No-code AI app and agent delivery workspace

For

Founders, operations leads and internal product teams building AI-powered apps and agents without writing code

Solves

Building and launching AI apps and agents requires stitching together separate tools for agent logic, authentication, payments, deployment and infrastructure.

Delivers

A deployed, human-reviewed app or agent with authentication, payments and monitoring

Built in

about 6 weeks of creation time, MVP in 7 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 the number of separate tools and handoffs needed to launch an AI app or agent.

1. Create and configure AI agents without code.
2. Turn a plain-language description into a working agent.
3. Add built-in user authentication and account management.
4. Connect Stripe subscriptions and pay-per-use billing.
5. Deploy the finished app or agent to a live environment in one step.
6. Run agents on web, Slack, WhatsApp and APIs.
7. Choose among multiple large language models.
8. Add reusable modular skills that can be shared or monetized.
9. Connect CRM, scheduling and knowledge-base integrations.
10. Support memory, retrieval-augmented generation, fallbacks and multi-agent collaboration.
11. Give agents web search, code execution, vector databases, image/video generation and webhooks.
12. Let agents attempt error correction and apply fixes.
13. Insert human review gates for higher-risk actions.
14. Export code and sync with GitHub.
15. Generate and modify templates, design themes and web copy with AI assistance.
16. Offer a low-code interface with full code access.
17. Pair AI agents with hands-on expert support to scope and build the product.
18. Bundle compute, token usage and storage into the service.

Everything these tools do, in one app

• No-code agent building Lets users create and configure AI agents without writing code.Found in Tate-A-Tate, Shipable AI by CNTXT AI, Blink Agent Builder
• Prompt-to-agent generation Turns a user's description or prompt into a working AI agent automatically.Found in Shipable AI by CNTXT AI, Blink Agent Builder
• Integrated authentication Provides built-in user authentication so apps can manage user accounts.Found in Tate-A-Tate, Blink Agent Builder, AI SaaS Launcher
• Stripe payment processing Handles payments through Stripe, including subscriptions and pay-per-use models.Found in Tate-A-Tate, Shipable AI by CNTXT AI, AI SaaS Launcher
• One-click deployment Deploys the finished app or agent to a live environment quickly.Found in Tate-A-Tate, Shipable AI by CNTXT AI, Blink Agent Builder and 1 more
• Multi-platform deployment Runs agents on multiple channels such as web, Slack, WhatsApp, and APIs.Found in Shipable AI by CNTXT AI
• Multi-model LLM support Lets users choose from multiple large language models for AI responses.Found in Shipable AI by CNTXT AI, Blink Agent Builder
• Reusable modular skills Allows adding complex AI logic through reusable modules that can be shared or monetized.Found in Tate-A-Tate
• Built-in integrations Connects to common third-party platforms like CRM, scheduling, and knowledge bases.Found in Shipable AI by CNTXT AI
• Advanced AI workflows Supports memory, retrieval-augmented generation, fallback mechanisms, and multi-agent collaboration.Found in Shipable AI by CNTXT AI, Blink Agent Builder
• Agent tools Gives agents abilities like web search, code execution, vector databases, image/video generation, and webhooks.Found in Blink Agent Builder
• Self-correction and bug fixes Enables agents to attempt error correction and apply fixes automatically.Found in Blink Agent Builder
• Human-in-the-loop gates Adds human review steps for higher-risk actions in agent workflows.Found in Blink Agent Builder
• Code export and GitHub sync Provides code download and GitHub synchronization for technical users.Found in Blink Agent Builder, AI SaaS Launcher
• AI-powered customization Generates and modifies templates, design themes, and web copy with AI assistance.Found in AI SaaS Launcher
• Low-code with full code access Offers a low-code interface with the option to access and edit the full code.Found in AI SaaS Launcher
• Expert-guided development Pairs AI agents with hands-on expert support to scope and build the product.Found in Hal9
• All-inclusive infrastructure Bundles compute, token usage, and storage into the service.Found in Hal9

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language product description, brand assets, workflow rules and integration requirements

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect a plain-language product description

4. 3

   Brand assets

5. 4

   Workflow rules and integration requirements

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   A deployed, human-reviewed app or agent with authentication, payments and monitoring

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 deployment target and one payment provider; final security, compliance and production checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Build brief and references, Editable agent and app preview, Client proof and delivery. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for references, constraints and comments. Let users compare versions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant asset. Make the task-specific outcome a deployed, human-reviewed app or agent with authentication, payments and monitoring visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision 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

Author-owned product descriptions, authorized brand assets and permitted integration sources. Cloud asset storage, design-file import/export and deployment 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.

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

   7 days

   One buyer segment, one recurring use case; first modules: create and configure AI agents without code; turn a plain-language description into a working agent. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   8 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 founders, operations leads and internal product teams building AI-powered apps and agents without writing code use it to solve "building and launching AI apps and agents requires stitching together separate tools for agent logic, authentication, payments, deployment and infrastructure"?
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: Time from approved scope to working deployment and accepted agent tasks per delivery hour.
4. Measure, then decide. Track time from approved scope to working deployment and accepted agent tasks 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 deployment target and one payment provider; final security, compliance and production checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: create and configure AI agents without code; turn a plain-language description into a working agent. Support the third module with operator review: add built-in user authentication and account management. 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 deployed, human-reviewed app or agent with authentication, payments and monitoring. Retain the explicit scope boundary: One approved deployment target and one payment provider; final security, compliance and production checks 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 technical QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved deployment target and one payment provider; final security, compliance and production 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 app or agent package. Offer a monthly production allowance after repeat demand. Quote complex integrations, multi-tenant scale or specialist compliance separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, human-reviewed app or agent with authentication, payments and monitoring. 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 the number of separate tools and handoffs needed to launch an AI app or agent. Demonstrate a concrete deployed, human-reviewed app or agent with authentication, payments and monitoring using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Founders, operations leads and internal product teams building AI-powered apps and agents without writing code professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample deployed, human-reviewed app or agent with authentication, payments and monitoring from a small authorized input set, with a transparent calculation of time from approved scope to working deployment and accepted agent tasks per delivery hour and no promised savings.

The first 30 days

1. Week 1: interview five founders, operations leads and internal product teams building AI-powered apps and agents without writing code and inspect a recent example of building and launching AI apps and agents requires stitching together separate tools for agent logic, authentication, payments, deployment and infrastructure.
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 time from approved scope to working deployment and accepted agent tasks 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: Time from approved scope to working deployment and accepted agent tasks 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

Time from approved scope to working deployment and accepted agent tasks 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 a deployed, human-reviewed app or agent with authentication, payments and monitoring. 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 deployment patterns, integration configurations and review examples, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for founders, operations leads and internal product teams building AI-powered apps and agents without writing code. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Tate-A-Tate, Shipable AI by CNTXT AI, Blink Agent Builder, AI SaaS Launcher and Hal9. Compare this product with the buyer's present method on time from approved scope to working deployment and accepted agent tasks 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

Generation attempts, model token usage, compute, 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 deployed, human-reviewed app or agent with authentication, payments and monitoring. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, integration permissions and deployment scope. Named owners approve substantive changes and production releases. One approved deployment target and one payment provider; final security, compliance and production checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.