ScoutINNtelligence
Dual-intelligence lead generation. Supply Scan ranks startups by fit; Demand Scan qualifies buyers with a 3-filter check (MarTech need, buying capacity, actionability).
I build LLM agents, RAG pipelines and computer vision systems that ship: grievance platforms for government portals, AI study tools for UPSC aspirants, legal-document intelligence, and agentic MarTech tools for brands like Hyundai and Kajaria. Feel free to reach out :)
Currently into: agentic & multi-agent systems · retrieval-augmented generation · educational & Indic NLP · document intelligence & OCR · generative image pipelines
./about.md · ./case-studies.json · ./contact.json
github · linkedin · resume.pdf · ashwin.mishra07@gmail.com
Dual-intelligence lead generation. Supply Scan ranks startups by fit; Demand Scan qualifies buyers with a 3-filter check (MarTech need, buying capacity, actionability).
Agentic competitive-ad intelligence for Hyundai's automotive marketing teams across 40+ brands. Collects competitor Meta ads, classifies models, offers and patterns with LLMs, and returns three evidence-backed recommendations for a dealer's next campaign.
AI-generated, brand-safe ad creatives for Hyundai, built at Innocean India. Separate dashboards for admins, brand teams and dealers; campaign jobs queued on BullMQ.
Full-stack tile visualization for Kajaria: upload a room photo, detect the floor, and overlay any of 60+ tiles. An auto-visualize mode uses AI room analysis to recommend tiles, with a custom tile upload, lead capture and a CRM drawer.
Plain-English search over a government grievance portal: an LLM turns the query into Elasticsearch DSL, a semantic cache skips repeat work, and hybrid keyword + vector retrieval feeds a RAG answer. Cut manual filtering and search time by ~50%.
Evidence-image validation for Kanpur Smart City. Multi-factor scoring (variance, edge density, entropy, OCR layout) plus a voting pipeline flags documents that were uploaded where a site photo was expected.
Classifies drone type from multi-frequency (26–40 GHz) radar cross-section measurements interpolated with cubic splines across phi–theta angles. 96.6% accuracy.
Legal document intelligence: multilingual OCR, clause and entity extraction, schema-driven summaries, and RAG chat grounded in the uploaded document. Improved case-review efficiency by 60%.
[Resume (PDF)] [Case studies] [GitHub] [LinkedIn]
Open to AI/ML roles, research collaborations and interesting problems. Email me at ashwin.mishra07@gmail.com.
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