Source-linked text and code reasoning workbench

For

Software teams and technical writers who need one owned assistant for writing, coding and reasoning work

Solves

Teams rent several AI subscriptions, spread prompts and context across tools, and cannot trace which source or model produced an output.

Delivers

Reviewed, source-linked drafts, code changes and reasoned answers

Built in

about 5 weeks of creation time, MVP in 5 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

Consolidate writing, coding and reasoning assistance into one owned console with source-linked outputs and administrator controls.

1. Generate coherent text for writing tasks.
2. Understand and answer in multiple languages.
3. Assist with writing, understanding and debugging code.
4. Solve logical and mathematical problems step by step.
5. Process images, charts and documents alongside text.
6. Handle long documents and conversations in one context.
7. Reduce unsupported claims with source-linked answers.
8. Apply custom system prompts and behavior rules.
9. Call approved external tools and APIs.
10. Run extended step-by-step reasoning on request.
11. Keep memory across longer tasks.
12. Follow user instructions precisely.
13. Integrate with VS Code, JetBrains and GitHub.
14. Run multi-step agentic tasks under review.
15. Provide a command-line interface for terminal work.
16. Offer selectable model sizes for cost and performance.
17. Automate routine drafting and check tasks.
18. Configure workflows per team or client.

Everything these tools do, in one app

• Text generation Generates coherent and contextually relevant text for various writing tasks.Found in Claude 3, Claude 2.1, Claude 4 and 7 more
• Multilingual support Understands and communicates in multiple languages.Found in Claude 3, Qwen3, Microsoft Phi-4 and 1 more
• Coding assistance Helps with writing, understanding, and debugging code.Found in Claude 3, Claude 4, Claude Code and Claude 3.7 Sonnet and 2 more
• Reasoning capabilities Solves complex problems through logical and mathematical reasoning.Found in Claude 3, Claude 4, Claude Code and Claude 3.7 Sonnet and 2 more
• Multimodal input Processes and interprets images, charts, and documents alongside text.Found in Claude 3
• Large context window Handles very long documents or conversations in a single interaction.Found in Claude 2.1
• Reduced hallucinations Generates more accurate and trustworthy content with fewer errors.Found in Claude 2.1
• System prompts Allows users to guide the AI's behavior and responses with custom instructions.Found in Claude 2.1
• Tool use Integrates with external tools and APIs to extend functionality.Found in Claude 2.1, Claude 4
• Extended thinking Engages in deeper, step-by-step reasoning for complex problems.Found in Claude 4, Claude Code and Claude 3.7 Sonnet, Qwen3
• Memory Maintains context over longer conversations and tasks.Found in Claude 4, Claude for iOS
• Instruction following Follows user instructions precisely to produce relevant outputs.Found in Claude 4
• Development tool integration Integrates with popular development environments like VS Code, JetBrains, and GitHub.Found in Claude 4
• Agentic capabilities Performs autonomous task management and multi-step actions.Found in Claude Code and Claude 3.7 Sonnet, GPT-5
• Command-line interface Enables AI-driven tasks directly from the terminal.Found in Claude Code and Claude 3.7 Sonnet
• Model size options Offers a range of model sizes to suit different resource and performance needs.Found in Claude 3, Claude 4, Qwen3 and 1 more
• Task automation Automates routine tasks to reduce manual workload.Found in Eternity AI
• Customizable workflows Allows users to tailor workflows to specific business needs.Found in Eternity AI

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, code repositories, images and conversation history

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Code repositories

5. 4

   Images and conversation history

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked drafts, code changes and reasoned answers

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 model set and one repository scope; final code merges and published text remain human decisions. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Workspace and source intake, Assistant console, Administrator console. Use a project list, a large central conversation and editing canvas, and a right-hand panel for sources, tools, memory and review state. Let users compare model sizes and extended-thinking runs side by side. Display draft, changes requested and approved states. Provide a source-linked preview with citations anchored to the relevant passage or file. Make the task-specific outcome reviewed, source-linked drafts, code changes and reasoned answers visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, prompt and system-prompt versions, model selection, tool permissions, memory scope, usage allowances, revision limits, export 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 documents, code repositories, images and permitted research sources. Cloud storage, VS Code, JetBrains, GitHub and terminal environments. Start with file exchange and validate destination specifications before promising direct publishing or 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

   5 days

   One buyer segment, one recurring use case; first modules: generate coherent text for writing tasks; understand and answer in multiple languages. 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 and technical writers who need one owned assistant for writing, coding and reasoning work use it to solve "teams rent several AI subscriptions, spread prompts and context across tools, and cannot trace which source or model produced an output"?
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 outputs per reviewer hour and corrections after approval.
4. Measure, then decide. Track accepted outputs per reviewer hour and corrections after approval; 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 repository scope; final code merges and published text remain human decisions. Implement one approved input format, a bounded representative case set and the first two task modules: generate coherent text for writing tasks; understand and answer in multiple languages. Support the third module with operator review: assist with writing, understanding and debugging code. 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 reviewed, source-linked drafts, code changes and reasoned answers. Retain the explicit scope boundary: One approved model set and one repository scope; final code merges and published text remain human decisions.

What the build depends on. Source 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 model set and one repository scope; final code merges and published text remain human decisions.

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 team workspace. Offer a monthly production allowance after repeat demand. Quote complex integrations or specialist review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked drafts, code changes and reasoned answers workflow. 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

Consolidate writing, coding and reasoning assistance into one owned console with source-linked outputs and administrator controls. Demonstrate a concrete reviewed, source-linked drafts, code changes and reasoned answers workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Software teams and technical writers 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 drafts, code changes and reasoned answers from a small authorized input set, with a transparent calculation of accepted outputs per reviewer hour and corrections after approval and no promised savings.

The first 30 days

1. Week 1: interview five software teams and technical writers who need one owned assistant for writing, coding and reasoning work and inspect a recent example of rented AI subscriptions, spread prompts and context across tools, and cannot trace which source or model produced an output.
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 outputs per reviewer hour and corrections after approval, 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 outputs per reviewer hour and corrections after approval. 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 outputs per reviewer hour and corrections after approval; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed, source-linked drafts, code changes and reasoned answers. 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, source-linked review examples and team workflow configurations, together with reliable delivery for a narrow technical niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for software teams and technical writers who need one owned assistant for writing, coding and reasoning work. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Claude 3, Claude 2.1, Claude 4, Claude for iOS, Claude Code and Claude 3.7 Sonnet, Qwen3, GPT-5, Microsoft Phi-4, Grok-1 and Eternity AI. Compare this product with the buyer's present method on accepted outputs per reviewer hour and corrections after approval. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model inference, long-context processing, image and document parsing, storage, reviewer hours, client revision rounds and licensed source material. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked drafts, code changes and reasoned answers. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, code provenance, quotation accuracy and usage permissions. Named reviewers approve substantive changes and publication or merge scope. One approved model set and one repository scope; final code merges and published text remain human decisions. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.