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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 11 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Analyzes audio recordings of student speech and provides detailed feedback on pronunciation accuracy, accent patterns, and phonetic correctness. Uses AI to identify specific pronunciation errors and compare against native speaker standards.
Evaluates student speech recordings for fluency metrics including speech rate, pause patterns, hesitation frequency, and overall naturalness. Provides quantitative scores and qualitative feedback on delivery smoothness.
Automatically syncs assessment grades and feedback to connected LMS gradebooks or exports grades in standard formats for manual import. Ensures grades appear in the official gradebook without manual data entry.
Evaluates student responses to prompts or questions by analyzing the content and accuracy of their spoken answers. Determines whether students understood the prompt and provided relevant, correct responses.
Processes multiple student audio submissions in bulk, automatically evaluating all recordings against consistent criteria and generating grades and feedback reports. Eliminates manual one-by-one grading workflow.
Seamlessly connects Speakable with Google Classroom, allowing teachers to assign speaking tasks, collect audio submissions, and return grades and feedback directly within the Classroom interface without switching platforms.
Integrates Speakable with Canvas LMS, enabling teachers to create speaking assignments, collect audio submissions, and sync grades and feedback back to Canvas gradebook within the existing learning management system.
Generates comprehensive, specific feedback on linguistic elements of student speech including pronunciation errors, fluency issues, grammar patterns, and vocabulary usage. Provides actionable insights for student improvement.
+3 more capabilities
Store and automatically index high-dimensional vector embeddings in a managed, scalable database without manual infrastructure provisioning. The system handles index optimization and partitioning transparently.
Query stored vectors to find semantically similar items by computing distance metrics between query embeddings and indexed vectors. Returns ranked results based on relevance in sub-100ms latency.
Serve as the retrieval component in Retrieval-Augmented Generation pipelines, providing relevant context documents to language models for grounded responses.
Provide a generous free tier (1 pod, 100K vectors) enabling teams to build and test real applications before committing to paid plans.
Execute searches using both dense vector embeddings and sparse keyword-based vectors simultaneously, combining results to improve relevance by capturing both semantic and lexical similarity.
Filter vector search results based on metadata attributes (tags, categories, timestamps, custom fields) before or during similarity search, enabling faceted and conditional retrieval.
Insert, update, or replace multiple vectors and their metadata in a single batch operation, optimizing throughput for bulk data ingestion without individual API calls.
Pinecone scores higher at 71/100 vs Speakable at 48/100. Speakable leads on ecosystem, while Pinecone is stronger on adoption and quality.
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Partition vector data within a single index using namespaces, enabling logical separation of data (by user, tenant, or dataset) without creating separate indexes.
+4 more capabilities