AI Structured Data Extraction
Turn documents and pasted text into a validated record a person approves before saving.
What it adds
A schema-driven extraction pipeline that converts unstructured input into a validated draft record with field-level evidence.
What your agent is told to do
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What your agent is told to do
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Define each target schema in the app with types, required fields, allowed values, and constraints, and validate every response against it before anything is stored. Treat the schema as the contract and the model output as an untrusted candidate.
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Present the result as a draft the user reviews field by field, with the extracted value beside the passage it came from. Approval writes the record; nothing writes itself.
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Distinguish three states per field: found in the source, absent from the source, and inferred. Collapsing absent into empty hides the difference between a document that omitted a value and one the extractor failed on.
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On invalid output, attempt one bounded repair pass constrained to the failing fields, then give up and surface the document for manual entry. Do not loop retries against a token budget the workspace has to pay for.
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Field-level extraction of names, dates, and amounts belongs to AI Entity Extraction; call it for candidate values and keep this feature responsible for schema assembly, validation, and the review flow.
Edge cases it handles
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Edge cases it handles
8- Output that does not satisfy the schema must be rejected, not patched into shape. Silently coercing a string into a number or dropping an unexpected key produces records that look valid and are not.
- A field the source never mentioned must be recorded as absent, and a field the model reasoned toward must be marked inferred and flagged in the review interface. The user needs to know which values were read and which were guessed.
- Every field that drives money, identity, scheduling, or access needs a stored pointer to its supporting passage, so a disputed record can be checked against its source months later.
- High-impact writes — creating an invoice, updating a customer record, scheduling something billable — must pass through explicit human approval regardless of confidence.
- Documents longer than the input ceiling must be chunked with overlap and merged deterministically, with conflicts between chunks surfaced for review rather than resolved by last-write-wins.
- Scanned and image-based documents may yield unusable text. Detect that before spending a run, and tell the user the document could not be read rather than returning an empty record.
- A refusal on sensitive content is a legitimate outcome. Show it as a refusal with a manual entry path, not as an extraction failure the user will retry indefinitely.
- Exclude payment credentials, government identifiers, and health details from anything sent to the provider unless the workspace has explicitly opted in, and record what was sent for each run.
Definition of done
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Definition of done
9- Every extraction is validated against a stored schema before any write occurs.
- Each field is labelled found, absent, or inferred, and the labels are visible in review.
- Fields with material consequences carry a stored reference to their source passage.
- No record is created or updated without explicit user approval of the draft.
- Invalid output triggers at most one bounded repair pass before falling back to manual entry.
- Unreadable documents and provider refusals produce distinct, actionable messages.
- Token and cost ceilings are enforced per run and per workspace.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
AI Insight Cards
AI Insight Cards
Turn a report into a few grounded cards, each tied to a number the app calculated.
What it does
A small set of cards summarising notable movements in a report, each linked to the query and figure that produced it.
How it works
- 1 Detect candidate movements in the app first, using the report's own aggregations. The model ranks and phrases the candidates it is given; it does not go looking for them and it does not produce the arithmetic.
- 2 Bind each card to the query, metric, and period that support it, and make the card link through to the filtered view so a reader can check it in one click.
- 3 Set a materiality threshold before generation, in both relative and absolute terms, so a swing on a metric with three events does not outrank a real change on a metric with thousands.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-insight-cards
AI Trend Analysis
AI Trend Analysis
Explain what a chart is actually showing, in sentences backed by computed numbers.
What it does
A written summary attached to a time series or metric set, describing direction, magnitude, and comparison period.
How it works
- 1 Compute every number in ordinary code before the model is involved: the change, the rate, the comparison period, the baseline, the seasonal adjustment. The model writes prose about figures it is given and must never produce a figure of its own.
- 2 State the comparison window and the time zone in the summary text, resolved from the workspace's configured zone rather than the server's. A change described as week over week is meaningless without saying which weeks.
- 3 Instruct the model to describe what the data shows and to stop there. Do not let it assert causes, attribute movement to campaigns or releases, or predict what happens next.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-trend-analysis
AI Key Point Extraction
AI Key Point Extraction
Reduce long content to the decisions, facts, and requests that actually matter.
What it does
A short list of the salient points in a long document or thread, each tied back to the passage it came from.
How it works
- 1 Apply this where length is the problem: long threads, meeting notes, call transcripts, lengthy tickets, multi-page documents. Content that already fits on a screen does not need extracting.
- 2 Classify each point by kind — decision, fact, risk, request, open question — so a reader can scan for the category they came for instead of reading a flat list.
- 3 Carry a reference to the source passage with every point and let the reader jump to it. A point nobody can verify is a claim, not a summary.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-key-point-extraction
How it works
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Copy the link
Grab the Markdown instruction URL for this feature.
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Give it to your AI
Paste it into Claude Code, Cursor, v0, Lovable — whatever you build with.
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It inspects, then implements
Your agent reads your existing app first, then adds the feature to fit it.
Works with your stack
These instructions are written to adapt. They tell the agent to detect your framework, match your existing design system, and reuse what you already have — rather than assuming a particular stack.
Need it tighter than that? Customize the feature and tell it exactly what you're running.