AI Duplicate Record Matching
Surface records that probably describe the same thing, with the evidence for each match.
What it adds
A duplicate detection pass combining deterministic keys with semantic similarity, producing reviewable candidate pairs.
What your agent is told to do
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What your agent is told to do
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Run deterministic matching first on the identifiers the domain already trusts — email, tax number, external system ID, normalized phone — and settle those cases before any model is involved. Semantic similarity is for what deterministic rules miss, not a replacement for them.
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Use similarity to generate candidates, then score each pair on a field-by-field basis and store which fields agreed, which conflicted, and by how much.
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Present matches as a review queue showing the two records side by side with the contributing fields highlighted, and make dismissal permanent so the same pair does not resurface every run.
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Scope every comparison to a single tenant and to records the reviewer may see. A duplicate finder that reaches across a workspace boundary is a data leak with a helpful interface.
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Make merges reversible for a defined window: keep the pre-merge state, record which record absorbed which, and preserve references from both so links elsewhere in the app do not break.
Edge cases it handles
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Edge cases it handles
8- Neither signal is sufficient alone. Deterministic identifiers catch the obvious cases, similarity catches the messy ones, and a pair should surface only when the combined evidence holds up field by field.
- Common names, shared support addresses, and generic descriptions produce large false-positive clusters. Require corroboration on a second independent field before proposing a merge on a name alone.
- Every candidate must show why it was proposed. A confidence percentage with no visible evidence gives a reviewer nothing to reason about and trains them to approve everything.
- Comparison must never cross tenant, workspace, or permission boundaries, and the review queue must exclude pairs where the reviewer cannot see both sides.
- Merges must be confirmed by a person or governed by rules narrow enough to be safe, and must be reversible. An automatic merge on a wrong pair destroys two records and the history that explains them.
- Field-level conflicts need an explicit resolution step. When two records disagree on an address or a phone number, the reviewer chooses rather than the newer record silently winning.
- Duplicate detection over a large table is expensive. Block candidates on cheap keys before scoring, cap the work per run, and process through the app's existing background-job system rather than during a request.
- Dismissals and merges must be recorded in an audit trail with who acted and when, since a merge is one of the least recoverable operations in the app.
Definition of done
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Definition of done
9- Deterministic identifier matches are resolved before semantic scoring runs.
- Every candidate pair records the fields that agreed and the fields that conflicted.
- The review interface shows both records with the matching evidence highlighted.
- Comparisons and the review queue respect tenant and permission boundaries.
- Merges require confirmation, resolve field conflicts explicitly, and are reversible within a defined window.
- Dismissed pairs do not reappear in later runs.
- Detection runs in the background with a bounded cost per run.
- The feature matches the existing design system.
- No existing functionality is broken.
Related features
Model Fallback
Model Fallback
Keep AI features working when the primary model provider fails.
What it does
A bounded retry path that reruns a failed AI task on a compatible alternative model and discloses the substitution.
How it works
- 1 Classify failures before reacting. Provider outages, capacity rejections, rate limiting, and timeouts are worth retrying elsewhere; a refusal, an invalid request, or an authentication error will fail identically on any destination and must surface immediately.
- 2 Honour rate limiting with backoff before moving on. Retrying instantly against a provider that asked you to wait deepens the outage and can extend the penalty period.
- 3 Allow fallback only to models that can honour the same output shape and the same tool behaviour as the original. A substitute that returns a different structure turns a provider outage into a data problem inside the app.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/model-fallback
AI Import Column Mapping
AI Import Column Mapping
Guess how an uploaded file's columns line up with the app's fields, then ask.
What it does
A proposed mapping from uploaded columns to destination fields, shown with samples and confirmed before the import runs.
How it works
- 1 Take the uploaded headers and a small sample of rows and propose a mapping to the destination fields. Match on exact and near-exact header names first and only consult the model for the columns that remain unresolved.
- 2 Constrain the candidate set to fields the current user and the importer are actually permitted to write. A mapping that targets a read-only, computed, or privileged field must never be offered, whatever the header says.
- 3 Show the proposal as a table: source column, sample values, destination field, confidence, and the transformation that will be applied. A user cannot approve a mapping they cannot see the effect of.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-import-column-mapping
AI Audit Trail
AI Audit Trail
Keep a reviewable record of what the AI saw, produced, recommended, and actually did.
What it does
A per-run record of inputs, sources, configuration, output, approvals, and executed actions, restricted and retained by purpose.
How it works
- 1 Write one record per run covering the feature invoked, the configuration and prompt version in force, the sources retrieved, the tools called, the output produced, who approved it, and what was carried out.
- 2 Distinguish a draft from an executed action explicitly in the record. A trail that shows only what text was produced cannot answer the question that matters in a review, which is whether anything happened.
- 3 Redact at the moment of writing. Strip credentials, tokens, and payment details before the record is stored, and keep only the personal content genuinely needed to understand the decision.
Copy the prompt
No account needed
Add this feature to my app:
https://addthisfeature.com/x/ai-audit-trail
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.