No-code AI app and agent delivery workspace

For

Operations and IT teams building internal AI apps and agents without a dedicated engineering team

Solves

Work is spread across several rented no-code agent tools, so workflows, data and approvals do not connect and the business does not own the result.

Delivers

Deployed, reviewed AI apps and agents

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

Replace several rented builder subscriptions with one owned workspace that turns plain-language requests into deployed, reviewed AI apps and agents.

1. Build apps and agents from plain-language descriptions.
2. Run goal-driven agents that plan steps and adapt to conditions.
3. Organize multiple agents into collaborating teams.
4. Edit workflows on a visual canvas.
5. Refine builds through a chat interface.
6. Use built-in tools such as web search, SMS, email and image generation.
7. Connect external apps so agents read and write data across systems.
8. Store and link work information in a centralized data layer.
9. Create role-specific interfaces without code.
10. Start from a template marketplace.
11. Route higher-stakes actions to human review and approval.
12. Track cost and run volume with usage controls.
13. Keep outputs and context in local-first storage with non-persistent cloud processing.
14. Reuse cards and templates across recurring workflows.
15. Test agents in a live preview with real data before publishing.
16. Access and edit underlying code for custom integrations.
17. Connect multiple LLM providers.
18. Share agents with teams or external users under access controls.
19. Suggest improvements and new automations from usage.
20. Organize unstructured data in a knowledge layer for retrieval.
21. Adapt behavior and recommendations per user role.
22. Parse and extract data from PDFs and Office files.
23. Run individual automation steps with real data samples.
24. Generate secure sharing links without exposing credentials.
25. Provide a context-aware copilot per app.
26. Manage role-based permissions centrally.
27. Deploy with built-in authentication, roles and access controls.
28. Customize branding and deployment, including email and chat interfaces.

Everything these tools do, in one app

• Natural-language app/agent builder Create apps or agents by describing what you want in plain language, without coding.Found in Blocks, Instruct, Relay.app Agents and 6 more
• Goal-driven autonomous agents Agents plan steps and adapt to conditions to achieve a goal rather than following a fixed checklist.Found in Instruct, Relay.app Agents, Albato AI and 1 more
• Multi-agent collaboration Organize multiple AI agents into teams that work together on tasks.Found in AgentX 2.0
• Visual canvas workflow editor See and edit workflows or agent logic on a visual canvas or flowchart.Found in Instruct, Nimo, Albato AI
• Chat-based builder interface Build and refine agents or apps through a conversational chat interface.Found in Instruct, Agentplace AI Agents
• Built-in agent tools Use built-in tools like web search, SMS, email, and image generation inside agents.Found in Blocks
• Wide app integrations Connect to many external apps and services so agents can read and write data across systems.Found in Blocks, Instruct, Relay.app Agents and 6 more
• Centralized data layer Store and link work information across apps and agents in one place.Found in Blocks
• Custom UI builder Create role-specific interfaces without code.Found in Blocks, Nimo, Vybe
• Template marketplace Start from prebuilt templates and examples to speed up common use cases.Found in Blocks, Agentplace AI Agents, Vybe
• Human-in-the-loop controls Let people review, approve, or refine agent actions for higher-stakes work.Found in Relay.app Agents
• Usage monitoring and controls Track and manage cost and run volume with monitoring and controls.Found in Relay.app Agents
• Local-first storage Keep user outputs and context stored locally, with cloud processing that doesn't persist data for training.Found in Nimo
• Reusable cards and templates Reuse cards and templates to speed up recurring workflows and preserve context.Found in Nimo
• Live preview and testing Test agents or workflows in a live preview with real data before publishing.Found in Agentplace AI Agents, Albato AI
• Developer code access Access and edit the underlying code for custom integrations and advanced control.Found in Agentplace AI Agents, Vybe
• Multiple LLM provider support Connect to multiple large language model providers for flexibility in performance and cost.Found in Agentplace AI Agents, AgentX 2.0
• Sharing and access controls Share agents or apps with teams or external users and restrict access as needed.Found in Agentplace AI Agents, Albato AI, CREAO and 1 more
• Self-learning suggestions Agents learn from usage and regularly suggest improvements or new automations.Found in LemonLime
• Knowledge layer for messy data Organize unstructured data for AI retrieval before passing it to agents.Found in LemonLime
• Per-user specialization Adapt recommendations and behavior to individual roles or users.Found in LemonLime
• Document parsing and extraction Parse and extract data from PDFs and Office files.Found in LemonLime
• Step-by-step testing Run individual automation steps with real data samples to validate and debug.Found in Albato AI
• Secure sharing links Generate secure links to share connections or automations without exposing credentials.Found in Albato AI
• Context-aware copilot per app Each app includes a copilot specialized for its specific workflow.Found in CREAO
• Role-based permissions Manage organizational control with role-based permissions and centralized management.Found in CREAO, Vybe
• Production-ready deployment Deploy apps with built-in authentication, roles, and access controls.Found in Vybe
• Branding and deployment customization Customize branding and deployment, including email and chat interfaces.Found in AgentX 2.0

