No-code multimodal agent delivery workspace

For

Support and operations teams that need AI agents handling conversations and tasks without coding

Solves

Teams rent several separate agent tools for building, voice, analytics and scheduling, and cannot combine them into one owned workflow.

Delivers

Reviewed, deployable agent configuration

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce tool sprawl while keeping one owned agent workflow.

1. Build agents in a visual no-code canvas.
2. Handle text, voice, image and video in one conversation.
3. Deploy voice agents with tested turn-taking.
4. Support multiple languages per agent.
5. Connect external tools and platforms.
6. Start from pre-built templates for common use cases.
7. Train agents on supplied data sources.
8. Show real-time conversation and performance analytics.
9. Run tasks such as CRM updates and appointment booking.
10. Hand off to a human agent on defined triggers.
11. Deploy across web, mobile, messaging apps and email.
12. Generate flows, interview questions and schedules from prompts.
13. Select a suitable model per use case.
14. Apply fallbacks and warm endings for edge cases.
15. Check agent quality against benchmarks before deployment.
16. Embed forms, calendars and product cards in conversations.
17. Capture lead details during conversations.
18. Create and monitor scheduled tasks.

Everything these tools do, in one app

• No-code agent builder Allows users to create AI agents through a visual interface without writing code.Found in NLX, YourGPT 2.0, Bolna Agent Studio and 1 more
• Multimodal interactions Enables agents to handle text, voice, images, and video within conversations.Found in NLX, YourGPT 2.0, Multimodal Agents by Sierra
• Voice agent support Provides capabilities to build and deploy voice-enabled AI agents.Found in NLX, YourGPT 2.0, Conversational AI by ElevenLabs and 2 more
• Multilingual support Enables agents to communicate in multiple languages.Found in NLX, Bolna Agent Studio, Kaily
• Third-party integrations Connects with external tools and platforms to extend agent functionality.Found in YourGPT 2.0, CronbotAI, Botsonic GPT Builder and 1 more
• Pre-built templates Offers ready-made templates to accelerate agent creation for common use cases.Found in Chikka, Kaily
• Training on custom data Allows agents to be trained on user-provided data sources for tailored responses.Found in YourGPT 2.0, Botsonic GPT Builder, Kaily
• Real-time analytics Provides live monitoring and insights into agent performance and conversations.Found in CronbotAI, Chikka
• Task automation Enables agents to perform actions like updating CRMs, booking appointments, and running workflows.Found in Kaily
• Human handoff Automatically transfers conversations to human agents when needed.Found in Kaily
• Omnichannel deployment Deploys agents across multiple channels such as web, mobile, messaging apps, and email.Found in Kaily, Botsonic GPT Builder
• AI-assisted content generation Uses AI to generate agent flows, interview questions, or cron expressions from prompts.Found in YourGPT 2.0, CronbotAI, Chikka
• Automatic model selection Automatically chooses the best AI model for the use case.Found in Bolna Agent Studio, Botsonic GPT Builder
• Edge-case handling Includes fallbacks and warm endings to manage non-linear conversations.Found in Bolna Agent Studio
• Pre-deployment quality review Checks agent quality against benchmarks before deployment.Found in Bolna Agent Studio
• Interactive components Embeds custom UI elements like forms, calendars, and product cards into conversations.Found in Multimodal Agents by Sierra
• Lead generation Captures customer information during conversations for sales follow-up.Found in Botsonic GPT Builder
• Scheduling automation Automates the creation and monitoring of scheduled tasks like cron jobs.Found in CronbotAI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved knowledge sources, channel requirements, escalation rules and task definitions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved knowledge sources

4. 3

   Channel requirements

5. 4

   Escalation rules and task definitions

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, deployable agent configuration

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. One approved knowledge set and one deployment channel; final escalation and compliance checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Agent builder canvas, Knowledge and training sources, Test and quality review, Channel deployment, Live analytics and handoff. Use a thumbnail gallery for agents, a large central flow canvas, and a right-hand panel for sources, constraints and comments. Let users compare agent versions side by side. Display draft, in review, approved and live states. Provide a client preview link with comments anchored to the relevant conversation step. Make the task-specific outcome reviewed, deployable agent configuration visible beside its evidence, review state and value baseline.

Accounts and administration

Agent ownership, source versions, conversation logs, approval states, usage allowances, channel limits, download history 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 knowledge bases, authorized conversation logs and permitted research sources. Cloud storage, CRM and helpdesk import/export and messaging destinations. Start with file exchange and validate destination specifications before promising direct deployment. 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: build agents in a visual no-code canvas; handle text, voice, image and video in one conversation. 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 support and operations teams that need AI agents handling conversations and tasks without coding use it to solve "teams rent several separate agent tools for building, voice, analytics and scheduling, and cannot combine them into one owned workflow"?
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: Resolved conversations per support hour and handoffs after deployment.
4. Measure, then decide. Track resolved conversations per support hour and handoffs after deployment; 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 knowledge set and one deployment channel; final escalation and compliance checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: build agents in a visual no-code canvas; handle text, voice, image and video in one conversation. Support the third module with operator review: deploy voice agents with tested turn-taking. 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 inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around a reviewed, deployable agent configuration. Retain the explicit scope boundary: One approved knowledge set and one deployment channel; final escalation and compliance checks remain human.

What the build depends on. Agent upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist support QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved knowledge set and one deployment channel; final escalation and compliance checks 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 300-1,500 fixed pilot for one defined agent package. Offer a monthly production allowance after repeat demand. Quote complex voice, video or specialist integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, deployable agent configuration. 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

Reduce tool sprawl while keeping one owned agent workflow. Demonstrate a concrete reviewed, deployable agent configuration using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support and operations teams that need AI agents handling conversations and tasks without coding professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, deployable agent configuration from a small authorized input set, with a transparent calculation of resolved conversations per support hour and handoffs after deployment and no promised savings.

The first 30 days

1. Week 1: interview five support and operations teams that need AI agents handling conversations and tasks without coding and inspect a recent example of teams renting several separate agent tools for building, voice, analytics and scheduling, and cannot combine them into one owned workflow.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure resolved conversations per support hour and handoffs after deployment, 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: Resolved conversations per support hour and handoffs after deployment. 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

Resolved conversations per support hour and handoffs after deployment; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a reviewed, deployable agent configuration. 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 flows, escalation rules and review examples, together with reliable delivery for a narrow support niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for support and operations teams that need AI agents handling conversations and tasks without coding. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

NLX, YourGPT 2.0, CronbotAI, Conversational AI by ElevenLabs, Bolna Agent Studio, Multimodal Agents by Sierra, AlTable.ai - No-code Al Agents, Chikka, Botsonic GPT Builder and Kaily. Compare this product with the buyer's present method on resolved conversations per support hour and handoffs after deployment. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, voice or video processing, 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 a reviewed, deployable agent configuration. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve customer voice, source attribution, quotation accuracy and usage permissions. Support leads approve substantive changes and deployment scope. One approved knowledge set and one deployment channel; final escalation and compliance checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.