Real-time multimodal agent deployment workspace

For

Product and platform teams deploying real-time voice, vision and data agents

Solves

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.

Delivers

Tested, region-aware agent deployment with shared endpoints and usage reporting

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Run real-time voice, vision and data agents on one owned deployment surface instead of renting separate services.

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.
6. Bring your own model or switch the underlying model.
7. Deploy custom Docker containers.
8. Deploy across multiple regions for low network latency.
9. Issue instant shared endpoints for testing without managing servers.
10. Offer dedicated infrastructure for privacy, compliance or VPC needs.
11. Fine-tune models on managed infrastructure.
12. Discover and use models from a marketplace.
13. Bill GPU costs bundled with API usage or separately for dedicated endpoints.
14. Work with multiple data formats and platforms.
15. Detect and correct errors during data handling.
16. Customize workflows to fit specific business needs.
17. Report real-time analytics and usage.
18. Provide a straightforward interface for technical and non-technical users.
19. Integrate with existing systems.
20. Support voice interaction in many languages and accents.
21. Keep multimodal input streams and API triggers from blocking each other.
22. Avoid storing live streams by default; make future storage opt-in.
23. Compare the reviewed result with the recorded baseline and value assumptions.
24. Capture corrections and named-owner approval before consequential use.
25. Export a versioned tested, region-aware agent deployment with source references and unresolved questions.

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

What goes in, what comes out

The workflow

1. In
   Start with

   Approved models, container images, API tool definitions, guardrail rules and region requirements

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved models

4. 3

   Container images

5. 4

   API tool definitions

6. 5

   Guardrail rules and region requirements

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   Tested, region-aware agent deployment with shared endpoints and usage reporting

AI does the heavy lifting, people stay in charge

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.

What your team sees

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.

Accounts and administration

Project ownership, model and container versions, endpoint access, guardrail policies, region settings, usage allowances, spend limits, request logs and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

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.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   6 days

   One buyer segment, one recurring use case; first modules: process streaming data for timely insights; support direct voice-to-voice conversation without intermediate text transcription. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   3 weeks

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product and platform teams deploying real-time voice, vision and data agents use it to solve "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"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Accepted agent tasks per deployment hour and incidents after release.
4. Measure, then decide. Track accepted agent tasks per deployment hour and incidents after release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. 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.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported models, containers, regions and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around the tested, region-aware agent deployment. Retain the explicit scope boundary: One approved model family, one container image, one region and one guarded tool set; final action authorization and production release remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model family, one container image, one region and one guarded tool set; final action authorization and production release remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 2,000-10,000 fixed pilot for one defined agent deployment. Offer a monthly platform allowance after repeat demand. Quote dedicated infrastructure, fine-tuning and specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded tested, region-aware agent deployment. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Run real-time voice, vision and data agents on one owned deployment surface instead of renting separate services. Demonstrate a concrete tested, region-aware agent deployment using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams deploying real-time voice, vision and data agents professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample tested, region-aware agent deployment from a small authorized input set, with a transparent calculation of accepted agent tasks per deployment hour and incidents after release and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams deploying real-time voice, vision and data agents and inspect a recent example of real-time voice, vision and data agents split across separate hosting, orchestration and tool-execution services.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted agent tasks per deployment hour and incidents after release, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Accepted agent tasks per deployment hour and incidents after release. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Accepted agent tasks per deployment hour and incidents after release; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a tested, region-aware agent deployment. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved guardrail policies, deployment configurations and review examples, together with reliable delivery for a narrow real-time agent niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and platform teams deploying real-time voice, vision and data agents. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Outspeed, Hathora and Eclatira, plus managed cloud AI platforms and self-hosted inference stacks. Compare this product with the buyer's present method on accepted agent tasks per deployment hour and incidents after release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

GPU and inference usage, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of the tested, region-aware agent deployment. Track cost per accepted output, including correction work, unsuccessful cases and support.

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.