Your team is doing work a machine should do. I'm Fariya Raza, and I build AI-powered ecosystems that take your team to its full potential.
I plug into the tools you already run. No new dashboards to learn, no copy-paste, no silent failures.
Each one is running for a client today. Hover a step to see what it does; press Play to watch a run, including the retry.
Multi-agent lead research and outreach
Text version: Research → Website intel → ICP score → Opportunity → Outreach → QA gate
A six-agent LangGraph system with one typed shared state: Researcher, Website Intelligence, ICP Scorer, Opportunity Finder, Outreach Writer and QA Validator, streamed into a Next.js front end.
On-brand ad creatives at volume, QA-checked by a vision model
Text version: Brief → Prompt → Generate → Vision QA → Regenerate → Approve
A scheduled n8n pipeline: brief → cached prompt → image generation → vision-model QA against written brand rules → automatic regeneration on failure, capped.
Messy documents in, validated records out
Text version: Upload → Extract → Resolve → Validate → Merge
Multi-format intake (vision model for scans) feeding an 8-tier fuzzy entity resolver built for OCR noise, with a hard validation gate before anything reaches the merged dataset.
From inbox to warehouse, four capabilities, scoped per project. One senior engineer, not an agency.
Agents, LLM pipelines, vision, structured outputs.
Multi-agent workflows with typed shared state, generation pipelines with deterministic validation and capped retries, document intelligence for messy inputs, and rescue of failing prototypes.
n8n, APIs, webhooks, CRM, WhatsApp and inbox.
AI inbox and DM handling with human approval before send, lead enrichment and routing into your CRM, scheduled reports, sheet-driven bulk jobs and quote drafting from email or PDF.
ETL, SQL, reconciliation, BI-ready datasets.
Platform ingestion from ShipHero, Shopify and GraphQL APIs into SQL Server, stored-procedure silver and fact tables, parity audits against legacy systems, Power BI-ready datasets.
SaaS, async GPU inference, customer-facing AI.
Next.js and FastAPI products with queued jobs and polling, self-hosted open-weight models on RunPod GPUs, EU-resident hardened infrastructure where residency matters.
Not a classic agency model. I work in your tools, processes and rhythms, and the system is audited before anyone relies on it.
Hours per week, records per month, error rate. If it can’t be measured, it can’t be automated safely.
State, boundaries, retries and the exact point where a human should approve. The model is one component.
AI proposes; code checks structure, rules and parity. Failures are queued with a reason, never silently passed.
Audited runs, logs and a report the team reads. Then a retainer for tuning as the real world changes.
APIs, automation, agents, data and interfaces, connected into production workflows with validation and human review where it matters. I work as a single senior engineer with clients in the US and EU, from Karachi, with full European overlap.
What makes the work different is the boring part: typed state, deterministic checks, parity audits and honest failure modes. Systems that are still running six months later.
A planning estimate, with the formula shown. Actual savings depend on workflow complexity, review requirements and exception volume.
hours/month = hours/week × people × 4.33
cost = hours/month × hourly cost
FTE = hours/month ÷ 160
Planning estimate only.
“Quick turnaround, was able to quickly understand the challenge and then proved that she can create the solution. Even managed to take my app and implement the solution within it. I’d be happy to work with you again.”
“An exceptional communicator and very proficient in working with AI engineering and automation. Clear recommendation!”
“Reliable, fast-learning, and brings genuine creative thinking to automation. Someone I’d recommend without hesitation.”
“Came up with a creative solution for the problem at hand. Fulfilled her task well and I can recommend her.”
Tell me what’s still being done manually. I’ll tell you whether a machine should do it, and what it would take.
