reconcile records by hand. On a list of 80,000+ suppliers, catching a duplicate visually is impossible.
Built a live check that runs the moment a new supplier is submitted. It compares the new name and details against every existing vendor, catches variations that look different but mean the same company (“Acme Corp.” vs “ACME Corporation Ltd”), and either flags a likely match for review or triggers the vendor-invitation step automatically when nothing matches. Plugs directly into the tools procurement already uses, so no new interface to learn.
Every new supplier now screened against 80,000+ existing records in under two seconds. Roughly 1 in 10 submissions turn out to be duplicates that would have slipped through before. The clean path is fully automated, so procurement only touches the requests that actually need judgment.
Everyone in the business had questions that needed answers from the company’s data. Only the data team could pull them. A simple question turned into a ticket, a ticket turned into a wait, and by the time the answer came back the moment had passed. Meanwhile the data team was drowning in requests instead of doing real engineering work.
Built a system that lets anyone type a question in plain English and get the answer straight from the company’s database. It understands the shape of the data, works out the right query behind the scenes, and returns results in seconds. Locked down so nothing can be accidentally broken — only safe, read-only questions get through. Runs inside the company’s own cloud so their data never leaves.
Operations, finance, and business leads now get their own answers in seconds instead of waiting days on a data-team ticket. The engineering team stopped being an ad-hoc query desk and got their focus back for real product work. Every unsafe query gets blocked before it reaches production.
On the factory floor, quality problems were only caught when someone eventually noticed them on a dashboard, sometimes days later. The information needed to spot issues early lived in three separate systems no one looked at together: work orders in one place, inventory in another, quality history in a third. By the time issues surfaced, the damage was already downstream.
Built a system that watches all three data sources continuously and pulls them into a single unified view. The moment something drifts out of spec, the right person gets a Microsoft Teams alert automatically — no dashboard-watching required. Plant leaders also got a simple exploration tool for asking questions about their operation directly, instead of waiting on a weekly report.
Quality issues that used to be caught days later now surface in minutes, straight in the Teams channel of the person who needs to act. Three previously disconnected systems now read as one operational picture. Plant teams shifted from reactive dashboard-checking to proactive alerts — which is where the actual cost savings live, because catching a bad batch early is worth vastly more than catching it late.
Both demos run on Streamlit Cloud and may take a few seconds to wake up on first visit.
Thirty minutes. We look at the problem, the data, and what a good outcome would look like. If AI is the wrong tool I will say so on the call.
Ten to fifteen business days for most projects. You get something you can click through and put in front of a real user, not a slide deck.
Deployed to your infrastructure or mine. Code, prompts, and evals are yours. Written handover doc so your team can maintain it.
Monthly retainer for tuning, evals, and adding capabilities. Optional. Most clients take it for the first three months.
I’m Mobeen, an AI engineer who builds systems that connect language models to real business data. Chatbots that answer from your documents. Agents that query your databases in plain English. Automations that remove manual steps from your workflows. My background is in applied AI: semantic search, multi-agent systems, and enterprise integrations across platforms like Azure, Databricks, and ServiceNow. I care less about which model is trendiest and more about whether the system actually holds up when someone other than me is using it.
I work solo and by design keep things small. Every project gets built, tested, and delivered by me directly, not handed off between account managers and junior developers. That means faster turnaround, clearer communication, and no translation loss between what you ask for and what gets built. If you need a production-grade AI feature without the overhead of hiring a full team, that’s exactly the gap I fill.
The more specific, the better. What are you trying to do, what data is involved, and what would “working” look like. I reply within one business day.
I’ll reply within one business day. If it’s urgent, email karimaisolutions@gmail.com directly.