{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_hyper-space","slug":"hyper-space","name":"Hyper-Space","type":"product","url":"https://www.hyper-space.io","page_url":"https://unfragile.ai/hyper-space","categories":["research-search"],"tags":[],"pricing":{"model":"paid","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_hyper-space__cap_0","uri":"capability://search.retrieval.real.time.index.updates.with.sub.second.latency","name":"real-time index updates with sub-second latency","description":"Hyper-Space maintains a continuously-updated search index that reflects data changes without traditional crawl delays, using event-driven architecture to ingest and index new content as it arrives. The system appears to employ streaming ingestion pipelines that process updates incrementally rather than batch-based re-indexing, enabling search results to reflect the latest information within seconds of publication or modification.","intents":["I need search results that always reflect the current state of my data without waiting for scheduled crawls","I want to embed search in my SaaS product where users expect real-time results after content updates","I need to search rapidly-changing datasets like news feeds, stock data, or live inventory without staleness"],"best_for":["SaaS platforms with frequently-updated content (news, e-commerce, collaboration tools)","Enterprise teams managing dynamic datasets requiring sub-minute freshness","Real-time applications where crawl-based indexing introduces unacceptable lag"],"limitations":["Real-time indexing increases infrastructure complexity and operational overhead compared to batch indexing","Consistency guarantees during high-volume concurrent updates are not publicly documented","No information on how index durability is maintained during cloud infrastructure failures"],"requires":["API integration capability to push content updates to Hyper-Space","Network connectivity with sufficient bandwidth for streaming ingestion","Paid subscription tier (specific SLA not publicly disclosed)"],"input_types":["structured data (JSON, CSV)","unstructured text","document streams via API"],"output_types":["ranked search results","relevance scores","metadata with freshness timestamps"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_1","uri":"capability://search.retrieval.ai.powered.semantic.relevance.ranking","name":"ai-powered semantic relevance ranking","description":"Hyper-Space applies machine learning models to rank search results based on semantic meaning and contextual relevance rather than keyword frequency or link-based signals. The system likely uses dense vector embeddings (possibly transformer-based) to understand query intent and match it against indexed content semantics, with learned ranking functions that optimize for user-defined relevance metrics beyond simple term matching.","intents":["I want search results ranked by actual relevance to user intent, not just keyword matches","I need to reduce noise and irrelevant results in my search experience compared to keyword-only matching","I want to customize relevance ranking based on my domain-specific signals (user engagement, conversion, quality scores)"],"best_for":["Enterprise search implementations where result quality directly impacts user satisfaction","E-commerce and content platforms where relevance ranking affects conversion and engagement","Teams building domain-specific search where generic keyword matching is insufficient"],"limitations":["Semantic ranking models require computational overhead that increases query latency compared to inverted-index lookups","Model training and fine-tuning approach is not publicly documented — unclear if custom models per tenant are supported","No published information on how ranking models handle domain-specific terminology or specialized vocabularies"],"requires":["Sufficient query volume to train effective ranking models (minimum threshold unknown)","Relevance feedback data or labeled examples to customize ranking (if supported)","Paid subscription tier with ML-powered ranking enabled"],"input_types":["natural language queries","indexed document content","optional relevance feedback signals"],"output_types":["ranked result lists","relevance scores (0-1 or similar scale)","explanation metadata (if available)"],"categories":["search-retrieval","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_10","uri":"capability://search.retrieval.pagination.and.result.windowing.with.cursor.based.navigation","name":"pagination and result windowing with cursor-based navigation","description":"Hyper-Space supports efficient pagination of large result sets using cursor-based navigation (likely keyset pagination) rather than offset-based pagination, enabling efficient retrieval of arbitrary result pages without scanning all preceding results. The system likely returns opaque cursors that encode the position in the result set, allowing clients to request next/previous pages efficiently.","intents":["I want to paginate through large result sets efficiently without performance degradation on later pages","I need to support 'load more' functionality without re-executing the full query","I want to avoid the performance cliff of offset-based pagination on large datasets"],"best_for":["Applications with large result sets where pagination performance is critical","Mobile applications where efficient pagination reduces bandwidth and latency","Real-time search where result sets change between pagination requests"],"limitations":["Cursor-based pagination requires stateless cursor encoding, which may limit sorting flexibility","No information on cursor expiration or validity period","Unclear if random access to arbitrary pages is supported or only sequential navigation"],"requires":["Support for cursor parameters in the search API","Client-side cursor handling and storage","Paid subscription tier"],"input_types":["search queries","cursor tokens (for subsequent pages)"],"output_types":["result page","next/previous cursors","total result count (if available)"],"categories":["search-retrieval","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_11","uri":"capability://search.retrieval.autocomplete.and.search.suggestions.with.prefix.matching","name":"autocomplete