Semantic Insights.Generated at Scale.
Queryboost is the next-generation text analytics platform that transforms raw text into business-ready data. Unlock deep semantic insights that power analytics, decisions, and automation at scale.
Introducing the AI data processor for semantic insights
The Queryboost AI data processor empowers analytics teams to query and transform raw text into structured semantic attributes, metrics, and insights that plug directly into analytics workflows.
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| 10234 | I've been waiting for 2 hours and still haven't gotten help. This is unacceptable! |
| 10235 | Thank you so much for your help! This solved my problem perfectly. |
| 10236 | I'm still experiencing the same error after following all your instructions. Nothing has changed. |
| 10237 | Perfect! The new update fixed everything. I really appreciate the quick response! |
| 10238 | The problem keeps happening. I don't think this workaround is going to cut it. |
Reliable, Analytics-Ready Outputs
AI that understands your target schema and consistently generates structured, schema-compliant attributes, giving analytics teams clean, trustworthy data they can use immediately in BI tools and workflows.
Cost-Effective Large-Scale Processing
Our unique distributed GPU architecture delivers high throughput and efficient resource utilization at scale, reducing the cost of processing millions of raw text records.
Integrates Anywhere Your Data Lives
Process text data directly from your existing systems with no heavy integration work. Connect any data source and start generating semantic insights on demand and in real time.
Powered by Queryboost-4B, our purpose-built language model for text analytics and semantic insight generation at scale
Our 4B model achieves best-in-class structured output accuracy within its weight class, outperforming leading 4B and even 14B open-weight models on reading comprehension and natural language inference benchmarks.
Benchmark Details: Structured output accuracy measures the percentage of model outputs that both conform to a predefined JSON schema and contain the correct answer. Each benchmark (HellaSwag, MultiNLI, RACE, BoolQ, SQuAD 2.0) was adapted for schema-constrained decoding evaluation, requiring the model to produce structured JSON outputs instead of free-form text. Results represent zero-shot performance. HellaSwag measures commonsense reasoning, MultiNLI tests natural language inference, RACE evaluates reading comprehension, BoolQ assesses yes/no question answering, and SQuAD 2.0 measures question answering with unanswerable questions.
Model Description: Queryboost-4B LLM was post-trained for general-purpose text analytics (e.g., classification, extraction, abstraction, reasoning, open-ended question answering, and more), schema-aware decoding, and structured output generation.
