Saved knowledge library and stewardship console

For

Researchers, marketers and knowledge workers who collect material from the web and other sources

Solves

Saved articles, PDFs, videos and notes sit in disconnected tools, so finding, reusing and sharing what was already collected takes repeated manual effort.

Delivers

A searchable, tagged, shareable library with review states and usage records

Built in

about 4 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

Reduce time spent re-finding and re-organizing saved material while keeping the owner's sources and notes under their control.

1. Save web pages, PDFs, videos, notes and other file types in one place.
2. Find saved information quickly using AI-driven search.
3. Automatically tag and sort saved content to reduce manual effort.
4. Answer questions and assist with writing and brainstorming from saved material.
5. Generate concise summaries from articles, PDFs, videos and other content.
6. Combine saved knowledge with up-to-date web search results.
7. Work with team members in shared private spaces.
8. Share curated collections or single items with others.
9. Schedule tasks, set reminders and manage daily activities.
10. Generate written content from templates and saved sources.
11. Provide context-aware writing suggestions and editing tools.
12. Support multiple languages for saved and generated content.
13. Schedule spaced reviews of saved information to improve retention.
14. Convert rough screen recordings into polished videos and guides.
15. Automate repetitive capture and filing tasks and save them as templates.
16. Plan and schedule posts across multiple social media platforms.
17. Track engagement and adjust strategies based on performance data.
18. View documents and media directly inside the app.
19. Compare the reviewed result with the recorded baseline and value assumptions.
20. Capture corrections and named-owner approval before consequential use.
21. Export a versioned searchable, tagged, shareable library with source references and unresolved questions.

Everything these tools do, in one app

• Multi-format content saving Save web pages, PDFs, videos, notes, and other file types in one place.Found in IKI.AI, IKI.AI 2.0, Sidekic AI and 1 more
• AI-powered search Find saved information quickly using AI-driven search.Found in IKI.AI, IKI.AI 2.0, Sidekic AI and 1 more
• Automatic tagging and sorting Automatically organize and tag saved content to reduce manual effort.Found in Sidekic AI, Glasp AI Clone: Learning Memory
• AI assistant for questions Ask questions and get answers or help with writing and brainstorming.Found in IKI.AI, IKI.AI 2.0, Maika Assistant 1.0
• Content summarization Generate concise summaries from articles, PDFs, videos, and other content.Found in Linfo.ai, Glasp AI Clone: Learning Memory, Trupeer
• Web search integration Enhance research by combining saved knowledge with up-to-date web search results.Found in IKI.AI, IKI.AI 2.0, Maika Assistant 1.0
• Collaborative spaces Work together with team members in shared private environments.Found in IKI.AI 2.0, Laterbase, Aili
• Content sharing Share curated collections or content with others easily.Found in IKI.AI, Sidekic AI
• Task management Schedule tasks, set reminders, and manage daily activities.Found in Maika Assistant 1.0
• Content generation Automatically generate written content using templates and AI.Found in Aili, Strawberry
• Writing assistance Get context-aware suggestions and editing tools to improve writing.Found in Aili, Strawberry
• Multi-language support Use the tool in multiple languages for global content.Found in Aili, Linfo.ai
• Spaced repetition Schedule reviews of saved information to improve memory retention.Found in Glasp AI Clone: Learning Memory
• Screen recording to video Convert rough screen recordings into polished videos and guides.Found in Trupeer
• Workflow automation Automate repetitive tasks on websites and save workflows as templates.Found in Strawberry
• Social media scheduling Plan and schedule posts across multiple social media platforms.Found in Laterbase
• Performance analytics Track engagement and adjust strategies based on performance data.Found in Laterbase
• Embedded content reader View documents and media directly within the app.Found in IKI.AI

What goes in, what comes out

The workflow

1. In
   Start with

   Saved web pages, PDFs, videos, notes and other files, plus permitted web search results and user-written questions

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect saved web pages

4. 3

   PDFs

5. 4

   Videos

6. 5

   Notes and other files

7. 6

   Then follow this sequence: 1

8. Out
   Finish with

   A searchable, tagged, shareable library with review states and usage records

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs 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. One fixed set of supported file types and languages; final fact, rights and publication checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Library and capture, Item reader and editor, Collections and sharing, Stewardship console. Use a thumbnail and list gallery for saved items, a large central reader for documents and media, and a right-hand panel for tags, summaries, questions and comments. Let users compare saved versions side by side. Display draft, reviewed and shared states. Provide a client preview link with comments anchored to the relevant item. Make the task-specific outcome a searchable, tagged, shareable library with review states and usage records visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, item versions, client comments, 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 saved items, permitted web sources and authorized research feeds. Cloud asset storage, file import/export and publishing 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

   5 days

   One buyer segment, one recurring use case; first modules: save web pages, PDFs, videos, notes and other file types in one place; find saved information quickly using AI-driven search. 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 researchers, marketers and knowledge workers who collect material from the web and other sources use it to solve "saved articles, PDFs, videos and notes sit in disconnected tools, so finding, reusing and sharing what was already collected takes repeated manual effort"?
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: Time to retrieve a saved item and reuse rate of saved material.
4. Measure, then decide. Track time to retrieve a saved item and reuse rate of saved material; 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 set of supported file types and languages; final fact, rights and publication checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: save web pages, PDFs, videos, notes and other file types in one place; find saved information quickly using AI-driven search. Support the third module with operator review: automatically tag and sort saved content to reduce manual effort. 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 the searchable, tagged, shareable library with review states and usage records. Retain the explicit scope boundary: One fixed set of supported file types and languages; final fact, rights and publication checks remain human.

What the build depends on. Item upload and preview, asynchronous processing jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist editorial QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed set of supported file types and languages; final fact, rights and publication checks remain human.

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 library package. Offer a monthly production allowance after repeat demand. Quote complex video, multi-language or specialist integrations separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, tagged, shareable library with review states and usage records. 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 time spent re-finding and re-organizing saved material while keeping the owner's sources and notes under their control. Demonstrate a concrete searchable, tagged, shareable library with review states and usage records using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Researchers, marketers and knowledge workers who collect material from the web and other sources professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, tagged, shareable library with review states and usage records from a small authorized input set, with a transparent calculation of time to retrieve a saved item and reuse rate of saved material and no promised savings.

The first 30 days

1. Week 1: interview five researchers, marketers and knowledge workers who collect material from the web and other sources and inspect a recent example of saved articles, PDFs, videos and notes sitting in disconnected tools.
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 time to retrieve a saved item and reuse rate of saved material, 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: Time to retrieve a saved item and reuse rate of saved material. 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

Time to retrieve a saved item and reuse rate of saved material; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs a searchable, tagged, shareable library with review states and usage records. 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 tags, collection structures and review examples, together with reliable delivery for a narrow knowledge-work niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for researchers, marketers and knowledge workers who collect material from the web and other sources. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

IKI.AI, IKI.AI 2.0, Laterbase, Sidekic AI, Maika Assistant 1.0, Aili, Linfo.ai, Glasp AI Clone: Learning Memory, Trupeer and Strawberry. Compare this product with the buyer's present method on time to retrieve a saved item and reuse rate of saved material. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Capture and processing attempts, video or image processing, 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 the searchable, tagged, shareable library with review states and usage records. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Owners approve substantive changes and sharing scope. One fixed set of supported file types and languages; final fact, rights and publication checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.