[{"data":1,"prerenderedAt":190},["ShallowReactive",2],{"profile":3,"work-digihire-ai":14},{"name":4,"role":5,"tagline":6,"location":7,"contact":8},"Sankar Vema","AI Builder & Architect of Agentic Systems","I help enterprise leaders turn AI ambition into capability that actually ships.","India · open to global advisory engagements",{"email":9,"linkedin":10,"github":11,"blog":12,"twitter":13},"sankar.vema@gmail.com","https:\u002F\u002Fwww.linkedin.com\u002Fin\u002Fsankarvema\u002F","http:\u002F\u002Fsankarvema.github.io\u002F","http:\u002F\u002Fsankarvema.blogspot.com\u002F","https:\u002F\u002Ftwitter.com\u002Fsansvema",{"id":15,"title":16,"body":17,"context":161,"description":148,"domain":162,"draft":163,"extension":164,"hero":165,"impact":166,"meta":167,"navigation":168,"path":169,"role":170,"scale":171,"seo":172,"slug":173,"stack":174,"status":181,"stem":182,"tags":183,"year":188,"__hash__":189},"work\u002Fwork\u002Fdigihire-ai.md","DigiHire.ai: End-to-End AI Recruitment Platform",{"type":18,"value":19,"toc":147},"minimark",[20,25,29,33,36,40,43,48,51,55,58,62,65,69,125,129,132,135],[21,22,24],"h2",{"id":23},"the-mandate","The mandate",[26,27,28],"p",{},"I own the product architecture and technology direction for DigiHire.ai, one of\nthe flagship products at Zettamine. The brief was not a feature bolted onto an\napplicant tracking system. It was to redesign the recruitment workflow around\nwhat AI can now do, and to ship something recruiters would trust with a decision\nthat is expensive to get wrong.",[21,30,32],{"id":31},"the-problem","The problem",[26,34,35],{},"Recruitment in most enterprises is three disconnected workflows pretending to be\none: sourcing on professional networks, screening through applicant tracking\nsystems, and evaluating in spreadsheets. The seams between them eat the bulk of\nrecruiter time and most of the signal. The premise of DigiHire.ai: collapse the\nthree into one AI-native loop where the system sources, the system screens, and\nthe system ranks, with humans intervening on judgment rather than janitorial work.",[21,37,39],{"id":38},"architecture","Architecture",[26,41,42],{},"Three engines under one product.",[44,45,47],"h3",{"id":46},"_1-deep-sourcing-engine","1. Deep-sourcing engine",[26,49,50],{},"A custom Chrome extension using segmented scrolling and DOM hydration to extract\nstructured profile data from professional networks that expose no usable public\nAPI, without tripping anti-bot heuristics. Output is piped into a typed candidate\nmodel, one adapter per source.",[44,52,54],{"id":53},"_2-llm-based-semantic-screening","2. LLM-based semantic screening",[26,56,57],{},"Resumes and, when available, interview transcripts are passed through an LLM with\nrole-specific prompts that return structured evaluations rather than free text.\nEvery evaluation is vector-indexed for similarity search across the candidate pool.",[44,59,61],{"id":60},"_3-ranking-and-recommendation","3. Ranking and recommendation",[26,63,64],{},"Candidates are ranked against the role profile using a hybrid of semantic\nsimilarity, structured-criterion matching and explicit recruiter weights. Every\nrank is explainable down to the contributing signals.",[21,66,68],{"id":67},"key-decisions-and-what-they-cost","Key decisions and what they cost",[70,71,72,88],"table",{},[73,74,75],"thead",{},[76,77,78,82,85],"tr",{},[79,80,81],"th",{},"Decision",[79,83,84],{},"Why",[79,86,87],{},"What it traded",[89,90,91,103,114],"tbody",{},[76,92,93,97,100],{},[94,95,96],"td",{},"Browser-extension sourcing over API ingestion",[94,98,99],{},"Coverage of networks with no public API",[94,101,102],{},"Brittleness when the DOM changes, contained with an adapter per source",[76,104,105,108,111],{},[94,106,107],{},"Structured LLM evaluation over free text plus parsing",[94,109,110],{},"Trust and explainability",[94,112,113],{},"Higher prompt-engineering cost up front",[76,115,116,119,122],{},[94,117,118],{},"Hybrid ranking (vector, criterion, weight) over pure vector",[94,120,121],{},"Recruiters need to steer the outcome",[94,123,124],{},"More moving parts, longer evaluation cycles",[21,126,128],{"id":127},"outcome","Outcome",[26,130,131],{},"Time-to-hire fell by more than 60 percent for pilot accounts, and every ranking\nstayed auditable, which is what turned it from a demo into a system a recruiting\nteam would actually run.",[133,134],"hr",{},[26,136,137,141,142],{},[138,139,140],"strong",{},"Related writing:"," ",[143,144,146],"a",{"href":145},"\u002Fwriting\u002Fenterprise-ai-adoption-playbook","The enterprise AI adoption playbook nobody writes",{"title":148,"searchDepth":149,"depth":149,"links":150},"",3,[151,153,154,159,160],{"id":23,"depth":152,"text":24},2,{"id":31,"depth":152,"text":32},{"id":38,"depth":152,"text":39,"children":155},[156,157,158],{"id":46,"depth":149,"text":47},{"id":53,"depth":149,"text":54},{"id":60,"depth":149,"text":61},{"id":67,"depth":152,"text":68},{"id":127,"depth":152,"text":128},"Zettamine","ai-products",false,"md","An AI-native recruitment platform that sources, screens and ranks candidates in one loop, and cut time-to-hire by more than 60 percent for pilot accounts.","Time-to-hire cut by more than 60%",{},true,"\u002Fwork\u002Fdigihire-ai","Product Architect and Technology Owner","Production platform, multi-account",{"title":16,"description":148},"digihire-ai",[175,176,177,178,179,180],"LLM","Vector DB","Semantic Search","Chrome Extension","Node.js","Nuxt","shipped","work\u002Fdigihire-ai",[184,185,186,187],"build","transform","llm","product","2023-2024","C3h-5ajUq2PxC9eeX2YyvBXSAJ5_wGNo4-x4T04TzwQ",1790601541903]