Your team shouldn't spend hours copying data, updating systems, sending repetitive emails or manually processing information.
We'll identify practical opportunities to automate them using AI, APIs and workflow automation.
TL;DR: Osama is a Full Stack AI Engineer who builds custom AI agents and RAG systems. He automates manual B2B workflows (such as prospect research and lead qualification) reducing research time from 20+ minutes per lead to seconds using Python, FastAPI, and Google ADK.
B2B cold outreach lives or dies on personalization. Real personalization takes 15 to 30 minutes per lead: scrape the site, read recent news, infer pain, write a tight email. At scale, teams either skip it and tank reply rates, or burn out doing it and churn SDRs.
Four agents on Google ADK with Gemini. URLReaderAgent scrapes the prospect's site. SearchAgent pulls recent news and announcements. CodeAgent handles any data the first two can't. SalesOrchestratorAgent synthesizes everything into a concise, non-robotic cold email. Default pitch is your AI services. Swap the prompt in app.py to pitch SEO, lead-gen, web dev, anything B2B.
Prospect research collapses from 20 minutes to about 15 seconds. Cold-email reply rates lift because every email is grounded in live context. The orchestrator pattern transfers: same shape works for inbound triage, RFP drafting, support escalation, any workflow that needs live context plus a written artifact. Premium tier (client work) adds CRM hooks, bulk processing, custom knowledge bases, and a human-in-the-loop approval UI.
| Feature | Manual Outreach | AI Orchestrator |
|---|---|---|
| Time per Lead | 20+ minutes | ~15 seconds |
| Personalization | Often generic | Highly contextual |
| Scalability | Low (Burnout risk) | Infinite |
I skim your docs, your CRM, your slowest workflow. You leave the call with a one-page note: here is what I would build first, here is what I would not, here is the rough cost. Free. No deck.
Inputs, model choice, retrieval design, eval set, latency budget, failure modes, success metric. Tight. Two rounds of edits max. Anything that does not serve the metric gets cut.
FastAPI service exposing REST APIs, pgvector on your infra, custom ETL pipelines pulling your docs and CRM, eval harness running against your labeled examples. You get a URL and a Loom. We iterate weekly.
Prompt injection tests, refusal behaviour, citation accuracy, retry/backoff, observability, kill switches. The unglamorous work that decides whether the system actually survives Monday morning.
Canary at 5%, watching logs, watching eval drift, paging me when something looks off. Two weeks of post-launch on-call is in every contract, no extra invoice.
Your team owns it. I write the runbook, record the walkthrough, sit on two calls while your team takes over. If you want me on standby, the retainer is fixed, capped, and cancelable.
Retrieval Augmented Generation (RAG) is an AI architecture that connects Large Language Models (LLMs) to your private business data, ensuring AI responses are accurate, grounded, and specific to your company.
A typical custom AI agent project takes 5 to 7 days for a functional prototype, followed by 1 to 2 weeks of production hardening to ensure sub second latency and 99% reliability.
Yes. We use Python and FastAPI to build custom integrations with Salesforce, HubSpot, Slack, and any internal tool that has an API or database.
Absolutely. We route data through enterprise API endpoints (like Vertex AI or OpenAI's API) which guarantee zero training on your data. For strict compliance, we can deploy open-weights models on your own infrastructure.
We build strict guardrails and human-in-the-loop approval UI for high-stakes workflows (like sending emails or updating financial records) so the AI acts as a drafter, not a rogue agent.
You only pay for the API tokens you use (often pennies per run) and standard cloud hosting. If you want me on standby for tuning, I offer a flat, cancelable monthly retainer.
If there is a workflow eating hours your team hates, bring it. I will tell you whether an AI system is the right fix or not, on the spot.