No-code AI application delivery workspace

For

Operations, product and IT teams building internal or client-facing AI apps without a development team

Solves

AI app projects stall between prototype tools, scattered data sources and deployment, so teams rent several subscriptions and still cannot ship a governed app.

Delivers

Deployed, monitored AI app with source references and unresolved questions

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 distance from a described workflow to a deployed, monitored AI app.

1. Assemble AI apps from drag-and-drop components without code.
2. Start from ready-made and open-source app templates.
3. Connect apps to selected AI models and any large language model backend.
4. Connect SQL databases, REST APIs and CSV files as managed data sources.
5. Integrate external systems and third-party APIs.
6. Automate manual process steps in the app workflow.
7. Build custom models from supplied data through a no-code interface.
8. Deploy models and apps into business processes.
9. Monitor model and app performance and flag drift.
10. Add authentication and payment processing.
11. Apply security, access and governance controls.
12. Deploy with one click or run locally.
13. Capture corrections and named-owner approval before consequential use.
14. Export a versioned deployed, monitored AI app with source references and unresolved questions.

Everything these tools do, in one app

• No-code app creation Build AI-powered applications without writing any code.Found in Brancher.ai, Diaflow, Obviously AI
• Ready-made templates Start projects quickly with pre-built app templates.Found in Brancher.ai, AdventAI
• AI model integration Connect your app to various AI models to leverage AI capabilities.Found in Brancher.ai, AdventAI
• Drag-and-drop interface Visually assemble app components by dragging and dropping elements.Found in Brancher.ai
• API integration Connect to external APIs to extend app functionality.Found in Brancher.ai, Diaflow
• Workflow automation Automate manual processes to speed up deployment and improve efficiency.Found in Diaflow
• Data management Connect and manage various data sources like SQL databases, REST APIs, and CSV files.Found in Diaflow, Obviously AI
• External system integration Integrate with external systems to build comprehensive internal apps.Found in Diaflow, Obviously AI
• Model building Create custom AI models directly from your data using a no-code interface.Found in Obviously AI
• Model deployment Quickly deploy AI models into business processes.Found in Obviously AI
• Monitoring and optimization Track model performance and ensure continuous improvement.Found in Obviously AI
• Security and governance Ensure robust security measures and compliance certifications.Found in Obviously AI
• Open-source templates Fork and modify freely available AI app templates.Found in AdventAI
• LLM compatibility Use any large language model as the AI backend.Found in AdventAI
• One-click deployment Deploy apps easily with a single click or run locally.Found in AdventAI
• Authentication and payment integration Add authentication and payment processing to your apps.Found in AdventAI
• Community support Get help and collaborate through community channels like Discord and GitHub Discussions.Found in AdventAI
• Monetization opportunities Monetize and share your app creations to generate revenue.Found in Brancher.ai, AdventAI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved process descriptions, data sources and model choices

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved process descriptions

4. 3

   Data sources and model choices

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Deployed, monitored AI app with source references and unresolved questions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three 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 hosting environment and one supported data-source set; final security review and process sign-off remain with the buyer. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: App builder canvas, Data and model connections, Deployment and monitoring. Use a project gallery, a central drag-and-drop assembly canvas, and a right-hand panel for components, data sources and permissions. Let users compare template versions side by side. Display draft, in review and deployed states. Provide a client or internal preview link with comments anchored to the relevant component. Make the task-specific outcome a deployed, monitored AI app visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, component versions, data-source credentials, approval states, usage allowances, deployment limits, access 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 data sources, authorized process documents and permitted model providers. Cloud hosting, identity providers, payment processors 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

   7 days

   One buyer segment, one recurring use case; first modules: assemble AI apps from drag-and-drop components without code; start from ready-made and open-source app templates. 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, product and IT teams building internal or client-facing AI apps without a development team use it to solve "AI app projects stall between prototype tools, scattered data sources and deployment, so teams rent several subscriptions and still cannot ship a governed app"?
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 deployed per delivery month and manual steps removed per process.
4. Measure, then decide. Track working apps deployed per delivery month and manual steps removed per process; 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 hosting environment and one supported data-source set; final security review and process sign-off remain with the buyer. Implement one approved input format, a bounded representative case set and the first two task modules: assemble AI apps from drag-and-drop components without code; start from ready-made and open-source app templates. Support the third module with operator review: connect apps to selected AI models and any large language model backend. 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, monitored AI app. Retain the explicit scope boundary: One approved hosting environment and one supported data-source set; final security review and process sign-off remain with the buyer.

What the build depends on. Component upload and preview, asynchronous build jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved hosting environment and one supported data-source set; final security review and process sign-off remain with the buyer.

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 or specialist security work separately. These are test prices, not market benchmarks. Package the initial sale as one bounded deployed, monitored AI app. 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 distance from a described workflow to a deployed, monitored AI app. Demonstrate a concrete deployed, monitored AI app using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Operations, product and IT teams building internal or client-facing AI apps without a development 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, monitored AI app from a small authorized input set, with a transparent calculation of working apps deployed per delivery month and manual steps removed per process and no promised savings.

The first 30 days

1. Week 1: interview five operations, product and IT teams building internal or client-facing AI apps without a development team and inspect a recent example of AI app projects stall between prototype tools, scattered data sources and deployment.
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 deployed per delivery month and manual steps removed per process, 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 deployed per delivery month and manual steps removed per process. 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 deployed per delivery month and manual steps removed per process; 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, monitored AI app. 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 components, data connectors 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, product and IT teams building internal or client-facing AI apps without a development team. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Brancher.ai, Diaflow, Obviously AI and AdventAI, plus freelance developers and internal build teams. Compare this product with the buyer's present method on working apps deployed per delivery month and manual steps removed per process. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model and API calls, hosting, storage, reviewer hours, client revision rounds and licensed source components. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a deployed, monitored AI app. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, access boundaries and usage permissions. Buyers approve substantive changes and deployment scope. One approved hosting environment and one supported data-source set; final security review and process sign-off remain with the buyer. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.