
Source-linked support assistant and admin console
Reduce repeated handling while keeping every answer traceable to an approved source.
- For
- Support leads and operations managers handling multi-channel customer conversations and internal tasks
- Solves
- Conversations, tasks and knowledge sit in separate rented tools, so context is lost and routine work is repeated.
- Delivers
- Reviewed replies, prioritized tasks and confirmed actions
- 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
What it does
Reduce repeated handling while keeping every answer traceable to an approved source.
- Maintain coherent context across multi-turn conversations.
- Adjust response style and behavior per channel or brand.
- Connect messaging platforms and CRM systems.
- Translate conversations in real time.
- Track conversation metrics and engagement.
- Prioritize and organize tasks with AI.
- Connect productivity platforms to coordinate workflows.
- Send automated reminders and notifications.
- Suggest next actions from user behavior and preferences.
- Support team communication and project tracking.
- Customize chatbot design and behavior from a dashboard.
- Use the buyer's own model API key for message scaling.
- Build knowledge-domain chatbots from approved sources.
- Run an always-on agent with retained context across sessions.
- Adjust preferences and workflows from feedback over time.
- Execute low-risk recurring tasks and request confirmation on higher-risk items.
- Build no-code workflows for common use cases.
- Share community-validated workflows while keeping private data isolated by default.
Everything these tools do, in one app
- Contextual multi-turn conversations Maintains coherent context across multiple messages in a conversation.Found in GaliChat AI
- Customizable response settings Allows adjusting response style and behavior to fit different communication needs.Found in GaliChat AI
- Integration with messaging platforms Connects with popular messaging platforms and CRM systems for seamless communication.Found in GaliChat AI
- Real-time language translation Translates conversations in real time to support users across different languages.Found in GaliChat AI
- Analytics dashboard Tracks conversation metrics and user engagement to monitor performance.Found in GaliChat AI
- AI-powered task management Prioritizes and organizes tasks efficiently using AI.Found in Helpedby AI
- Integration with productivity platforms Connects with popular productivity platforms to coordinate workflows.Found in Helpedby AI
- Automated reminders and notifications Sends reminders and notifications to keep users on track.Found in Helpedby AI
- Personalized suggestions Provides suggestions based on user behavior and preferences.Found in Helpedby AI
- Collaborative tools Facilitates team communication and project tracking.Found in Helpedby AI
- Customizable chatbot design Allows full customization of chatbot design and behavior through a dashboard.Found in Ghostly Chat
- Bring your own OpenAI API key Integrates with users’ own OpenAI API keys for independent message scaling.Found in Ghostly Chat
- Knowledge-based chatbots Supports creating chatbots tailored to specific knowledge domains and use cases.Found in Ghostly Chat
- Persistent agent on dedicated VM Runs an always-on agent on a dedicated cloud VM with retained context across sessions.Found in MuleRun
- Self-evolving memory Learns from user behavior and feedback to adjust preferences and workflows over time.Found in MuleRun
- Proactive actions with confidence gating Automatically executes low-risk recurring tasks while asking for confirmation on higher-risk items.Found in MuleRun
- No-code workflows Enables building or choosing workflows for various use cases without programming.Found in MuleRun
- Opt-in knowledge network Allows sharing and discovering community-validated workflows while keeping private data isolated by default.Found in MuleRun
What goes in, what comes out
- Permitted conversation history
- Knowledge documents
- Task lists
- Platform events
AI drafts, people review. Source-linked assistant and administrator console.
- Reviewed replies
- Prioritized tasks
- Confirmed actions
How it works
The workflow
- InStart with
Permitted conversation history, knowledge documents, task lists and platform events
- 1
Confirm the buyer's problem and scope
- 2
Collect permitted conversation history
- 3
Knowledge documents
- 4
Task lists and platform events
- 5
Then follow this sequence: 1
- OutFinish with
Reviewed replies, prioritized tasks and confirmed actions
AI does the heavy lifting, people stay in charge
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate replies, task priorities and action proposals for the stated task modules. Use deterministic code for routing rules, schema validation, confidence thresholds and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Final customer commitments, refunds, account changes and policy exceptions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Assistant console, Conversation workspace, Admin and knowledge. Use a conversation list with channel and status, a central thread view with source citations, and a right-hand panel for tasks, suggestions and confidence state. Let reviewers compare draft and approved replies side by side. Display draft, changes requested and approved states. Provide an admin view for knowledge sources, workflow rules, confidence thresholds and audit history. Make the task-specific outcome reviewed replies, prioritized tasks and confirmed actions visible beside its evidence, review state and value baseline.
