Cross-assistant conversation transfer console

For

Developers and technical teams moving AI chat context between assistants

Solves

Switching between AI assistants loses conversation context and forces manual re-pasting.

Delivers

Reviewed context pack linked to source messages

Built in

about 4 weeks of creation time, MVP in 4 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 manual rework while keeping the conversation thread intact.

1. Detect the active chat thread in the source assistant.
2. Extract the full conversation with roles and timestamps.
3. Strip buttons, citations and widgets from the extracted text.
4. Compress the thread into a shorter context pack.
5. Preserve key facts, decisions and open questions during compression.
6. Let the user select full thread or latest messages only.
7. Export the pack as plain text or Markdown.
8. Download chat logs to the device.
9. Transfer the pack to another assistant with one click.
10. Auto-detect tab switch and offer to drop the pack into the new assistant.
11. Support multiple assistants including ChatGPT, Claude and Gemini.
12. Run as a browser extension on Chrome and Edge.
13. Store threads in local encrypted storage.
14. Encrypt provider keys locally with AES-256-GCM.
15. Run summarization with the user's own provider API key.
16. Operate without telemetry or analytics.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed context pack linked to source messages with source references and unresolved questions.

Everything these tools do, in one app

• Conversation transfer Moves an AI chat from one assistant to another while keeping its context.Found in ChatHop, ThreadPort, Prompt Bridge
• Context preservation Keeps the full conversation thread so the new assistant can continue without losing context.Found in ChatHop, ThreadPort, Prompt Bridge
• Multi-assistant support Works with several major AI assistants like ChatGPT, Claude, Gemini, and others.Found in ChatHop, ThreadPort, Prompt Bridge
• Browser extension Runs as an extension in the browser for quick access.Found in ChatHop, ThreadPort, Prompt Bridge
• Free tier Offers a limited number of free uses per month.Found in ChatHop, ThreadPort
• No account required Lets you use the tool without creating an account or logging in.Found in ThreadPort, Prompt Bridge
• Local processing Keeps all data in your browser without sending it to external servers.Found in ThreadPort, Prompt Bridge
• Plain text/Markdown export Copies the chat as plain text or Markdown for easy pasting.Found in ChatHop
• Selective copy Lets you choose to copy the entire conversation or only the latest messages.Found in ChatHop
• Download chat logs Saves chat logs to your device for offline storage or reference.Found in ChatHop
• One-click transfer Transfers the active chat to another AI service with a single click.Found in ThreadPort
• UI clutter removal Strips out buttons, citations, and widgets from the conversation before pasting.Found in ThreadPort
• Chrome and Edge support Works as a browser extension on both Chrome and Edge.Found in ThreadPort
• Thread compression Compresses the conversation into a shorter context pack to reduce token count.Found in Prompt Bridge
• Auto-detection on tab switch Automatically detects when you switch tabs and drops the context pack into the new AI's input box.Found in Prompt Bridge
• Local encrypted storage Stores threads in IndexedDB and encrypts provider keys locally with AES-256-GCM.Found in Prompt Bridge
• Bring-your-own-key summarization Runs compression using your own provider API key and talks directly to those services.Found in Prompt Bridge
• No telemetry or analytics Operates without sending any telemetry or analytics data.Found in Prompt Bridge

What goes in, what comes out

The workflow

1. In
   Start with

   Active chat thread, thread history and provider settings

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect an active chat thread

4. 3

   Its thread history and provider settings

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed context pack linked to source messages

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 fixed browser extension and supported assistant set; final context and meaning checks remain with the user. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Source-linked transfer console, Editable context pack preview, Admin and audit log. Use a thread list for conversations, a large central preview of the context pack, and a right-hand panel for provider selection, compression level and transfer settings. Let users compare original and compressed versions side by side. Display draft, transferred and failed states. Provide an admin view with transfer history, key status and retention controls. Make the task-specific outcome reviewed context pack linked to source messages visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, thread versions, transfer history, 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 chat threads, authorized provider APIs and permitted research sources. Cloud asset storage, browser extension APIs and assistant input 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

   4 days

   One buyer segment, one recurring use case; first modules: detect the active chat thread in the source assistant; extract the full conversation with roles and timestamps. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 days

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

4. 4

   Full product

   9 days

   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 developers and technical teams moving AI chat context between assistants use it to solve "switching between AI assistants loses conversation context and forces manual re-pasting"?
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: Successful transfers per hour and context retained after transfer.
4. Measure, then decide. Track successful transfers per hour and context retained after transfer; 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 fixed browser extension and supported assistant set; final context and meaning checks remain with the user. Implement one approved input format, a bounded representative case set and the first two task modules: detect the active chat thread in the source assistant; extract the full conversation with roles and timestamps. Support the third module with operator review: strip buttons, citations and widgets from the extracted text. 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 context pack linked to source messages. Retain the explicit scope boundary: One fixed browser extension and supported assistant set; final context and meaning checks remain with the user.

What the build depends on. Thread upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed browser extension and supported assistant set; final context and meaning checks remain with the user.

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 thread package. Offer a monthly production allowance after repeat demand. Quote complex multi-provider or enterprise integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed context pack linked to source messages. 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 manual rework while keeping the conversation thread intact. Demonstrate a concrete reviewed context pack linked to source messages using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Developers and technical teams moving AI chat context between 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 context pack linked to source messages from a small authorized input set, with a transparent calculation of successful transfers per hour and context retained after transfer and no promised savings.

The first 30 days

1. Week 1: interview five developers and technical teams moving AI chat context between assistants and inspect a recent example of switching between AI assistants loses conversation context and forces manual re-pasting.
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 successful transfers per hour and context retained after transfer, 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: Successful transfers per hour and context retained after transfer. 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

Successful transfers per hour and context retained after transfer; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed context pack linked to source messages. 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 transfer patterns, provider constraints 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 developers and technical teams moving AI chat context between assistants. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

ChatHop, ThreadPort and Prompt Bridge. Compare this product with the buyer's present method on successful transfers per hour and context retained after transfer. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, provider API 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 reviewed context pack linked to source messages. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve author voice, source attribution, quotation accuracy and usage permissions. Authors approve substantive changes and publication scope. One fixed browser extension and supported assistant set; final context and meaning checks remain with the user. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.