Source-linked AI application build and operations console

For

Software teams building and running AI-powered applications and assistants

Solves

AI application work is split across separate prompt, retrieval, agent, diagnostics and observability tools, so teams cannot trace answers, permissions or decisions end to end.

Delivers

Reviewed, source-linked assistant and administrator console

Built in

about 4 weeks of creation time, MVP in 5 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

Reduce tool sprawl and traceability gaps while keeping the team's own workflow.

1. Combine prompts, functions and vector stores in one interface.
2. Generate ready-to-use APIs for AI capabilities.
3. Monitor AI performance and usage with built-in analytics.
4. Provide a developer interface for building AI applications.
5. Rebuild permission scope on every query.
6. Link every answer line to its source.
7. Capture decisions with context and flag contradictions.
8. Identify the right person for a topic.
9. Run agent workflows that require human approval and produce reports.
10. Operate inside Claude, Cursor and ChatGPT via MCP.
11. Guide computer troubleshooting through conversation.
12. Give step-by-step software and hardware instructions.
13. Run system diagnostics and suggest optimization tips.
14. Support multiple operating systems and common applications.
15. Adapt responses to user input and problem context in real time.
16. Suggest context-aware code completions across languages and frameworks.
17. Detect errors in real time during development.
18. Integrate with Visual Studio Code and JetBrains IDEs.
19. Suggest refactoring for readability and maintainability.
20. Allow customizable assistant behavior.
21. Offer a notebook-inspired prompt and model comparison environment.
22. Access text, image and audio models from multiple providers.
23. Connect models to PDFs, text files and HTML datasets.
24. Run templates on large datasets for batch execution and evaluation.
25. Share and clone workbooks with commenting.
26. Deploy locally on Windows, Linux or macOS.
27. Let users control models, data access and actions.
28. Build agents by combining functions through the API.
29. Offer a free evaluation tier without time limits.

Everything these tools do, in one app

• Unified AI platform Combines multiple AI components such as prompts, functions, and vector stores into a single interface.Found in Hyperaide
• Instant API generation Provides ready-to-use APIs for integrating AI capabilities into applications.Found in Hyperaide
• Built-in analytics Monitors AI performance and usage through observability tools.Found in Hyperaide
• Developer-friendly interface Offers an interface designed to accelerate AI application development.Found in Hyperaide
• Permission-aware retrieval Rebuilds permission scope on every query to only surface information the user can access.Found in Pulse
• Cited answers Links every line in an answer to its source for verification.Found in Pulse
• Decision tracking Captures decisions with context and detects contradictions with past decisions.Found in Pulse
• Expert finder Identifies the right person for a topic within the organization.Found in Pulse
• Agent workflows Runs agents that require human approval before executing and can produce reports.Found in Pulse
• MCP integration Works inside tools like Claude, Cursor, and ChatGPT via the Model Context Protocol.Found in Pulse
• Conversational troubleshooting Provides interactive guidance through natural language conversations for computer issues.Found in GPT Computer Assistant
• Step-by-step instructions Offers detailed steps for resolving software and hardware issues.Found in GPT Computer Assistant
• System diagnostics Performs system diagnostics and suggests optimization tips.Found in GPT Computer Assistant
• Multi-OS support Supports multiple operating systems and common applications.Found in GPT Computer Assistant
• Real-time adaptive responses Adapts responses based on user input and problem context in real time.Found in GPT Computer Assistant
• Context-aware code completion Provides code suggestions that adapt to different programming languages and frameworks.Found in OpenCopilot v2
• Real-time error detection Detects errors and offers suggestions to catch bugs early during development.Found in OpenCopilot v2
• IDE integration Integrates with multiple IDEs including Visual Studio Code and JetBrains products.Found in OpenCopilot v2
• Code refactoring suggestions Suggests refactoring to improve code readability and maintainability.Found in OpenCopilot v2
• Customizable settings Allows users to tailor the assistant's behavior to their workflow.Found in OpenCopilot v2
• Notebook-inspired interface Provides an interactive environment to iterate on prompts, compare models, and build templates.Found in LastMile AI
• Multi-modal model access Supports a range of generative AI models across text, image, and audio from various providers.Found in LastMile AI
• Data customization Connects large language models to specific datasets such as PDFs, text files, and HTML.Found in LastMile AI
• Batch execution Runs templates on large datasets to automate processes and evaluate app performance.Found in LastMile AI
• Collaboration and sharing Allows workbooks to be shared and cloned, with commenting features for team-based development.Found in LastMile AI
• Local deployment Runs on your own infrastructure on Windows, Linux, or macOS, from a single machine to production servers.Found in LM-Kit One
• Model and data control Users control which models run, what data those models can access, and what actions they can take.Found in LM-Kit One
• Agent building Combines multiple functions through the API to build AI agents.Found in LM-Kit One
• Free evaluation Offers a free tier or evaluation period without time limits for testing.Found in Hyperaide, GPT Computer Assistant, OpenCopilot v2 and 2 more