Accounts and administration
Organization ownership, channel connections, knowledge source versions, workflow rules, confidence thresholds, reviewer roles, usage allowances, retention limits, export logs and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls and explicit approval for external actions.
Integrations and data access
Buyer-owned conversation history, knowledge documents, task lists and permitted platform events. Messaging platforms, CRM systems, productivity platforms and the buyer's own model API key. Start with file exchange and validate destination specifications before promising direct platform actions. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
How we build it
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
Scoping call
Day 1Thirty 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
MVP
4 daysOne buyer segment, one recurring use case; first modules: maintain coherent context across multi-turn conversations; adjust response style and behavior per channel or brand. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
5 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
9 daysSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will support leads and operations managers handling multi-channel customer conversations and internal tasks use it to solve "conversations, tasks and knowledge sit in separate rented tools, so context is lost and routine work is repeated"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: First-contact resolution, reviewer correction time and confirmed actions per support hour.
- Measure, then decide. Track first-contact resolution and reviewer correction time and confirmed actions per support hour; 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 support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: maintain coherent context across multi-turn conversations; adjust response style and behavior per channel or brand. Support the remaining modules with operator review: connect messaging platforms and CRM systems; translate conversations in real time; track conversation metrics and engagement; prioritize and organize tasks with AI; connect productivity platforms to coordinate workflows; send automated reminders and notifications; suggest next actions from user behavior and preferences; support team communication and project tracking; customize chatbot design and behavior from a dashboard; use the buyer's own model API key for message scaling; build knowledge-domain chatbots from approved sources; run an always-on agent with retained context across sessions; adjust preferences and workflows from feedback over time; execute low-risk recurring tasks and request confirmation on higher-risk items; build no-code workflows for common use cases; share community-validated workflows while keeping private data isolated by default. 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 channels and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed replies, prioritized tasks and confirmed actions. Retain the explicit scope boundary: One support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human.
What the build depends on. Conversation upload and preview, asynchronous job processing, editable version history, reviewer access and tested export formats. High-fidelity support requires qualified human review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: maintain coherent context across multi-turn conversations; adjust response style and behavior per channel or brand. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$46,000about 4 weeks of creation time · start with the MVP from $13,500
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $60–$120 | $90–$180 |
| Full productabout 50 customers | $110–$210 | $530–$1,050 | $640–$1,260 |
Run it or resell it
For your own team
Support leads and operations managers handling multi-channel customer conversations and internal tasks run it inside the business: permitted conversation history, knowledge documents, task lists and platform events in, reviewed replies, prioritized tasks and confirmed actions out, reviewed by your people.
As part of your offer
Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.
Your brand, or this one
Run it under your own brand, or start from this concept style.
- primary
#917127 - accent
#547fc9 - surface
#f1ede4 - ink
#22201e
- Headings
- Libre Baskerville
- Text
- IBM Plex Sans
- Voice
- Warm, clear, calm under pressure
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 support workflow. Offer a monthly production allowance after repeat demand. Quote complex integrations or multi-brand deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed replies, prioritized tasks and confirmed actions 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
Reduce repeated handling while keeping every answer traceable to an approved source. Demonstrate a concrete reviewed replies, prioritized tasks and confirmed actions workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Support leads and operations managers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample of reviewed replies, prioritized tasks and confirmed actions from a small authorized input set, with a transparent calculation of first-contact resolution, reviewer correction time and confirmed actions per support hour and no promised savings.
The first 30 days
- Week 1: interview five support leads and operations managers handling multi-channel customer conversations and internal tasks and inspect a recent example of conversations, tasks and knowledge sitting in separate rented tools, so context is lost and routine work is repeated.
- Week 2: prepare a consented or synthetic demonstration of the task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure first-contact resolution, reviewer correction time and confirmed actions per support hour, 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: First-contact resolution, reviewer correction time and confirmed actions per support hour. 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
First-contact resolution, reviewer correction time and confirmed actions per support hour; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs reviewed replies, prioritized tasks and confirmed actions. 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 replies, workflow rules and review examples, together with reliable delivery for a narrow support niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for support leads and operations managers handling multi-channel customer conversations and internal tasks. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
GaliChat AI, Helpedby AI, Ghostly Chat and MuleRun are what buyers use today, each covering part of the job. Compare this product with the buyer's present method on first-contact resolution, reviewer correction time and confirmed actions per support hour. Offer one owned workflow with combined features, buyer-held data and the buyer's own brand instead of renting several subscriptions. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Model calls, translation processing, 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 replies, prioritized tasks and confirmed actions. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Preserve customer privacy, source attribution, consent records and usage permissions. Named reviewers approve substantive replies, account changes and external actions. One support channel, one knowledge domain and one task workflow; final customer commitments and policy exceptions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.