Define your output
Don't settle for unstructured text. Use our visual builder to define exactly what you want to extract—sentiment scores, categories, or any structured data you need.
Iterate on the "Why"
Test your criteria against real data samples immediately. If the AI gets it wrong, tweak the prompt and re-run just that row until it clicks. Version control included.
Scale with Confidence
Once your process is solid, run it on thousands of rows in parallel. Helioscope handles the infrastructure, rate limits, and verification. You just get the structured data.
Who is Helioscope for?
Teams turning unstructured data into structured insights.
View All Roles & TemplatesCompetitive Intelligence Analyst
- Track competitor feature launches and pricing changes from scattered web sources into a unified feed
- Extract positioning strategies from product announcements to map competitive landscape
- Score competitive threats by analyzing product reviews and user sentiment patterns
Customer Insights Manager
- Discover unexpected pain points by coding 50,000 support tickets beyond predefined categories
- Extract feature requests from unstructured feedback and map them to product roadmap themes
- Identify churn signals in customer communications before they escalate
Legal Discovery Associate
- Screen thousands of emails for privilege, relevance, and potential misconduct patterns
- Extract contractual obligations and liability clauses from diverse agreement formats
- Identify conceptual similarities across case documents that keyword search would miss
The trust problem
Off-the-shelf AI is cheap but unreliable. How do you know what's accurate and what's hallucinated? Helioscope lets you iterate quickly on your process, review thousands of results without rabbit-holing on individual examples, and understand exactly where the AI struggles so you can refine your approach. You stay in control
Steep marginal costs
Manual review costs $1+ per item. Doing it yourself or with a small team doesn't scale. At 10,000 rows, you've spent a small fortune
Wide Research
Explore thousands of examples to discover patterns and refine your process. Once you've got it right, run at scale with near-zero marginal cost and trusted results
Prohibitive fixed costs
Custom ML models, BPO, and rules engines require $10k-$40k upfront—and you need to know exactly what you want before you start
Structured data you can trust
Export results with confidence. Every output is traceable to your process, versioned, and validated through your iteration. Ready to analyze, integrate, or act on—without second-guessing the data