What goes in, what comes out

The workflow

1. In
   Start with

   Prompts, functions, vector stores, datasets, permission rules and model settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect prompts

4. 3

   Functions

5. 4

   Vector stores

6. 5

   Datasets

7. 6

   Permission rules and model settings

8. 7

   Then follow this sequence: 1

9. Out
   Finish with

   Reviewed, source-linked assistant and administrator console

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, permission checks, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final security, permission and production decisions remain with the development team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Build workspace, Source-linked assistant, Admin console. Use a project list, a central notebook-style canvas for prompts, functions and vector stores, and a right-hand panel for sources, permissions, model settings and comments. Let users compare model and prompt versions side by side. Display draft, in review and approved states. Provide a cited answer view with line-level source links and an approval queue for agent actions. Make the task-specific outcome reviewed, source-linked assistant and administrator console visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, team comments, approval states, usage allowances, model access, 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

Team-owned repositories, authorized datasets and permitted model providers. Cloud or local deployment, IDE plugins, MCP clients and analytics 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

   5 days

   One buyer segment, one recurring use case; first modules: combine prompts, functions and vector stores in one interface; generate ready-to-use APIs for AI capabilities. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   2 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 software teams building and running AI-powered applications and assistants use it to solve "AI application work is split across separate prompt, retrieval, agent, diagnostics and observability tools, so teams cannot trace answers, permissions or decisions end to end"?
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: Accepted AI features per developer hour and traced answers with correct permission scope.
4. Measure, then decide. Track accepted AI features per developer hour and traced answers with correct permission scope; 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, one deployment target and one IDE integration; final security, permission and production decisions remain with the development team. Implement one approved input format, a bounded representative case set and the first two task modules: combine prompts, functions and vector stores in one interface; generate ready-to-use APIs for AI capabilities. Support the third module with operator review: monitor AI performance and usage with built-in 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 the reviewed, source-linked assistant and administrator console. Retain the explicit scope boundary: One approved model set, one deployment target and one IDE integration; final security, permission and production decisions remain with the development team.

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 development QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved model set, one deployment target and one IDE integration; final security, permission and production decisions remain with the development team.

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 application package. Offer a monthly production allowance after repeat demand. Quote complex multi-tenant, on-premise or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked assistant and administrator console. 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 and traceability gaps while keeping the team's own workflow. Demonstrate a concrete reviewed, source-linked assistant and administrator console using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams building and running AI-powered applications and assistants professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed, source-linked assistant and administrator console from a small authorized input set, with a transparent calculation of accepted AI features per developer hour and traced answers with correct permission scope and no promised savings.

The first 30 days

1. Week 1: interview five software teams building and running AI-powered applications and assistants and inspect a recent example of AI application work split across separate prompt, retrieval, agent, diagnostics and observability 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 accepted AI features per developer hour and traced answers with correct permission scope, 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: Accepted AI features per developer hour and traced answers with correct permission scope. 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

Accepted AI features per developer hour and traced answers with correct permission scope; 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, source-linked assistant and administrator console. 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 prompts, permission rules, evaluation cases and review examples, together with reliable delivery for a narrow development niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams building and running AI-powered applications and assistants. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Hyperaide, Pulse, GPT Computer Assistant, OpenCopilot v2, LastMile AI and LM-Kit One. Compare this product with the buyer's present method on accepted AI features per developer hour and traced answers with correct permission scope. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, 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 the reviewed, source-linked assistant and administrator console. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, permission scope, decision context and usage permissions. Development teams approve substantive changes and deployment scope. One approved model set, one deployment target and one IDE integration; final security, permission and production decisions remain with the development team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.