AI app for science and research · no coding needed
Visual identification and explanation workspace
Reduce manual identification effort while keeping a reviewable evidence trail.
Made for: Researchers, field scientists and analysts who must identify and explain what is shown in a photo or video
What it does for you
The problem
Visual identification is scattered across single-purpose apps, so explanations, confidence and source evidence cannot be reviewed or reused.
What it gives you
Reviewer-approved visual identification reports linked to source evidence
What you give it
Licensed photosvideosreference collectionsdomain taxonomies
Build your own version of Chance AI: Visual Reasoning, Chance AI for iOS and more
One app with what these 8 AI tools do, yours to keep and change: Chance AI: Visual Reasoning, Chance AI for iOS, Chance: Visual Intelligence, Fuyu-8B, Aya Vision, RockPic, seefood, Pixplain by Merlin AI.
Everything these tools do, in one app
- Photo-based identification Lets users snap or upload a photo and get an identification of what is shown.Found in Chance AI: Visual Reasoning, Chance AI for iOS, RockPic and 1 more
- Contextual explanations Provides history, background, and hidden details about the photographed subject.Found in Chance AI: Visual Reasoning, Chance AI for iOS
- Image and video analysis Analyzes both images and videos to extract visual insights.Found in Chance: Visual Intelligence, Aya Vision
- Object detection and classification Automatically detects and classifies objects within images or videos.Found in Aya Vision, seefood
- Real-time processing Delivers fast, immediate results for quicker decisions.Found in Chance: Visual Intelligence, seefood
- Multilingual support Works in multiple languages and can read answers aloud.Found in Chance AI: Visual Reasoning
- Automated tagging and categorization Organizes visual data by automatically tagging and categorizing content.Found in Chance: Visual Intelligence
- Customizable settings Allows users to adjust parameters to fit different types of visual data and needs.Found in Chance: Visual Intelligence, Aya Vision
- Batch processing Processes large volumes of visual data at once.Found in Aya Vision
- Detailed reporting and visualization Generates reports and visualizations to present insights from visual data.Found in Aya Vision
- Confidence percentage Shows a confidence score to indicate how certain the identification is.Found in RockPic
- Stone-specific insights Provides details like rarity, magnetism, composition, and origin for rocks, gems, and jewelry.Found in RockPic
- Privacy-focused Operates with no ads, no tracking, and no collection of user data.Found in RockPic
- Text descriptions of images Generates readable, brief explanations that highlight key elements in an image.Found in Pixplain by Merlin AI
- Multiple image format support Accepts a variety of image file formats for input.Found in Pixplain by Merlin AI
- Integration with other tools Connects with popular platforms, APIs, or frameworks for seamless workflows.Found in Chance: Visual Intelligence, Fuyu-8B, Aya Vision and 2 more
- Fine-tuning options Allows adaptation of the model to specific domain requirements.Found in Fuyu-8B
- Live data updates Provides up-to-date information, including breaking news related to the image content.Found in Chance AI for iOS
How it works, step by step
- Accept photos and videos in common formats
- Identify what is shown and return candidate labels
- Show a confidence score beside each candidate
- Add contextual explanations, history and hidden details
- Detect and classify objects within images or videos
- Return results in real time for quick decisions
- Support multiple languages and read answers aloud
- Auto-tag and categorize visual content
- Adjust parameters for different visual data and needs
- Process large volumes of visual data in batches
- Generate reports and visualizations from the results
- Provide domain-specific insights such as rarity, composition and origin for rocks, gems and jewelry
- Produce brief readable text descriptions of images
- Keep processing private with no ads, tracking or collection of user data
- Connect to external platforms, APIs and frameworks
- Fine-tune the model for specific domain requirements
- Pull live updates, including breaking news related to the image content
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned reviewer-approved visual identification report linked to source evidence with source references and unresolved questions
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Visual identification and explanation workspace with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Visual identification and explanation workspace with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria11 KB
- demo/index.htmlThe working demo on sample data198 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce manual identification effort while keeping a reviewable evidence trail. For researchers, field scientists and analysts who must identify and explain what is shown in a photo or video, convert licensed photos and videos, reference collections and domain taxonomies into reviewer-approved visual identification reports linked to source evidence. The benefit is a testable hypothesis, measured through accepted identifications per analyst hour and corrections after review; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect licensed photos and videos, reference collections and domain taxonomies, then follow this sequence: 1. Accept photos and videos in common formats. 2. Identify what is shown and return candidate labels. 3. Show a confidence score beside each candidate. 4. Add contextual explanations, history and hidden details. 5. Detect and classify objects within images or videos. 6. Generate reports and visualizations from the results. Resolve uncertain cases with qualified reviewers, approve reviewer-approved visual identification reports linked to source evidence, and measure accepted identifications per analyst hour and corrections after review against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One fixed taxonomy and licensed reference set; final identification and explanation checks remain with qualified reviewers. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, identification accuracy and usage permissions. Qualified reviewers approve substantive identifications and publication scope. One fixed taxonomy and licensed reference set; final identification and explanation checks remain with qualified reviewers. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One fixed taxonomy and licensed reference set; final identification and explanation checks remain with qualified reviewers. Implement one approved input format, a bounded representative case set and the first two task modules: accept photos and videos in common formats; identify what is shown and return candidate labels. Support the third module with operator review: show a confidence score beside each candidate. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Researcher-owned media, authorized reference collections and permitted data sources. Cloud asset storage, media import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Case intake and references, Editable identification workspace, Review and delivery. Use a thumbnail gallery for cases, a large central viewer for the photo or video, and a right-hand panel for candidate labels, confidence, references and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant frame. Make the task-specific outcome reviewer-approved visual identification reports linked to source evidence visible beside its evidence, review state and value baseline.
