Built a persona-adaptive sales enablement platform for medical technology teams that need faster onboarding without losing regulatory context. The system combines RAG, MCP-style content updates, and skill-gap analysis so each rep can practice against role-specific objections, product narratives, and compliant messaging. It turns static enablement material into an interactive coaching workflow that helps managers see where reps are stuck and gives new hires a clearer path from training to confident field conversations.

Created a voice-enabled data copilot that lets analysts and business users ask questions naturally instead of writing SQL by hand. The workflow captures spoken intent, translates it into structured database queries, and returns usable answers from Snowflake-backed data sources. LangGraph coordinates the voice, reasoning, SQL generation, and response steps so users can move from a business question to a data-backed answer in seconds while keeping the interaction simple enough for non-technical teams.

Built IntervueRecall as a SaaS workspace for candidates who want a clear record of the questions asked in their interviews. Users upload an interview recording, the app transcribes the audio, extracts only the interviewer questions, and presents them as a clean list. The workflow is focused on question recall so candidates can review what was asked and prepare for future interviews with a more accurate question bank.

Built an enterprise assistant that can decide whether a question should be answered from documents, structured tables, or a combination of both. The system uses Databricks, RAG, Delta-backed data, and tool-style routing so employees can ask operational questions without knowing where the answer lives. It is designed for repeated internal use: clear answers, grounded context, and a workflow that reduces the manual analyst effort normally spent searching files, checking dashboards, and stitching together evidence.

Built LinkedInfluencer, a local browser app that discovers live GenAI discussions from Reddit, Hacker News, Dev.to, and Stack Overflow, scores story relevance, and turns the strongest ideas into LinkedIn-ready posts. The LangGraph pipeline runs discovery, aggregation, analysis, selection, hook generation, content transformation, sanitization, quality control, and final JSON export with live server-sent progress events in the UI. It supports Fast and Deep modes, configurable audience, goal, tone, source selection, worker limits, output folders, and Claude token budgets so content can be tuned without touching code.

Built FitScout as a recruiter-facing screening assistant that turns a pile of resumes into structured hiring signals. It uses LlamaIndex and Pinecone to extract skills, experience, role fit, and searchable candidate context, giving recruiters a faster way to compare applicants without reading every document from scratch. The product focuses on practical hiring workflows: resume ingestion, skill matching, evidence-backed summaries, and shortlists that still leave the recruiter in control of the final judgment.

Designed a polished legal AI assistant for Swiss document workflows, focused on making contract review, clause summarization, and legal Q&A feel fast, controlled, and professional. The experience presents a clean chatbot interface over a privacy-conscious RAG flow, helping users move from dense legal text to practical answers, draft language, and review-ready summaries. The project emphasizes trust signals that matter in legal operations: source-grounded responses, clear document context, GDPR-aware handling, and a calmer workflow for teams that need accuracy without slowing down routine review.

Integrated HR and finance knowledge into a shared AI assistant for routine internal support. The chatbot answers policy questions, surfaces payroll information, explains process details, and routes users toward the right operational context without forcing them through multiple systems. Built with LangChain, Azure OpenAI, Databricks, and ETL pipelines, it is shaped around the kind of questions employees ask repeatedly and the kind of auditability support teams need when answers involve people, money, and policy.