No-code AI app delivery workspace

For

Operations leads and product owners in small firms who need internal or client-facing AI apps but have no development team

Solves

AI app features are spread across many rented tools, so teams rebuild the same workflow, cannot own their data, and depend on several subscriptions.

Delivers

A deployed, reviewed app the client controls

Built in

about 6 weeks of creation time, MVP in 7 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 AI app builders with one owned workspace that covers building, data, users, monetization and deployment.

1. Build functional apps from plain-language requirements.
2. Generate app structure from natural language prompts.
3. Provide a chat interface for building and editing.
4. Offer pre-built templates for common app types.
5. Configure custom workflows and app logic.
6. Connect multiple large language models.
7. Store and manage app data in a built-in database.
8. Handle user registration and authentication.
9. Set up subscriptions and payment collection.
10. Pull in real-time data sources.
11. Accept and return text, image and file inputs.
12. Generate images for use inside apps.
13. Publish and deploy apps to a hosted address.
14. Embed built tools into existing websites.
15. Share and reuse components in a permissioned workspace.
16. Query datasets in natural language.
17. Apply access controls and data protection settings.
18. Attach hosting, domain and email configuration.
19. Create avatar videos for app content.
20. Run safety checks on prompts and outputs.
21. Report user interaction analytics.

Everything these tools do, in one app

• No-code app building Create functional apps without writing any code.Found in Imagica, Licode, Build AI and 7 more
• Natural language prompts Build apps by describing requirements in plain language.Found in Build AI, Vitara AI, Pixoai and 1 more
• Chat interface Interact with the platform through a chat-based interface.Found in Imagica, Hostinger Horizons
• Pre-built templates Use ready-made templates to accelerate app development.Found in Imagica, Build AI, Pickaxe and 1 more
• Custom workflows Set up automated processes and tailor app logic.Found in Licode, Metatext
• LLM integrations Connect with various large language models for AI capabilities.Found in Licode, Riku.ai
• Built-in database Store and manage data within the platform.Found in Licode
• User management Handle user authentication and registration.Found in Licode, Build AI
• Monetization support Charge users and generate revenue from apps.Found in Imagica, Licode, Build AI and 1 more
• Real-time data Incorporate dynamic, up-to-date data into apps.Found in Imagica
• Multimodal input/output Handle multiple data types like text and images.Found in Imagica
• Image generation Generate images for use in apps.Found in Imagica, Argil
• Publishing and deployment Publish or deploy apps with ease.Found in Imagica, Vitara AI, Hostinger Horizons
• App embedding Embed created tools into existing websites.Found in Pickaxe
• Community collaboration Discover, share, and collaborate on AI tools.Found in Pickaxe
• Data retrieval Query and retrieve information from datasets using natural language.Found in Pixoai
• Security compliance Ensure data protection with GDPR compliance and access controls.Found in Pixoai
• Integrated hosting Hosting, domain, and email services included.Found in Hostinger Horizons
• Video creation Create videos with customizable avatars.Found in Argil
• AI safety testing Identify vulnerabilities and protect against threats.Found in Metatext
• User analytics Gain insights into user interactions.Found in Riku.ai

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language requirements, sample data, brand assets and target user roles

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect plain-language requirements

4. 3

   Sample data and brand assets

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A deployed, reviewed app the client controls

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured app structures and generate candidate screens and workflows 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 model set and one hosting target; final security, legal and payment checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Requirements chat and template picker, App builder canvas, Data and model settings, User and billing setup, Review and deployment. Use a project list with status, a central builder showing app screens and workflow steps, and a right-hand panel for prompts, data sources and review notes. Let users compare template and custom versions side by side. Display draft, in review and deployed states. Provide a client preview link with comments anchored to the relevant screen. Make the task-specific outcome a deployed, reviewed app the client controls visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, app versions, user roles, model keys, data retention rules, payment settings, usage allowances, 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

Client-owned data sources, authorized model providers, payment processors and hosting targets. Cloud storage, design-file import/export and publishing 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: build functional apps from plain-language requirements; generate app structure from natural language prompts; configure custom workflows and app logic. 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 operations leads and product owners in small firms who need internal or client-facing AI apps but have no development team use it to solve "AI app features are spread across many rented tools, so teams rebuild the same workflow, cannot own their data, and depend on several subscriptions"?
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: Working apps delivered per month and manual steps removed per workflow.
4. Measure, then decide. Track working apps delivered per month and manual steps removed per workflow; 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 set and one hosting target; final security, legal and payment checks remain human. Implement one approved input format, a bounded representative case set and the first three task modules: build functional apps from plain-language requirements; generate app structure from natural language prompts; configure custom workflows and app logic. Support later modules with operator review: connect models, database and user accounts; set up payments, hosting and embedding; run safety checks and review analytics. 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, reviewed app the client controls. Retain the explicit scope boundary: One approved model set and one hosting target; final security, legal and payment checks remain human.

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 security and payment QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model set and one hosting target; final security, legal and payment 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 package. Offer a monthly production allowance after repeat demand. Quote complex integrations, video or specialist compliance separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, reviewed app the client controls. 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 AI app builders with one owned workspace that covers building, data, users, monetization and deployment. Demonstrate a concrete deployed, reviewed app the client controls using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations leads and product owners in small firms 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 app the client controls from a small authorized input set, with a transparent calculation of working apps delivered per month and manual steps removed per workflow and no promised savings.

The first 30 days

1. Week 1: interview five operations leads and product owners in small firms who need internal or client-facing AI apps but have no development team and inspect a recent example of AI app features spread across many rented tools.
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 working apps delivered per month and manual steps removed per workflow, 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: Working apps delivered per month and manual steps removed per workflow. 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

Working apps delivered per month and manual steps removed per workflow; 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, reviewed app the client controls. 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 app patterns, workflow templates and review examples, together with reliable delivery for a narrow operational niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for operations leads and product owners in small firms who need internal or client-facing AI apps but have no development team. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Imagica, Licode, Build AI, Vitara AI, Pickaxe, Pixoai, Hostinger Horizons, Argil, Metatext and Riku.ai, plus freelancers and generic development agencies. Compare this product with the buyer's present method on working apps delivered per month and manual steps removed per workflow. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, image and 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 deployed, reviewed app the client controls. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve client data ownership, source attribution, access permissions and payment compliance. Clients approve substantive app changes and deployment scope. One approved model set and one hosting target; final security, legal and payment checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.