AI app for it and development · no coding needed
Source-linked AI application build and operations console
Reduce tool sprawl and traceability gaps while keeping the team's own workflow.
Made for: Software teams building and running AI-powered applications and assistants
What it does for you
The problem
AI application work is split across separate prompt, retrieval, agent, diagnostics and observability tools, so teams cannot trace answers, permissions or decisions end to end.
What it gives you
Reviewed, source-linked assistant and administrator console
What you give it
Promptsfunctionsvector storesdatasetspermission rulesmodel settings
Build your own version of Hyperaide, Pulse and more
One app with what these 6 AI tools do, yours to keep and change: Hyperaide, Pulse, GPT Computer Assistant, OpenCopilot v2, LastMile AI, LM-Kit One.
Everything these tools do, in one app
- Unified AI platform Combines multiple AI components such as prompts, functions, and vector stores into a single interface.Found in Hyperaide
- Instant API generation Provides ready-to-use APIs for integrating AI capabilities into applications.Found in Hyperaide
- Built-in analytics Monitors AI performance and usage through observability tools.Found in Hyperaide
- Developer-friendly interface Offers an interface designed to accelerate AI application development.Found in Hyperaide
- Permission-aware retrieval Rebuilds permission scope on every query to only surface information the user can access.Found in Pulse
- Cited answers Links every line in an answer to its source for verification.Found in Pulse
- Decision tracking Captures decisions with context and detects contradictions with past decisions.Found in Pulse
- Expert finder Identifies the right person for a topic within the organization.Found in Pulse
- Agent workflows Runs agents that require human approval before executing and can produce reports.Found in Pulse
- MCP integration Works inside tools like Claude, Cursor, and ChatGPT via the Model Context Protocol.Found in Pulse
- Conversational troubleshooting Provides interactive guidance through natural language conversations for computer issues.Found in GPT Computer Assistant
- Step-by-step instructions Offers detailed steps for resolving software and hardware issues.Found in GPT Computer Assistant
- System diagnostics Performs system diagnostics and suggests optimization tips.Found in GPT Computer Assistant
- Multi-OS support Supports multiple operating systems and common applications.Found in GPT Computer Assistant
- Real-time adaptive responses Adapts responses based on user input and problem context in real time.Found in GPT Computer Assistant
- Context-aware code completion Provides code suggestions that adapt to different programming languages and frameworks.Found in OpenCopilot v2
- Real-time error detection Detects errors and offers suggestions to catch bugs early during development.Found in OpenCopilot v2
- IDE integration Integrates with multiple IDEs including Visual Studio Code and JetBrains products.Found in OpenCopilot v2
- Code refactoring suggestions Suggests refactoring to improve code readability and maintainability.Found in OpenCopilot v2
- Customizable settings Allows users to tailor the assistant's behavior to their workflow.Found in OpenCopilot v2
- Notebook-inspired interface Provides an interactive environment to iterate on prompts, compare models, and build templates.Found in LastMile AI
- Multi-modal model access Supports a range of generative AI models across text, image, and audio from various providers.Found in LastMile AI
- Data customization Connects large language models to specific datasets such as PDFs, text files, and HTML.Found in LastMile AI
- Batch execution Runs templates on large datasets to automate processes and evaluate app performance.Found in LastMile AI
- Collaboration and sharing Allows workbooks to be shared and cloned, with commenting features for team-based development.Found in LastMile AI
- Local deployment Runs on your own infrastructure on Windows, Linux, or macOS, from a single machine to production servers.Found in LM-Kit One
- Model and data control Users control which models run, what data those models can access, and what actions they can take.Found in LM-Kit One
- Agent building Combines multiple functions through the API to build AI agents.Found in LM-Kit One
- Free evaluation Offers a free tier or evaluation period without time limits for testing.Found in Hyperaide, GPT Computer Assistant, OpenCopilot v2 and 2 more
How it works, step by step
- Combine prompts, functions and vector stores in one interface
- Generate ready-to-use APIs for AI capabilities
- Monitor AI performance and usage with built-in analytics
- Provide a developer interface for building AI applications
- Rebuild permission scope on every query
- Link every answer line to its source
- Capture decisions with context and flag contradictions
- Identify the right person for a topic
- Run agent workflows that require human approval and produce reports
- Operate inside Claude, Cursor and ChatGPT via MCP
- Guide computer troubleshooting through conversation
- Give step-by-step software and hardware instructions
- Run system diagnostics and suggest optimization tips
- Support multiple operating systems and common applications
- Adapt responses to user input and problem context in real time
- Suggest context-aware code completions across languages and frameworks
- Detect errors in real time during development
- Integrate with Visual Studio Code and JetBrains IDEs
- Suggest refactoring for readability and maintainability
- Allow customizable assistant behavior
- Offer a notebook-inspired prompt and model comparison environment
- Access text, image and audio models from multiple providers
- Connect models to PDFs, text files and HTML datasets
- Run templates on large datasets for batch execution and evaluation
- Share and clone workbooks with commenting
- Deploy locally on Windows, Linux or macOS
- Let users control models, data access and actions
- Build agents by combining functions through the API
- Offer a free evaluation tier without time limits
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 Source-linked AI application build and operations console 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 Source-linked AI application build and operations console 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 links5 KB
- questions.mdQuestions to answer before you build2 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare26 KB
- prompt-vps.mdThe same build on your own server (Docker)26 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria13 KB
- demo/index.htmlThe working demo on sample data196 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 tool sprawl and traceability gaps while keeping the team's own workflow. For software teams building and running AI-powered applications and assistants, convert prompts, functions, vector stores, datasets, permission rules and model settings into a reviewed, source-linked assistant and administrator console. The benefit is a testable hypothesis, measured through accepted AI features per developer hour and traced answers with correct permission scope; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect prompts, functions, vector stores, datasets, permission rules and model settings, then follow this sequence: 1. Combine prompts, functions and vector stores in one interface. 2. Generate ready-to-use APIs for AI capabilities. 3. Monitor AI performance and usage with built-in analytics. Resolve uncertain cases with qualified reviewers, approve the reviewed, source-linked assistant and administrator console, and measure accepted AI features per developer hour and traced answers with correct permission scope against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, permission checks, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final security, permission and production decisions remain with the development team. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, permission scope, decision context and usage permissions. Development teams approve substantive changes and deployment scope. One approved model set, one deployment target and one IDE integration; final security, permission and production decisions remain with the development team. 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 approved model set, one deployment target and one IDE integration; final security, permission and production decisions remain with the development team. Implement one approved input format, a bounded representative case set and the first two task modules: combine prompts, functions and vector stores in one interface; generate ready-to-use APIs for AI capabilities. Support the third module with operator review: monitor AI performance and usage with built-in analytics. 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
Team-owned repositories, authorized datasets and permitted model providers. Cloud or local deployment, IDE plugins, MCP clients and analytics 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: Build workspace, Source-linked assistant, Admin console. Use a project list, a central notebook-style canvas for prompts, functions and vector stores, and a right-hand panel for sources, permissions, model settings and comments. Let users compare model and prompt versions side by side. Display draft, in review and approved states. Provide a cited answer view with line-level source links and an approval queue for agent actions. Make the task-specific outcome reviewed, source-linked assistant and administrator console visible beside its evidence, review state and value baseline.
