AI Entity Extraction
Pull names, dates, places, and amounts out of free text with the source spans kept.
What it adds
Extraction of typed entities from unstructured text, each carrying its original wording, a normalized value, and its position in the source.
What your agent is told to do
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What your agent is told to do
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Fix the entity types up front — people, organizations, locations, products, dates, amounts, identifiers — and reject anything outside that set. An open-ended extractor produces a taxonomy nobody can query.
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Require a source span for every entity: the offset and the exact original text. Without it a user cannot check the extraction, and a wrong value becomes indistinguishable from a right one.
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Keep both forms of every value. Store the original wording as written and a normalized form beside it, so a display can show what the document said while a query can match across spellings.
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Validate dates, currency amounts, phone numbers, and structured identifiers with the app's own deterministic parsers after extraction. The model is good at finding candidates and unreliable at formatting them.
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This feature works within a single piece of text and returns spans. Assembling whole records against a schema belongs to AI Structured Data Extraction; have that feature call this one for candidate values rather than each maintaining its own extraction path.
Edge cases it handles
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Edge cases it handles
8- Every entity must be traceable to a span in the source, and the interface should highlight it on request. An extraction that cannot be located in the original text must be dropped.
- Normalization must never overwrite the original. A company written three ways in one document needs one canonical value and three preserved surface forms, because the wording sometimes matters more than the match.
- The same entity appearing repeatedly should be returned once with all its occurrences, and genuinely ambiguous references should be returned as separate candidates rather than merged on a guess.
- Do not attach an extracted entity to an existing record automatically. Link only above a high confidence threshold, only where the current user may see the target record, and always with a visible way to undo.
- Run every date, number, and identifier through a real parser and discard whatever fails. A model-produced date that no parser accepts is not a date, and ambiguous day-month ordering must be resolved against the document's locale or left unresolved.
- Overlapping and nested spans occur naturally, such as a city inside an organization name. Define which wins and apply it consistently instead of returning both as peers.
- Long documents must be chunked with overlap so entities are not severed at a boundary, and offsets must be mapped back to the original document rather than to the chunk.
- When the provider fails or the response is truncated, save nothing partial. A half-extracted document presented as complete is worse than one marked unprocessed.
Definition of done
8
Definition of done
8- Every returned entity carries a type from the fixed set, a source span, an original form, and a normalized form.
- Dates, amounts, and identifiers are validated by deterministic parsers and invalid ones are discarded.
- Repeated mentions are grouped and ambiguous references remain separate candidates.
- Automatic linking to existing records respects confidence thresholds and the viewer's permissions, and is reversible.
- Chunked documents produce offsets correct against the original text.
- Failed or truncated runs leave the document marked unprocessed with no partial results stored.
- 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.