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In my 5+ years teaching secondary school, I’ve seen veteran teachers retire or transfer and with them go entire lifetimes of teaching wisdom. This isn’t just anecdotal, research shows that in secondary schools, experienced teachers often leave with tacit knowledge because there

The greatest threat to a company is not competition it is forgetting what it already paid to learn. Enterprises track revenue, payroll, cloud overhead with precision. Yet the most expensive leak is invisible: When a person leaves, their knowledge leaves too. And no spreadsheet

https://t.co/KVZcQ7V6Oy In the long run, organizations are not just collections of people, tools, and processes. They are memory systems. Most companies have accepted that when a great person leaves, a chapter closes and much of their value is gone forever. Sensay’s thesis is

If that experience feels like where organizations are inevitably heading where knowledge is captured by default, where expertise is queryable, where departures no longer equal forgetting then the rest follows. The product is live. The ecosystem token exists. The integrations are

You do not need to model the entire future to test the premise. You can: Go to https://t.co/l4ahFev202 • Upload a few documents or a short voice note • Spin up a replica of yourself or your role • Ask it how you work, what you prioritize, how you respond in certain edge cases

For crypto-native analysts, $SNSY is an experiment in tying an asset to a real institutional behaviour change: • As more companies decide that losing knowledge is unacceptable, AI offboarding becomes standard. • Every replica, every integration, every new seat introduces

For CIOs and CTOs, Sensay sits at a specific intersection: • Risk Management → reduces single-point-of-failure risk tied to individuals • Operational Excellence → slashes time to competence, improves consistency •Knowledge Governance → centralizes yet contextualizes

From a traction perspective: • 3.4M raised in April 2024 to scale platform and ecosystem • Adoption by S&P 500 companies, validating enterprise readiness • 25k+ replicas, 500k+ daily interactions, tens of thousands of active users • A growing base across sectors:

Now, lets compare this with the current field: ➥ Starmind → builds internal expert networks but still depends on people being available. Knowledge remains person-bound. ➥ Moveworks → excels at ticket automation and IT workflows, but does not aim to preserve institutional

From the enterprise side, this architecture enables very specific use cases: • 𝙊𝙣𝙗𝙤𝙖𝙧𝙙𝙞𝙣𝙜 & 𝙏𝙧𝙖𝙞𝙣𝙞𝙣𝙜: New hires ramp in under 30 days instead of 90+ because they aren’t waiting on subject-matter experts; they query the replica directly. • 𝙆𝙣𝙤𝙬𝙡𝙚𝙙𝙜𝙚

𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴, 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 & 𝗥𝗲𝘁𝗿𝗮𝗶𝗻𝗶𝗻𝗴 A replica that never changes is as dangerous as a stale wiki. Sensay layers: • Usage analytics (who queries what, where friction occurs) •Feedback loops (was this answer useful, accurate, compliant?) •Confidence

Supply is not just a static cap it is a time structured instrument. Indicative circulation milestones: ➝ Launch Q1 2024: initial public sale + liquidity/incentives (~1.2B in play) ➝ Aug 2024: ≈ 3.2B in circulation (public sale fully vested) ➝ Apr 2025: ≈ 5B circulating

From a system perspective, $SNSY exists to do four things: • Access → pay for replicas, platform, and advanced features • Governance → vote on roadmap and protocol choices • Rewards → staking / contribution incentives for high-quality knowledge and maintenance

𝗧𝗼𝗸𝗲𝗻𝗶𝘇𝗲𝗱 𝗔𝗰𝗰𝗲𝘀𝘀 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 $SNSY 𝗮𝘀 𝘁𝗵𝗲 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗹𝗮𝘆𝗲𝗿 Sensay’s ecosystem is powered by $SNSY, an ERC-20 omnichain asset (Ethereum, Arbitrum, Base). It uses LayerZero’s OFT standard to move seamlessly across networks. Total

𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 Once trained, replicas are not trapped in a dashboard. They are deployed to where work actually happens: ⇛ Internal chat (Slack, Teams, Discord, Telegram) Customer facing interfaces (site widgets, support portals) ⇛ API endpoints for

𝗔𝗜 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 & 𝗥𝗲𝗽𝗹𝗶𝗰𝗮 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 This is where a replica becomes more than a search wrapper. The system learns: ⇛How the expert prioritizes trade-offs (“speed vs robustness,” “short-term patch vs refactor”) ⇛ Which solutions were preferred and why

𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 & 𝗔𝗻𝗼𝗻𝘆𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Raw inputs contain redundancy, noise, and sensitive material. Sensay: ⇛ Cleans and normalizes text ⇛ Chunks content into semantically coherent units ⇛ Redacts or anonymizes sensitive identifiers ⇛ Tags content by

𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗖𝗮𝗽𝘁𝘂𝗿𝗲: Sensay ingests: ⇛ Documents, wikis, SOPs ⇛ PDFs, decks, spreadsheets ⇛ Meeting transcripts, recordings ⇛ Email threads and chat histories (where appropriate) ⇛ Voice interviews via Sophia Crucially, it captures both explicit and tacit

From an engineering / enterprise architecture lens, Sensay is a six-stage knowledge pipeline optimized for scale, security, and continuity: ➠ Knowledge Capture ➠ Data Preprocessing & Anonymization ➠ AI Reasoning & Replica Training ➠ Enterprise Deployment ➠ Tokenized