and search suggestions with prefix matching","description":"Hyper-Space provides autocomplete functionality that suggests search terms and phrases as users type, using prefix-matching algorithms to find completions from indexed content or a curated suggestion dictionary. The system likely uses a trie or similar data structure for efficient prefix matching, returning ranked suggestions based on popularity or relevance.","intents":["I want to show search suggestions as users type to guide them toward relevant queries","I need to reduce query latency by suggesting common searches before users finish typing","I want to improve search recall by suggesting related terms users might not think of"],"best_for":["Consumer-facing search interfaces where autocomplete improves user experience","E-commerce platforms where autocomplete drives product discovery","Applications with large vocabularies where autocomplete reduces typing burden"],"limitations":["Autocomplete suggestions must be pre-computed or indexed separately, adding indexing overhead","No information on how suggestions are ranked (popularity, relevance, recency)","Unclear if autocomplete supports multi-language or special character handling"],"requires":["Autocomplete API endpoint","Suggestion data indexed or configured","Paid subscription tier"],"input_types":["partial query strings (prefixes)","optional context (user history, location)"],"output_types":["ranked suggestion list","suggestion metadata (popularity, category)"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_2","uri":"capability://automation.workflow.cloud.native.auto.scaling.infrastructure","name":"cloud-native auto-scaling infrastructure","description":"Hyper-Space is built on cloud-native architecture (likely Kubernetes or serverless) that automatically scales compute and storage resources in response to query load and indexing volume. The system provisions additional capacity during traffic spikes without manual intervention, using horizontal scaling patterns and distributed query processing to maintain performance under variable demand.","intents":["I need search to handle traffic spikes without manual scaling or performance degradation","I want to avoid over-provisioning infrastructure for peak load while keeping costs reasonable at baseline","I need predictable search performance regardless of concurrent user count or query complexity"],"best_for":["SaaS platforms with variable or unpredictable query traffic patterns","Enterprise deployments where search must remain responsive during traffic surges","Teams without dedicated infrastructure operations staff to manually scale search systems"],"limitations":["Auto-scaling introduces variable latency during scale-up events (typically 30-60 seconds to provision new capacity)","Pricing model likely charges per query or compute unit, making costs unpredictable during traffic spikes","No published information on minimum latency guarantees or SLA terms during scaling transitions"],"requires":["Cloud infrastructure account (AWS, GCP, Azure — specific providers not documented)","API integration to submit queries to cloud-hosted search service","Paid subscription with scaling enabled"],"input_types":["search queries via REST/GraphQL API","content for indexing via streaming or batch APIs"],"output_types":["search results","query latency metrics","usage/billing data"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_3","uri":"capability://tool.use.integration.custom.search.embedding.and.indexing.via.api","name":"custom search embedding and indexing via api","description":"Hyper-Space provides REST/GraphQL APIs to ingest custom content, define indexing schemas, and configure how data is tokenized, embedded, and stored in the search index. Developers can push documents with custom metadata, specify which fields are searchable, and control how content is processed before indexing, enabling integration with existing data pipelines and custom data sources.","intents":["I want to embed Hyper-Space search into my SaaS product without building search infrastructure myself","I need to index my proprietary data and control exactly how it's processed and searched","I want to integrate search with my existing data pipeline and ETL workflows"],"best_for":["SaaS platforms embedding white-label search functionality","Teams with custom data sources that don't fit standard search engine assumptions","Developers building search-as-a-service offerings on top of Hyper-Space"],"limitations":["API-based ingestion introduces network latency and potential bottlenecks for high-volume indexing (throughput limits not published)","Schema flexibility is unknown — unclear if arbitrary custom fields and metadata are supported","No information on bulk indexing performance or recommended batch sizes for efficient ingestion"],"requires":["API key and authentication credentials","HTTP/REST or GraphQL client capability","Understanding of Hyper-Space's indexing schema and field configuration","Paid subscription tier"],"input_types":["JSON documents with custom fields","structured metadata","text content for embedding"],"output_types":["indexed documents","indexing status/confirmation","schema validation responses"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_4","uri":"capability://tool.use.integration.multi.tenant.search.isolation.with.per.tenant.customization","name":"multi-tenant search isolation with per-tenant customization","description":"Hyper-Space appears to support multi-tenant deployments where each tenant maintains isolated search indexes and can customize ranking, filtering, and relevance algorithms independently. The system likely uses logical data isolation (separate indexes per tenant) rather than physical isolation, with per-tenant configuration for relevance tuning, field weighting, and custom ranking rules.","intents":["I'm building a SaaS platform and need each customer to have isolated search without cross-tenant data leakage","I want each tenant to customize search relevance and ranking for their specific domain or use case","I need to manage search configuration per customer without operational