The Analysis Problem at Scale
AI is brilliant at analysis — extracting key fields from contracts, classifying customer feedback, comparing competing proposals. But doing this one document at a time in a chat window defeats the purpose. You spend more time on logistics than on understanding the results.
Automating analysis with AI means building a structured pipeline: define the extraction rules once, the classification logic once, and the comparison criteria once — then run the pipeline on any number of inputs. Feluda provides the visual canvas to build that pipeline without writing code.
Core Analysis Capabilities
Structured Extraction
An LLM block can be instructed to extract specific fields from any text: names, dates, monetary amounts, categories, claims, keywords. You define what to extract; the AI does the rest. Output is structured and consistent.
Content Classification
LLM Label blocks classify text into categories you define — "positive / negative / neutral," "billing / technical / legal," or any taxonomy. Each category routes to a different processing path in the workflow.
Multi-Source Comparison
Feed the same data through multiple LLM blocks (different providers or different prompts) and use a downstream block to compare, merge, or select the best result. This is multi-model quality control built into your analysis pipeline.
Synthesis and Summarisation
After extraction and classification, chain a summarisation block that synthesises all findings into a concise report. The final output captures what matters — nothing more.
Journal Tool
Enable the journal tool and your AI agents can write analysis results, notes, and summaries to a journal as they work. Browse entries later on the Journal page — a persistent, timestamped, searchable notebook.
Scheduled Analysis
The Schedule Manager runs your analysis workflow automatically — daily, weekly, or hourly. Incoming data gets analysed without your involvement. Results appear in the Output — or in the journal if the AI writes them there.
Real Analysis Workflows
Contract Analysis
Input contract text → Extract parties, dates, monetary terms, and renewal clauses → Classify risk level (low / medium / high) → Generate executive summary → Output. One pipeline processes an entire contract portfolio consistently.
Customer Feedback Triage
Input feedback → Classify sentiment (positive / negative / mixed) → Extract product mentions and feature requests → Route "negative" to a detailed analysis block → Summarise trends → Output. Enable the journal tool so the AI logs findings as it works. Run daily over new feedback.
Proposal Comparison
Feed two competing proposals into parallel extraction blocks → Pull out pricing, scope, timeline, and risk factors → A comparison block evaluates both side by side → Output a structured recommendation. Objective analysis, no bias.
Security Advisory Processing
Input security advisory feed → Extract CVE numbers, severity scores, affected products → Classify by your organisation's asset inventory → Generate prioritised alert report → Output. The AI can also write alerts to a journal for ongoing tracking.
Frequently Asked Questions
How can I automate analysis using AI tools?
Build a visual analysis pipeline in Feluda Studio: Input → Extract → Classify → Summarise → Output. Enable the journal tool if you want the AI to write findings as it works. Define the rules once and run the pipeline on any number of inputs. No code required.
Can AI extract structured data from unstructured text?
Yes. Instruct an LLM block with the fields you want (names, dates, amounts, categories). It extracts them from any unstructured text input and returns structured results you can use in further analysis steps.
Can I compare results from different AI providers?
Yes. Connect the same input to multiple LLM blocks with different providers — OpenAI, Anthropic, a local model. A downstream block compares or selects the best result, giving you multi-model quality control.
Does my data leave my computer?
Only if you choose a cloud AI provider. Feluda runs entirely on your desktop. If you use a local AI model, all analysis happens on your machine — your data never leaves your computer.
Analyse Smarter. Automate the Extraction.
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