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Sahil Shaik@sahil_bhai
3
Events attended
2
Submissions made
India
1 year of experience
About me
I’m a BTech student actively learning and building in AI/ML and Data Science. My experience includes learning Python, Data Structures & Algorithms, SQL/DBMS, and core Machine Learning concepts. I have been exploring AI tools, APIs, and open-source projects to understand how real-world AI applications are built and deployed.
🤝 Top Collaborators
🤓 Latest Submissions
Parallax
PARALLAX is a GenAI-native cyber-fraud investigation platform for banks, fraud teams, SOC analysts, and cyber cells responding to APK-led financial crime. It analyzes suspicious Android applications and related fraud evidence to identify banking-malware behavior such as accessibility abuse, overlay attacks, SMS or notification interception, suspicious permissions, network indicators, and runtime compromise signals. PARALLAX converts these findings into structured evidence, confidence-scored claims, risk assessments, and investigation artifacts. The system is designed around an evidence-first workflow: ingest a suspicious APK, run static and dynamic analysis, extract IOCs and observations, snapshot the evidence bundle, coordinate specialist AI agents, surface challenges or disagreements, and produce a final human-reviewable action packet. Instead of treating fraud review as a static report, PARALLAX models it as a live investigation. Specialist agents can assess device compromise, transaction traces, mule-account patterns, evidence quality, liability context, legal evidence requirements, and final recommendations. The result is an auditable reasoning trail that helps teams understand not just what the system concluded, but why. PARALLAX keeps humans in control. Its outputs are designed to support analysts and bank officers with clear evidence, unresolved questions, recommended next steps, and exportable documentation for escalation or cyber-cell reporting.
19 Jun 2026
Tendril
Tendril is a live go-to-market (GTM) intelligence platform that converts scattered public web data, and spoken conversations, into evidence-backed, scored sales signals for B2B revenue teams. The core is a durable, multi-stage pipeline (discover, scrape, extract, graph, score, brief, outreach), where every stage commits state before yielding, so scans survive restarts. Bright Data powers live web acquisition: the SERP API runs targeted per-account queries, the Web Unlocker fetches bot-protected pages, and a Scraping Browser handles JavaScript-heavy fallbacks. AI/ML API acts as an OpenAI-compatible model gateway with routing: a cheap model for strict-JSON signal extraction, a stronger model (GPT-4o) for briefs, and a fast model for drafts. A parallel multimodal branch is the differentiator. Bright Data discovers public spoken sources, Tendril extracts the audio (yt-dlp and ffmpeg), and Speechmatics transcribes it live with speaker diarization and word-level timestamps. A cheap Featherless relevance filter gates the expensive extraction, and content-addressable hashing (SHA-256 over audio) guarantees the same media is never transcribed, or paid for, twice. Both branches converge into Cognee Cloud, a per-account knowledge graph that is written to and recalled from, grounding each brief in how the account changes over time. Engineering for trust and reliability is first-class. A transparent 0 to 100 rubric (fit, timing, relationship, evidence) gates "sales-ready", PII is scrubbed before any memory write, and per-scan budget caps plus a full provider-call audit trail keep cost and behavior accountable. Mock, live, and cached modes plus graceful fallbacks (Cognee to local, AI/ML to deterministic) mean a demo never breaks. Outreach is human-in-the-loop with ethical guardrails and switchable tone, never auto-sent. Tendril tells GTM teams who is ready now, why it matters, and what to say next, with every claim tied to a live, verifiable source.
31 May 2026