What goes in, what comes out

The workflow

1. In
   Start with

   Natural-language requests, connected app data, documents and role rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect natural-language requests

4. 3

   Connected app data

5. 4

   Documents and role rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Deployed, reviewed AI apps and agents

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 integration set and one deployment environment; final access, data-handling and production decisions remain with the buyer's named owners. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Builder chat and canvas, Live preview and step test, Deployment and access. Use a project gallery, a large central canvas with a chat panel for plain-language edits, and a right-hand panel for tools, integrations, knowledge and permissions. Let users compare agent versions side by side. Display draft, in review, approved and deployed states. Provide a secure preview link with comments anchored to the relevant step. Make the task-specific outcome deployed, reviewed AI apps and agents visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, agent versions, connected credentials, approval states, usage allowances, run limits, deployment 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

Buyer-owned app accounts, authorized documents and permitted data sources. Cloud storage, identity providers and deployment 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 apps and agents from plain-language descriptions; run goal-driven agents that plan steps and adapt to conditions. 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 operations and IT teams building internal AI apps and agents without a dedicated engineering team use it to solve "work is spread across several rented no-code agent tools, so workflows, data and approvals do not connect and the business does not own the result"?
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: Deployed workflows in production use and reviewer-approved agent actions per delivery hour.
4. Measure, then decide. Track deployed workflows in production use and reviewer-approved agent actions per delivery hour; 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 integration set and one deployment environment; final access, data-handling and production decisions remain with the buyer's named owners. Implement one approved input format, a bounded representative case set and the first two task modules: build apps and agents from plain-language descriptions; run goal-driven agents that plan steps and adapt to conditions. Support the third module with operator review: organize multiple agents into collaborating teams. 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 deployed, reviewed AI apps and agents. Retain the explicit scope boundary: One approved integration set and one deployment environment; final access, data-handling and production decisions remain with the buyer's named owners.

What the build depends on. Asset upload and preview, asynchronous build jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist integration QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved integration set and one deployment environment; final access, data-handling and production decisions remain with the buyer's named owners.

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 app package. Offer a monthly production allowance after repeat demand. Quote complex multi-agent or custom-integration work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, reviewed AI apps and agents. 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

Replace several rented builder subscriptions with one owned workspace that turns plain-language requests into deployed, reviewed AI apps and agents. Demonstrate a concrete deployed, reviewed AI apps and agents using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations and IT teams building internal AI apps and agents without a dedicated engineering team professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample deployed, reviewed AI apps and agents from a small authorized input set, with a transparent calculation of deployed workflows in production use and reviewer-approved agent actions per delivery hour and no promised savings.

The first 30 days

1. Week 1: interview five operations and IT teams building internal AI apps and agents without a dedicated engineering team and inspect a recent example of work spread across several rented no-code agent tools, so workflows, data and approvals do not connect and the business does not own the result.
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 deployed workflows in production use and reviewer-approved agent actions per delivery hour, 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: Deployed workflows in production use and reviewer-approved agent actions per delivery hour. 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

Deployed workflows in production use and reviewer-approved agent actions per delivery hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs deployed, reviewed AI apps and agents. 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 agent patterns, integration mappings and review examples, together with reliable delivery for a narrow operations niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for operations and IT teams building internal AI apps and agents without a dedicated engineering team. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Blocks, Instruct, Relay.app Agents, Nimo, Agentplace AI Agents, LemonLime, Albato AI, CREAO, Vybe and AgentX 2.0 are what buyers use today. Compare this product with the buyer's present method on deployed workflows in production use and reviewer-approved agent actions per delivery hour. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, integration and storage usage, 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 deployed, reviewed AI apps and agents. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, data permissions and access boundaries. Named owners approve substantive changes and deployment scope. One approved integration set and one deployment environment; final access, data-handling and production decisions remain with the buyer's named owners. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.