overhead"],"best_for":["SaaS platforms with multiple customers requiring isolated search","Marketplace or multi-tenant applications where search customization per user is critical","Enterprise deployments with multiple business units needing independent search configurations"],"limitations":["Multi-tenant isolation adds complexity to query routing and index management, potentially increasing latency","Unclear if physical isolation (separate infrastructure per tenant) is available for high-security requirements","No published information on tenant quota enforcement or resource limits per tenant"],"requires":["Tenant identifier in API requests for routing to correct index","Per-tenant API keys or authentication tokens","Paid subscription with multi-tenant support enabled"],"input_types":["tenant-scoped documents","tenant-specific configuration","tenant-scoped queries"],"output_types":["tenant-isolated search results","per-tenant configuration status","tenant-specific usage metrics"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_5","uri":"capability://search.retrieval.faceted.search.and.filtering.with.dynamic.facet.generation","name":"faceted search and filtering with dynamic facet generation","description":"Hyper-Space supports faceted navigation where search results are automatically categorized by configurable dimensions (e.g., category, price range, date), allowing users to refine results by selecting facet values. The system likely generates facet counts dynamically based on current search results, enabling drill-down exploration without requiring separate queries for each facet combination.","intents":["I want users to refine search results by clicking facets (category, price, date, etc.) without writing new queries","I need to show facet counts that reflect the current search results, not the entire index","I want to enable exploratory search where users discover content by navigating facet hierarchies"],"best_for":["E-commerce platforms where faceted navigation drives product discovery","Content platforms (news, documentation) where faceted filtering improves findability","Enterprise search where users need to narrow results by multiple dimensions"],"limitations":["Facet generation on every query adds computational overhead, potentially increasing latency for high-cardinality facets","Unclear if hierarchical facets (e.g., category > subcategory) are supported","No information on facet caching strategies or performance optimization for facets with millions of unique values"],"requires":["Facet configuration specifying which fields are facetable","Support for facet queries in the search API","Paid subscription tier"],"input_types":["search queries with optional facet filters","facet configuration (field names, display names)"],"output_types":["search results","facet counts per value","facet metadata (display order, ranges)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_6","uri":"capability://data.processing.analysis.query.analytics.and.relevance.feedback.collection","name":"query analytics and relevance feedback collection","description":"Hyper-Space collects query logs and user interaction signals (clicks, dwell time, conversions) to measure search effectiveness and provide insights into query patterns and result quality. The system likely uses this feedback to identify low-performing queries, track relevance metrics, and optionally feed signals back into ranking models to improve results over time.","intents":["I want to understand which queries are returning poor results and why","I need to measure search effectiveness using metrics like click-through rate and conversion","I want to use user behavior signals to continuously improve search ranking"],"best_for":["SaaS platforms where search quality directly impacts user satisfaction and retention","E-commerce and content platforms optimizing for conversion or engagement","Teams with data science capability to analyze search metrics and iterate on relevance"],"limitations":["Query analytics require client-side instrumentation to capture user interactions, adding implementation complexity","Privacy implications of collecting query logs and user behavior are not publicly documented","Unclear if analytics data is retained indefinitely or subject to retention policies"],"requires":["Client-side instrumentation to track clicks and user interactions","Integration with Hyper-Space analytics API to submit feedback signals","Paid subscription tier with analytics enabled"],"input_types":["query logs","click events","conversion signals","dwell time metrics"],"output_types":["query performance metrics","relevance dashboards","low-performing query reports","user behavior insights"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_7","uri":"capability://search.retrieval.typo.tolerance.and.fuzzy.matching.with.phonetic.variants","name":"typo tolerance and fuzzy matching with phonetic variants","description":"Hyper-Space handles misspelled queries and phonetic variations by matching against indexed content even when exact spelling doesn't match, using edit-distance algorithms (likely Levenshtein or similar) and phonetic encoding (Soundex, Metaphone) to find relevant results despite user input errors. The system likely applies fuzzy matching at query time with configurable tolerance thresholds.","intents":["I want search to return results even when users misspell query terms","I need to handle phonetic variations and alternate spellings (e.g., 'color' vs 'colour')","I want to improve search recall by matching similar terms without requiring exact spelling"],"best_for":["Consumer-facing search where users may have varying spelling proficiency","Mobile search where typos are common due to small keyboards","International applications where phonetic variations and transliterations are frequent"],"limitations":["Fuzzy matching increases query latency and computational cost compared to exact matching","Overly permissive fuzzy matching can return irrelevant results, requiring careful threshold tuning","No information on how fuzzy matching interacts with semantic ranking or whether it applies before/after ranking"],"requires":["Configuration of fuzzy matching tolerance (edit distance