Hyper vs Nina by Antalpha: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Hyper and Nina by Antalpha — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Hyper
Hyper
A company knowledge layer that learns from Docs, Slack, Email and Calendar to power smarter, context-aware AI across teams.
Key features
- Unified Knowledge Ingestion: Continuously imports and indexes data from Docs, Slack, Email, and Calendar to build a central, searchable company knowledge graph.
- Contextual AI Plug-ins: Provides an interface and connectors so teams can inject company-specific context into external or internal AI models, improving accuracy and relevance of responses.
- Persistent Institutional Memory: Retains historical context across conversations and workflows so the system remembers past decisions, preferences, and policies without manual re-entry.
- Real-time Sync and Updates: Keeps ingested sources up to date with near real-time synchronization so answers reflect the latest documents, messages, and schedule changes.
- Access Controls & Security: Enables role-based access and privacy controls to ensure sensitive documents and communications are only used where permitted.
- Searchable Knowledge Retrieval: Offers semantic search and retrieval of relevant docs, messages, and calendar events to surface precise context for queries and automations.
- Workflow Automation: Leverages stored knowledge to trigger or assist with routine tasks (e.g., follow-ups, meeting summaries) and reduce manual work.
- Integration Framework: Supports connectors and APIs to integrate with common productivity tools and plug the company brain into existing AI assistants or platforms.
- Ingests and learns from Docs, Slack, Email and Calendar
- Creates a centralized, searchable company knowledge layer
- Integrates/"plugs into" existing AI systems to provide context and memory
- Context enrichment for downstream AI responses and workflows
- Connectors to common collaboration sources (Docs, Slack, Email, Calendar)
Best for
- Onboarding Acceleration: New hires query the company brain to get accurate, contextual answers about processes, past decisions, and team norms without repeatedly asking colleagues.
- Customer Support Enablement: Support agents retrieve up-to-date product docs, past tickets, and policy notes to craft faster, consistent responses to customers.
- Meeting Summaries & Action Items: Automatically summarize calendar events and linked documents, then surface follow-ups and owners based on historical context.
- Internal Knowledge Discovery: Employees search across Slack, emails, and docs to find precedents, design decisions, or technical notes relevant to current projects.
- Automated Follow-ups: Use contextual knowledge to draft or schedule follow-up emails and tasks after meetings, ensuring continuity and reducing manual tracking.
- Compliance & Audit Readiness: Aggregate and index communications and documents to simplify internal audits and demonstrate policy adherence with searchable records.
- Developer and Product Support: Engineers and PMs query past architecture decisions, bug histories, and release notes to speed troubleshooting and planning.
- Provide company-specific context to LLMs and AI assistants
- Centralized knowledge retrieval and enterprise search across Docs, Slack, Email and Calendar
- Faster onboarding by surfacing institutional knowledge
- Automated summarization and context-aware drafting for email and meetings
- Enriching customer-support or internal automation agents with up-to-date company info
Nina by Antalpha
Antalpha Technologies Pte. Ltd.
An always-on Web3 AI assistant that answers crypto questions in natural language using live market data, on-chain activity and news.
Key features
- Natural Language Q&A: Ask about tokens, protocols or on-chain events in everyday language, with no technical background or query syntax required.
- Real-Time Market Data: Live crypto prices and rankings are pulled into answers so a question about a price move is grounded in current numbers.
- On-Chain Activity Decoding: Address activity and token holdings are read and explained at a glance instead of being left as raw transaction data.
- Prediction Market Coverage: The Sentry section tracks analysis across trending prediction-market topics, extended to tech, culture and weather markets.
- Antalpha In-House Model: The assistant runs on Antalpha's own AI model rather than a third-party endpoint, with the processing disclosed in the privacy policy.
- Guided Onboarding: A first-sign-in tour introduces each core feature one at a time so new users are not dropped into an empty chat.
Best for
- Understanding a Price Move: Asking why a token moved and getting market data and recent news assembled into one explanation.
- Researching a Protocol: Getting a plain-language walkthrough of how a DeFi protocol works before committing time to its documentation.
- Inspecting an Address: Checking what a wallet holds and what it has been doing on-chain without reading a block explorer directly.
- Following Prediction Markets: Tracking analysis on trending prediction-market questions across crypto, tech, culture and weather.
- Onboarding to Crypto: Letting a newcomer ask basic Web3 questions conversationally instead of piecing answers together across ten tabs.
- Mobile Market Check-ins: Reviewing prices, rankings and on-chain movement from a phone during the day rather than at a desk.