Source-linked customer and investor answer console

For

Support, community and investor-relations teams answering recurring questions from customers, users and investors

Solves

Incoming questions arrive around the clock across email, chat, messenger and social channels, and staff answer the same things repeatedly from scattered documents, CRM records and logs.

Delivers

Reviewed, source-linked answers and completed follow-up actions

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 repetitive answering effort while keeping every reply traceable to approved sources.

1. Answer incoming questions automatically around the clock.
2. Ground replies in the organization's own documents and records.
3. Reduce repetitive emails and repeated pitch-deck answers.
4. Support early-stage fundraising and investor questions.
5. Provide a simple console for managing the assistant.
6. Send customizable automated client messages.
7. Keep centralized client records with real-time updates.
8. Connect CRM and email platforms.
9. Show engagement and response analytics.
10. Automate follow-ups and reminders.
11. Let teams describe desired outcomes in plain language.
12. Trigger actions across connected systems, not only suggest replies.
13. Route high-stakes actions to human checkpoints and approvals.
14. Apply company, channel and specialist policy layers.
15. Use prebuilt connectors to knowledge bases and operational tools.
16. Handle financial and banking terminology accurately.
17. Answer in the supported languages.
18. Apply privacy and data-protection rules to stored and sent data.
19. Integrate with messengers, CRMs and social networks.
20. Ask clarifying questions and search knowledge bases before answering.
21. Run specialized agents per team with tailored data and instructions.
22. Analyze logs and telemetry to diagnose reported issues.
23. Integrate with documentation sites, Slack and helpdesk tools.
24. Generate validated custom code answers for technical questions.
25. Compare the reviewed result with the recorded baseline and value assumptions.
26. Capture corrections and named-owner approval before consequential use.
27. Export a versioned reviewed, source-linked answers and completed follow-up actions record with source references and unresolved questions.

Everything these tools do, in one app

• 24/7 automated answering Provides round-the-clock responses to incoming questions without human intervention.Found in Agent Sam, SermoAI
• Trained on organization data Uses the organization's own information to give personalized and relevant answers.Found in Agent Sam
• Reduces repetitive communications Cuts down on repetitive emails and pitch deck presentations by handling common inquiries.Found in Agent Sam
• Supports fundraising communications Helps startups manage early-stage fundraising interactions with investors.Found in Agent Sam
• Simple interface Offers an easy-to-use interface for managing the assistant.Found in Agent Sam
• Automated client messaging Sends customizable messages to clients automatically.Found in Qlient
• Centralized client data Stores and manages client information in one place with real-time updates.Found in Qlient
• CRM and email integration Connects with popular CRM and email platforms to streamline operations.Found in Qlient
• Engagement analytics dashboard Tracks engagement and response rates through a dashboard.Found in Qlient
• Follow-up task automation Automates follow-ups and reminders to ensure timely client interactions.Found in Qlient
• Natural-language agent setup Allows teams to describe desired outcomes in plain language to build automations without flowcharts or code.Found in Typewise AI Customer Service
• Actionable agents Enables agents to trigger operations across connected systems to complete tasks, not just suggest replies.Found in Typewise AI Customer Service
• Human handoffs and approvals Keeps humans in control for high-stakes actions through configurable checkpoints and approvals.Found in Typewise AI Customer Service
• Policy and safety controls Provides multi-level instruction layers for company-wide, channel-specific, and specialist guidance.Found in Typewise AI Customer Service
• Prebuilt connectors Links knowledge bases and operational tools through many prebuilt integrations.Found in Typewise AI Customer Service
• Financial terminology expertise Understands and responds accurately using financial and banking terminology.Found in SermoAI
• Multilingual support Communicates with users in over 60 languages.Found in SermoAI
• Privacy regulation compliance Complies with banking privacy and data protection regulations.Found in SermoAI
• Messenger and social integration Integrates flexibly with messengers, CRMs, and social networks.Found in SermoAI
• Agentic reasoning Actively understands user questions and performs actions such as requesting clarifications and searching knowledge bases.Found in RunLLM
• Multi-agent support Allows creation of specialized agents for different teams with tailored data and instructions.Found in RunLLM
• Log and telemetry analysis Automatically analyzes logs and telemetry to diagnose and resolve issues.Found in RunLLM
• Documentation and helpdesk integration Integrates with documentation sites, Slack, and Zendesk for seamless workflow incorporation.Found in RunLLM
• Custom code generation Generates validated custom code solutions to solve complex technical problems.Found in RunLLM

