Support resolution knowledge distiller
Turn verified fixes into reusable operational knowledge.
- For
- Technical support knowledge teams
- Solves
- Reliable fixes remain trapped in long case threads.
- Delivers
- Validated resolution recipe library
- Built in
- about 5 weeks of creation time, MVP in 6 days
- Investment
- $28,500 for the MVP, $50,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
Turn verified fixes into reusable operational knowledge.
- Extract proposed fix sequences.
- Replay them in a sandbox.
- Publish only reviewed reproducible recipes.
- Compare the reviewed result with the recorded baseline and value assumptions.
- Capture corrections and named-owner approval before consequential use.
- Export a versioned validated resolution recipe library with source references and unresolved questions.
What goes in, what comes out
- Approved solved cases
- Runnable test fixtures
AI drafts, people review. Technical delivery workspace with managed implementation.
- Validated resolution recipe library
How it works
The workflow
- InStart with
Approved solved cases and runnable test fixtures
- 1
Confirm the buyer's problem and scope
- 2
Collect approved solved cases and runnable test fixtures
- 3
Then follow this sequence: 1
- OutFinish with
Validated resolution recipe library
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 technical stack; engineers approve recipes. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Authorized input and test setup, Proposed implementation, Test results and release review. Show a work backlog, proposed changes and verification results. Link each item to its source configuration, code or data mapping. Provide execution logs and an owner-facing health view. Keep environments and approval states clearly separated so a draft cannot be mistaken for a live change. Make the task-specific outcome validated resolution recipe library visible beside its evidence, review state and value baseline.
Accounts and administration
Project access, environment separation, versioned changes, test evidence, owner approvals, execution logs, rollback instructions and incident handling. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.
Integrations and data access
Support inboxes, help centers, order records and customer feedback systems. Approved repositories, application APIs, execution platforms and monitoring systems. Validate current API access and behavior during discovery before promising compatibility. 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
6 daysOne buyer segment, one recurring use case; first modules: extract proposed fix sequences; replay them in a sandbox. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
7 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
3 weeksSelf-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 technical support knowledge teams use it to solve "reliable fixes remain trapped in long case threads"?
- 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: Repeat resolution time saved minus validation and maintenance cost.
- Measure, then decide. Track repeat resolution time saved minus validation and maintenance cost; 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 technical stack; engineers approve recipes. Implement one approved input format, a bounded representative case set and the first two task modules: extract proposed fix sequences; replay them in a sandbox. Support the third module with operator review: publish only reviewed reproducible recipes. 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 validated resolution recipe library. Retain the explicit scope boundary: One technical stack; engineers approve recipes.
What the build depends on. Authorized technical access, suitable test environments, documented APIs or schemas, secrets management, meaningful checks and recovery procedures. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One technical stack; engineers approve recipes.
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: extract proposed fix sequences; replay them in a sandbox. 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$50,000about 5 weeks of creation time · start with the MVP from $28,500
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
Technical support knowledge teams run it inside the business: approved solved cases and runnable test fixtures in, validated resolution recipe library 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
#916627 - accent
#5472c9 - surface
#f1ece4 - ink
#22201e
- Headings
- Sora
- Text
- Work Sans
- Voice
- Warm, clear, calm under pressure
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 1,000-4,000 for one bounded implementation or technical review, then USD 200-1,000 monthly for defined maintenance. Hosting, vendor fees and major feature changes are separate. Prices are hypotheses. Package the initial sale as one bounded validated resolution recipe library. 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
Turn verified fixes into reusable operational knowledge. Demonstrate a concrete validated resolution recipe library using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Technical support knowledge teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample validated resolution recipe library from a small authorized input set, with a transparent calculation of repeat resolution time saved minus validation and maintenance cost and no promised savings.
The first 30 days
- Week 1: interview five technical support knowledge teams and inspect a recent example of reliable fixes remain trapped in long case threads.
- Week 2: prepare a consented or synthetic demonstration of the three task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure repeat resolution time saved minus validation and maintenance cost, 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: Repeat resolution time saved minus validation and maintenance cost. 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
Repeat resolution time saved minus validation and maintenance cost; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs validated resolution recipe library. 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
Reliable niche implementations, integration knowledge, representative tests and ongoing operational responsibility. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for technical support knowledge teams. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
Developers, system integrators, existing automation products and internal engineering work. Compare this product with the buyer's present method on repeat resolution time saved minus validation and maintenance cost. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Engineering, testing, cloud execution, third-party API fees, monitoring, incident response and vendor-change maintenance. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of validated resolution recipe library. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Keep customer account access scoped. Escalate missing evidence and consequential exceptions to staff. Review quality alongside any speed measure. One technical stack; engineers approve recipes. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.