threshold)","Optional phonetic encoding configuration","Paid subscription tier"],"input_types":["user queries (potentially misspelled)","fuzzy matching configuration"],"output_types":["search results including fuzzy matches","indication of whether results are exact or fuzzy matches"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_8","uri":"capability://search.retrieval.structured.data.filtering.and.range.queries","name":"structured data filtering and range queries","description":"Hyper-Space supports filtering search results by structured fields (numbers, dates, categories) using range queries, equality filters, and boolean combinations, enabling users to narrow results by price ranges, date ranges, categories, and other discrete values. The system likely uses inverted indexes on structured fields to efficiently evaluate filters without scanning all results.","intents":["I want users to filter results by price range, date range, or category without full-text search","I need to combine text search with structured filters (e.g., 'laptop' AND price < $1000)","I want to support complex filter combinations (AND, OR, NOT) on multiple fields"],"best_for":["E-commerce platforms where structured filtering is essential for product discovery","Content platforms with metadata (date, author, category) that users filter by","Enterprise search where filtering by department, status, or other attributes is common"],"limitations":["Complex filter combinations can be expensive to evaluate, potentially increasing query latency","No information on support for nested or hierarchical structured data","Unclear if range queries support custom collation or locale-specific sorting"],"requires":["Structured field definitions in the indexing schema","Filter syntax in the query API (exact syntax not documented)","Paid subscription tier"],"input_types":["structured field values (numbers, dates, strings)","filter expressions (range, equality, boolean combinations)"],"output_types":["filtered search results","result count per filter value","filter metadata (min/max ranges)"],"categories":["search-retrieval","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_hyper-space__cap_9","uri":"capability://search.retrieval.synonym.expansion.and.query.rewriting","name":"synonym expansion and query rewriting","description":"Hyper-Space supports synonym dictionaries that expand queries to include related terms, enabling users to find results using alternative terminology without explicit knowledge of indexed content vocabulary. The system likely applies synonym expansion at query time, rewriting queries to match multiple term variants and improving recall for domain-specific synonyms.","intents":["I want searches for 'car' to also return results for 'automobile', 'vehicle', etc.","I need domain-specific synonym handling (e.g., 'MI' = 'Michigan' in real estate search)","I want to improve search recall by automatically expanding queries with related terms"],"best_for":["Domain-specific search (medical, legal, real estate) where terminology varies","E-commerce platforms where product synonyms improve discoverability","International applications where translations and regional variants are common"],"limitations":["Synonym expansion can increase query latency and false positive matches if not carefully curated","No information on how synonyms are managed or updated (manual configuration vs. learned from data)","Unclear if bidirectional synonyms are supported or if expansion is one-directional"],"requires":["Synonym dictionary configuration (likely via API or configuration file)","Understanding of domain-specific terminology and variants","Paid subscription tier"],"input_types":["user queries","synonym dictionary (term pairs or groups)"],"output_types":["expanded queries","search results including synonym matches"],"categories":["search-retrieval","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":43,"verified":false,"data_access_risk":"low","permissions":["API integration capability to push content updates to Hyper-Space","Network connectivity with sufficient bandwidth for streaming ingestion","Paid subscription tier (specific SLA not publicly disclosed)","Sufficient query volume to train effective ranking models (minimum threshold unknown)","Relevance feedback data or labeled examples to customize ranking (if supported)","Paid subscription tier with ML-powered ranking enabled","Support for cursor parameters in the search API","Client-side cursor handling and storage","Paid subscription tier","Autocomplete API endpoint"],"failure_modes":["Real-time indexing increases infrastructure complexity and operational overhead compared to batch indexing","Consistency guarantees during high-volume concurrent updates are not publicly documented","No information on how index durability is maintained during cloud infrastructure failures","Semantic ranking models require computational overhead that increases query latency compared to inverted-index lookups","Model training and fine-tuning approach is not publicly documented — unclear if custom models per tenant are supported","No published information on how ranking models handle domain-specific terminology or specialized vocabularies","Cursor-based pagination requires stateless cursor encoding, which may limit sorting flexibility","No information on cursor expiration or validity period","Unclear if random access to arbitrary pages is supported or only sequential navigation","Autocomplete suggestions must be pre-computed or indexed separately, adding indexing overhead","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.78,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:31.445Z","last_scraped_at":"2026-04-05T13:23:42.552Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=hyper-space","compare_url":"https://unfragile.ai/compare?artifact=hyper-space"}},"signature":"6cKJeV/XGSY5yL9LOfC7Q9+9P+C+tvFvkJ+7QBl9bNeTtkOA8Pu7RyYz8PH4s0RMpktGk29FWUy6HWBcFWLIAA==","signedAt":"2026-06-22T10:24:55.468Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/hyper-space","artifact":"https://unfragile.ai/hyper-space","verify":"https://unfragile.ai/api/v1/verify?slug=hyper-space","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}