Documentation Index

Fetch the complete documentation index at: https://help.1nspect.app/llms.txt

Use this file to discover all available pages before exploring further.

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AI During Capture

The Capture Screen has four AI tools and one zero-cost local feature, all designed to reduce the writing burden during an inspection without removing your judgment. This article covers what each one does on-site, when to use it, what it costs, and what to do when it fails. For the platform-wide AI reference (including the web admin’s AI Draft, Polish, and SPO tools), see AI Features Guide. For per-operation IT costs across the platform, see the Complete AI Operation Cost Reference.

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The five tools at a glance

[SCREENSHOT: ai-tools-row.png — mobile, the AI TOOLS row on the Capture Screen showing the four AI buttons: ✦ AI Generate, 👁 Vision Scan (with NEW badge), 🪄 Library, and ℹ️ Sys Info — plus FREE Library chips visible above.] A subtle design point: ✦ AI Generate is the same button in both Vision and non-Vision modes — the 👁 toggle just changes the path the AI Generate request takes. You don’t tap Vision Scan to run it; you toggle Vision on, then tap AI Generate.

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FREE (Library) chips — the zero-cost first stop

The cheapest, fastest, most under-used tool in 1nspecT. Most inspectors discover its value the second time they capture a finding type they’ve documented before.

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What it does

As you type the Caption, the app runs a local keyword match against your narrative library. Matching narratives appear as green chips labeled FREE (Library) just below the caption field. Tap a chip to insert that narrative into the Narrative field.

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Why it’s worth optimizing for

• 0 IT — completely free, runs on-device
• Works fully offline — no network required
• Fast — appears as you type, no API round-trip
• Library grows with use — every narrative you save back to your library (via the Save to Library action on the narrative field) becomes a future FREE chip

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How to make more chips appear

The library is keyword-matched, so:

• Caption with specific words matches better than generic captions. “Double-tapped breaker at main panel” matches more chips than “Issue at panel.”
• Build the library over time. After writing or editing any narrative, tap Save to Library below the narrative field. Cost: 1 IT to save (the system de-identifies the text). Future captures with similar captions auto-suggest it for 0 IT.
• Your library is private to your tenant. Other inspection companies’ libraries never feed into yours.

For semantic search of the same library when keyword match returns nothing useful, see 🪄 Library Search below.

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✦ AI Generate — text-only narrative

The most-used AI tool on 1nspecT. Generates a full CAR-format narrative from your caption and the active section/subsection context.

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What CAR means

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How to use it

1. Write a specific caption (the more specific, the better the narrative).
2. Select the correct section and subsection — the AI uses these to calibrate severity and language.
3. Make sure the 👁 Vision toggle is OFF.
4. Tap ✦ AI Generate.
5. In ~2 seconds the narrative populates the Narrative field.
6. Review and edit. The AI produces a strong first draft — never publish it without reading it.

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Cost: 2 IT

This was previously documented as 4 IT in older release notes — that’s out of date. Current cost is 2 IT per call. See Verification Notes for the drift history.

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Best for

• Common, well-described findings where your caption already tells the story
• Inspectors with limited writing time on-site
• Standardizing language across inspectors at the same company
• Captures where you don’t have a photo (e.g. functional tests, non-visual issues)

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Tips

• Write the caption like the narrative isn’t going to exist. If the caption alone wouldn’t be clear to a customer, the AI won’t have enough to work with either.
• The AI follows TREC SoP language by default for Texas residential inspections. If your template is for a different SoP, the language calibrates to that template’s standards.
• The AI does not cite specific building codes (GFCI, AFCI requirements, etc.) because code citations require knowledge of permit date, local amendments, and AHJ interpretation. Add code references manually if your standards require them.

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👁 Vision Scan + ✦ AI Generate — photo-grounded narrative

When you want the AI to write a narrative based on what’s actually in the photo, not just the caption.

