AI app for it and development · no coding needed
Real-time multimodal agent deployment workspace
Run real-time voice, vision and data agents on one owned deployment surface instead of renting separate services.
Made for: Product and platform teams deploying real-time voice, vision and data agents
What it does for you
The problem
Real-time voice, vision and data agents are split across separate hosting, orchestration and tool-execution services, so teams cannot test, govern or run them as one owned system.
What it gives you
Tested, region-aware agent deployment with shared endpoints and usage reporting
What you give it
Approved modelscontainer imagesAPI tool definitionsguardrail rulesregion requirements
Build your own version of Outspeed, Hathora and more
One app with what these 3 AI tools do, yours to keep and change: Outspeed, Hathora, Eclatira.
Everything these tools do, in one app
- Real-time data processing Processes data quickly to provide timely insights.Found in Outspeed
- Voice-to-voice interaction Enables direct speech conversation without intermediate text transcription.Found in Eclatira
- Real-time vision processing Processes live camera or screen streams at 30 FPS for visual input.Found in Eclatira
- API and tool execution Executes actions across custom APIs, MCP servers, and third-party integrations.Found in Eclatira
- Guardrails for actions Lets developers limit which actions autonomous agents can take.Found in Eclatira
- Model flexibility Supports bringing your own models or switching underlying models.Found in Hathora, Eclatira
- Custom container deployment Allows deploying custom Docker containers.Found in Hathora
- Global low-latency deployment Deploys across multiple regions to achieve low network latency.Found in Hathora
- Shared endpoints for testing Provides instant shared endpoints for quick testing without managing servers.Found in Hathora
- Dedicated infrastructure Offers dedicated infrastructure for privacy, compliance, or VPC needs.Found in Hathora
- Fine-tuning on managed infrastructure Enables fine-tuning of models on managed infrastructure.Found in Hathora
- Model marketplace Provides a marketplace to discover and use models.Found in Hathora
- Flexible GPU billing Bills GPU costs either bundled with API usage or separately for dedicated endpoints.Found in Hathora
- Multi-format compatibility Works with multiple data formats and platforms.Found in Outspeed
- Automated error detection Automatically detects and corrects errors during data handling.Found in Outspeed
- Customizable workflows Allows customizing workflows to fit specific business needs.Found in Outspeed
- Real-time analytics and reporting Provides real-time analytics and reporting capabilities.Found in Outspeed
- User-friendly interface Offers a straightforward interface for both technical and non-technical users.Found in Outspeed
- Flexible integration Integrates flexibly with existing systems.Found in Outspeed
- Multilingual voice support Supports voice interactions in 100 languages and approximately 400 accents.Found in Eclatira
- Async processing Keeps multimodal input streams and API triggers from blocking each other.Found in Eclatira
- Privacy by default Does not store live streams by default; future storage would be opt-in.Found in Eclatira
How it works, step by step
- Process streaming data for timely insights
- Support direct voice-to-voice conversation without intermediate text transcription
- Process live camera or screen streams at 30 FPS
- Execute actions across custom APIs, MCP servers and third-party integrations
- Limit which actions autonomous agents may take
- Bring your own model or switch the underlying model
- Deploy custom Docker containers
- Deploy across multiple regions for low network latency
- Issue instant shared endpoints for testing without managing servers
- Offer dedicated infrastructure for privacy, compliance or VPC needs
- Fine-tune models on managed infrastructure
- Discover and use models from a marketplace
- Bill GPU costs bundled with API usage or separately for dedicated endpoints
- Work with multiple data formats and platforms
- Detect and correct errors during data handling
- Customize workflows to fit specific business needs
- Report real-time analytics and usage
- Provide a straightforward interface for technical and non-technical users
- Integrate with existing systems
- Support voice interaction in many languages and accents
- Keep multimodal input streams and API triggers from blocking each other
- Avoid storing live streams by default; make future storage opt-in
- Compare the reviewed result with the recorded baseline and value assumptions
- Capture corrections and named-owner approval before consequential use
- Export a versioned tested, region-aware agent deployment 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 Real-time multimodal agent deployment 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 Real-time multimodal agent deployment 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 Cloudflare27 KB
- prompt-vps.mdThe same build on your own server (Docker)27 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria14 KB
- demo/index.htmlThe working demo on sample data201 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
Run real-time voice, vision and data agents on one owned deployment surface instead of renting separate services. For product and platform teams deploying real-time voice, vision and data agents, convert approved models, containers, API tools and guardrail rules into a tested, region-aware agent deployment with shared endpoints and usage reporting. The benefit is a testable hypothesis, measured through accepted agent tasks per deployment hour and incidents after release; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect approved models, container images, API tool definitions, guardrail rules and region requirements, then follow this sequence: 1. Process streaming data for timely insights. 2. Support direct voice-to-voice conversation without intermediate text transcription. 3. Process live camera or screen streams at 30 FPS. 4. Execute actions across custom APIs, MCP servers and third-party integrations. 5. Limit which actions autonomous agents may take. Resolve uncertain cases with qualified reviewers, approve the tested, region-aware agent deployment, and measure accepted agent tasks per deployment hour and incidents after release 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. Final action authorization, guardrail policy and production release remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve source attribution, tool permissions, data residency and usage permissions. Named owners approve guardrail policies and production releases. One approved model family, one container image, one region and one guarded tool set; final action authorization and production release remain human. 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 family, one container image, one region and one guarded tool set; final action authorization and production release remain human. Implement one approved input format, a bounded representative case set and the first two task modules: process streaming data for timely insights; support direct voice-to-voice conversation without intermediate text transcription. Support the remaining modules with operator review: live vision processing, API and tool execution, guardrails and region deployment. 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
Customer-owned model registries, container registries, API and MCP tool endpoints, identity providers and observability systems. Cloud storage, deployment destinations and billing systems. 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: Agent and model setup, Live test console, Deployment and regions, Usage and guardrails. Use a project gallery, a central live test console with stream and transcript panels, and a right-hand panel for tools, guardrails and region settings. Let users compare model and container versions side by side. Display draft, testing, deployed and paused states. Provide a shared endpoint link with request logs anchored to the relevant agent run. Make the task-specific outcome tested, region-aware agent deployment visible beside its evidence, review state and value baseline.
