Mario Magdy

• • Delivered an end-to-end workflow that turns natural-language requests into extracted parameters, executes the request through an LLM layer (Flask/Pandas), and returns explainable outputs and video context, working closely with a cross-functional team. Validated the migration by running two parallel branches (legacy AssistJS+Flask and the new FastAPI branch) side by side.

• • Built a team-owned LLM evaluation harness (v0.1 to v0.2) using Group Questions and voting-based consensus to approximate ground truth when labels were missing, and formalized two metrics (Accuracy and Stability across paraphrases) to guide model selection. Reduced hallucinations by 65%.

• • Implemented prompt-robustness regression tests by generating paraphrase groups per intent and enforcing that different wording leads to the same intent and the same retrieval/query logic.

• • Scaled stress testing with a Griller flow that generates 9 rephrase variations per query, runs them in parallel, and stores raw and filtered outputs for review, achieving 10x paraphrase coverage and persistent evaluation data for QA and regression tracking.

• • Built an end-to-end LLM-powered stock analysis system that ingests company disclosures (e.g., press releases and related financial narrative text), normalizes inputs, and produces structured decision support outputs for screening and reporting.

• • Engineered a multi-step LLM pipeline that generates: concise investment-oriented summaries, criteria based scoring/classification, and evidence highlights mapped back to source text (line-numbered for traceability and review).

• • Implemented production safeguards for structured outputs (JSON validation + correction/retry paths) and added operational tooling (model selection via config, dedicated logging endpoint, and CSV export of results).

• • Started with a market-screening phase to choose where to invest first, using publicly available signals across cities, counties, and metros to prioritize the initial target region before lead generation.

• • Designed a two-source enrichment workflow to convert public records into actionable leads by combining court and probate case discovery with FastPeopleSearch identity resolution (names, addresses, relatives), producing one consolidated lead record per case. Result: an auditable pipeline from court records to people search to a merged lead, ready for CSV and Excel export; volume processed and match-rate improvement:

• • Imported structured leads into Zoho CRM so the sales and outreach team could operate from a single pipeline.

• • Established an operating process in Zoho CRM to continuously ingest new leads, track statuses, and maintain clean data for segmentation, outreach, and ongoing analysis.