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How to use it

1. Capture a photo first (the 📷 button on the Capture Screen).
2. Write a caption with meaningful context — at least 5–6 words describing what you’re looking at.
3. Toggle 👁 Vision Scan ON (the button has a NEW badge until you tap it).
4. Tap ✦ AI Generate.
5. The AI sends the photo + caption to a multimodal model. In 10–15 seconds the narrative appears, often with a confirmation alert: ”🔬 Vision Analysis Complete — Professional narrative generated from photo. Review and edit as needed.”

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Cost: 8 IT

Replaces the 2 IT text-only cost — not in addition to it. One tap on AI Generate with Vision toggled on = 8 IT total.

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Pre-flight balance check

Vision Analysis pre-checks your tenant’s IT balance before sending the photo to the model. If the balance is below 8, the call is rejected immediately with:

Insufficient IT Tokens Vision Analysis requires 8 IT. Purchase a top-up pack in Settings.

The “Settings” referenced here is the web admin Settings (mobile has no top-up flow). See Settings & Tokens → IT Tokens for the full explanation.

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Best for

• Complex visual deficiencies where the photo tells more than the caption could (e.g. unusual moisture staining patterns, atypical structural defects).
• Verification that the narrative matches the photo before you save the finding — Vision-generated narratives are unlikely to describe something not visible in the image.
• High-confidence safety hazards where the AI’s grounded analysis adds credibility.

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Co-Inspector — the secondary-findings feature

After Vision Analysis returns, the AI looks for additional deficiencies in the same photo that are not the primary finding. If it finds any high-confidence ones (it targets >85% confidence only), a review modal slides up automatically: [SCREENSHOT: co-inspector-modal.png — mobile, the Co-Inspector Review Modal showing the heading ”🔬 Co-Inspector Spotted More” with 2 proposed findings, each with a + Queue and ✕ Skip button, plus a “Done — Skip Remaining” button at the bottom.] The modal title is ”🔬 Co-Inspector Spotted More”. For each proposed finding: After you accept findings, the workflow becomes:

1. Finish writing/saving your current finding.
2. Tap 💾 Save + New.
3. On the cleared form, tap a green 📋 pill at the top of the Capture Screen — the caption and narrative pre-populate for that pending finding.
4. Capture a new photo of that specific deficiency (so the report has the right hero photo).
5. Adjust section/subsection if needed.
6. Save.
7. Repeat until all pills are cleared.

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Strict criteria — what Co-Inspector will and won’t flag

Co-Inspector findings are suggestions. Always verify visually before saving. The AI is a second pair of eyes; your judgment is final.

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Cost: included in the 8 IT Vision Analysis

Secondary findings do NOT add extra cost — the Co-Inspector pass is bundled into the 8 IT Vision Analysis call.

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🪄 Library Search — semantic AI matching

When FREE chips don’t surface what you’re looking for, semantic Library Search casts a wider net.

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How it differs from FREE chips

• FREE chips: local keyword match. “shingles damaged” matches “damaged shingles, granule loss visible” because both contain “shingles” and “damaged”.
• 🪄 Library: AI semantic match. “roof issue” might match “damaged shingles, granule loss visible” because the AI understands they’re semantically related.

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How to use it

1. Write a caption (any quality works).
2. Tap the 🪄 button in the AI Tools row.
3. Up to 3 ranked suggestions appear as chips below the caption.
4. Tap a chip to insert that narrative.

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Cost: 1 IT

Each call costs 1 IT regardless of how many suggestions it returns.

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Best for

• Finding a past narrative for a finding type you’ve documented before when you can’t remember the exact wording
• Captures where the caption is short or generic
• Inspectors with large libraries (>100 entries) where keyword search misses lots

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ℹ️ Sys Info — manufacturer / model / age extraction

When you photograph an equipment label (water heater nameplate, HVAC manufacturer plate, electrical panel sticker), the Sys Info tool extracts manufacturer, model, and year of manufacture from the label.