What goes in, what comes out

The workflow

1. In
   Start with

   Organization documents, client records, product logs and policy rules

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect organization documents

4. 3

   Client records

5. 4

   Product logs and policy rules

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked answers and completed follow-up actions

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers and actions for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Financial, legal and privacy-sensitive replies remain subject to named human approval. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Knowledge and policy setup, Answer review queue, Client and engagement console. Use a channel inbox for incoming questions, a large central answer canvas with cited sources, and a right-hand panel for client history, policy layers and approvals. Let reviewers compare draft and approved replies side by side. Display draft, changes requested, approved and sent states. Provide an administrator view for agents, connectors, languages and retention. Make the task-specific outcome reviewed, source-linked answers and completed follow-up actions visible beside its evidence, review state and value baseline.

Accounts and administration

Agent ownership, knowledge versions, channel settings, approval states, language coverage, connector permissions, retention rules, export logs and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps and explicit approval for external actions.

Integrations and data access

Organization knowledge bases, CRM and email platforms, messengers, social networks, documentation sites, Slack and helpdesk tools. Start with file exchange and validate destination specifications before promising direct sending. 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: answer incoming questions automatically around the clock; ground replies in the organization's own documents and records. 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

   10 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 support, community and investor-relations teams answering recurring questions from customers, users and investors use it to solve "incoming questions arrive around the clock across email, chat, messenger and social channels, and staff answer the same things repeatedly from scattered documents, CRM records and logs"?
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 answers per support hour and corrections after send.
4. Measure, then decide. Track accepted answers per support hour and corrections after send; 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 and one knowledge set; financial, legal and privacy-sensitive replies remain subject to named human approval. Implement one approved input format, a bounded representative question set and the first two task modules: answer incoming questions automatically around the clock; ground replies in the organization's own documents and records. Support the remaining modules with operator review. 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 connector. Expand supported channels, languages and question volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed, source-linked answers and completed follow-up actions. Retain the explicit scope boundary: One support channel and one knowledge set; financial, legal and privacy-sensitive replies remain subject to named human approval.

What the build depends on. Knowledge upload and indexing, asynchronous answer jobs, editable version history, reviewer access and tested export formats. Regulated replies require qualified human review. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One support channel and one knowledge set; financial, legal and privacy-sensitive replies remain subject to named human approval.

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 channel and knowledge set. Offer a monthly answering allowance after repeat demand. Quote complex integrations, extra languages or regulated review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked answers and completed follow-up 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 repetitive answering effort while keeping every reply traceable to approved sources. Demonstrate a concrete reviewed, source-linked answers and completed follow-up actions workflow using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Support, community and investor-relations 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 answers and completed follow-up actions record from a small authorized input set, with a transparent calculation of accepted answers per support hour and corrections after send and no promised savings.

The first 30 days

1. Week 1: interview five support, community and investor-relations teams and inspect a recent example of incoming questions arriving around the clock across email, chat, messenger and social channels.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure accepted answers per support hour and corrections after send, 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 answers per support hour and corrections after send. 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 answers per support hour and corrections after send; 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 answers and completed follow-up 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 answers, policy layers and reviewer corrections, together with reliable delivery for a narrow support and investor-relations niche. Build a permissioned library of representative question cases, reviewer corrections and verified operating constraints for support, community and investor-relations teams. Repeatable delivery and useful connectors matter more than access to a base model.

Alternatives and positioning

Agent Sam, Qlient, Typewise AI Customer Service, SermoAI and RunLLM, plus manual inbox handling. Compare this product with the buyer's present method on accepted answers per support hour and corrections after send. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, log and telemetry processing, storage, reviewer hours, client revision rounds and connector maintenance. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked answers and completed follow-up actions. Track cost per accepted answer, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive, financial, legal and privacy-sensitive replies and external actions. One support channel and one knowledge set; financial, legal and privacy-sensitive replies remain subject to named human approval. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.