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How to use it

1. Capture a clear photo of the label.
2. Write a caption that mentions the equipment (e.g. “Water heater label, garage”).
3. Tap the ℹ️ Sys Info button.
4. The extracted information populates the relevant informational fields on the finding.

If the caption mentions a manufacturer label but no photo was captured (e.g. “Furnace serial number — Carrier, 1998”), the tool falls back to a text-only extraction.

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Cost

• 3 IT with a photo (OCR via Document AI)
• 1 IT as a text-only fallback (heuristic extraction from the caption)

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Best for

• HVAC equipment (where age and model number drive aging-equipment alerts)
• Water heaters (where age is the primary deficiency indicator)
• Electrical panels (where manufacturer matters — Federal Pacific, Zinsco, etc.)
• Roof access ladders, attic equipment

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The aging-equipment toast

A separate but related feature: when Sys Info extracts a manufacture date that puts the equipment near or past its expected service life, an aging-equipment toast appears as you save the finding. The toast surfaces the calculated age and a recommended action (e.g. “Water heater is 14 years old — typical service life is 8–12 years. Recommend evaluation for replacement timing.”).

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What happens when AI is unavailable

The mobile app is designed to fail gracefully when the AI service can’t be reached. Two distinct failure modes:

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“⚠️ AI Offline” — network or service down

If the AI service is unreachable (network outage, service maintenance), AI Generate returns this alert:

⚠️ AI Offline AI service unreachable. A template CAR narrative has been pre-filled — edit as needed.

The narrative field is populated with a template CAR narrative (boilerplate text scaffolded for your subsection). You edit it like a regular narrative and save. No IT cost is deducted for failed calls.

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”⚠️ AI Unavailable” — service returned an error

If the AI service responds but returns an error code:

⚠️ AI Unavailable AI service returned 503. A template CAR narrative has been pre-filled — edit as needed.

Same outcome — template fallback, no IT cost, edit and continue.

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Other error states

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A practical AI workflow

For most findings, the cheapest-and-best sequence is:

1. Capture photo first if relevant.
2. Write a specific caption (6–15 words, mentioning the section, the component, and the condition).
3. Look at the FREE chips — if one matches, tap it. Done. 0 IT.
4. If not, decide:
   • Caption clear, photo helpful → toggle 👁 Vision ON, tap ✦ AI Generate (8 IT, photo-grounded).
   • Caption clear, no photo needed → keep Vision OFF, tap ✦ AI Generate (2 IT, text-only).
   • Need to find a past narrative → tap 🪄 (1 IT, semantic).
5. Review the narrative — edit anything that doesn’t match what you actually observed.
6. If you wrote a particularly good narrative, tap Save to Library (1 IT) to make it a future FREE chip.

Average inspectors using this workflow spend 30–80 IT per inspection (depending on the number of findings and Vision use). Build the library over the first 5–10 inspections and the per-inspection cost drops as FREE chips replace AI calls.

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What AI does NOT do

• Auto-publish. Every AI narrative populates the field — you must save the finding for it to commit. You can edit or reject any AI output.
• Replace inspector judgment. Co-Inspector findings are suggestions the AI is >85% confident about, but you are the licensed inspector. Final decision is yours.
• Cite specific codes. No GFCI/AFCI/clearance/load-calc citations — those require knowledge the AI doesn’t have.
• Identify the inspector personally. Narratives don’t say “I observed” by name; they use the template’s inspector-voice configured in your company settings.
• Cost money on rejection. Rejecting an AI suggestion (or not saving the finding) does not refund the cost. AI calls deduct on send, not on accept.

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Related articles

• Capture Screen — where the AI Tools row lives
• Photos & Media — capturing the photo that powers Vision and Sys Info
• AI Features Guide — platform-wide AI reference (mobile + web)
• Settings & Tokens — what to do when IT runs out
• Troubleshooting → AI errors — additional error-state reference