Private search and data stewardship console

For

IT, security and privacy teams that need untracked web research and a searchable record of what was found

Solves

Staff research runs through tracked search services, so queries, sources and findings are scattered, profiled and hard to reuse or audit.

Delivers

A searchable, permissioned research library with source references

Built in

about 5 weeks of creation time, MVP in 6 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 exposure of research activity while keeping findings searchable and reusable.

1. Run private searches without tracking or profiling.
2. Store no personal search history by default.
3. Query an independent index rather than a single major provider.
4. Apply community-weighted ranking signals with visible provenance.
5. Return images, maps, news and chat results in one view.
6. Gather and organize multiple pages into a single brief.
7. Support !Bang shortcuts to external sites.
8. Block ads and trackers in the reading view.
9. Provide a distraction-free reader mode.
10. Open with the keyboard ready for immediate search.
11. Adapt the interface colors to the visited site.
12. Integrate with privacy-focused browsers.
13. Offer a dedicated private browsing surface.
14. Provide browser extensions for supported platforms.
15. Support VPN routing for added privacy.
16. Apply email protection to reduce digital footprint.
17. Show results from an anonymous global perspective.
18. Allow use without an account.
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, permissioned research library with source references and unresolved questions.

Everything these tools do, in one app

• Private search Search the web without being tracked or profiled.Found in Brave Search, DuckDuckGo AI
• No personal data storage Your search history and personal information are not stored.Found in DuckDuckGo AI
• Independent search index Results come from its own index rather than relying on major search providers.Found in Brave Search
• Community-weighted ranking Rankings are influenced by community input to improve transparency and result quality.Found in Brave Search
• Comprehensive search results Get complete search results including images, maps, news, and chat.Found in DuckDuckGo AI
• Browse for Me Automatically gathers and organizes information from multiple pages into a single tab.Found in Arc Search
• !Bang shortcuts Quickly search external websites directly from the search interface.Found in DuckDuckGo AI
• Ad and tracker blocking Blocks ads and trackers for a cleaner, more private browsing experience.Found in Arc Search
• Reader Mode Provides a distraction-free reading environment.Found in Arc Search
• Quick Search Interface Opens with the keyboard ready for immediate searching.Found in Arc Search
• Minimal adaptive design Interface adapts to match the colors of visited websites.Found in Arc Search
• Privacy-focused browser integration Works seamlessly with privacy-focused browsers.Found in Brave Search
• Dedicated browser Offers a dedicated browser for private browsing.Found in DuckDuckGo AI
• Browser extensions Provides extensions for various platforms to maintain privacy.Found in DuckDuckGo AI
• Built-in VPN Includes VPN capabilities for added privacy.Found in DuckDuckGo AI
• Email protection Offers email protection to minimize digital footprint.Found in DuckDuckGo AI
• Anonymous global perspective Option to view search results from an anonymous global perspective.Found in Brave Search
• No account needed Use the service without creating an account.Found in Arc Search

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted queries, approved sources and team notes

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted queries

4. 3

   Approved sources and team notes

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   A searchable, permissioned research library with source references

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 approved index configuration and permitted source set; final source verification and privacy judgments remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Query and source intake, Searchable library console, Brief and export. Use a thumbnail gallery for saved research sets, a large central result and document viewer, and a right-hand panel for sources, tags, permissions and comments. Let users compare result versions side by side. Display draft, changes requested and approved states. Provide a permissioned share link with comments anchored to the relevant source. Make the task-specific outcome a searchable, permissioned research library with source references visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, team comments, approval states, usage allowances, retention limits, export 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

Team-owned query logs, approved source lists and permitted research sources. Cloud storage, browser extension surfaces and export 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

   6 days

   One buyer segment, one recurring use case; first modules: run private searches without tracking or profiling; store no personal search history by default. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   7 days

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

4. 4

   Full product

   3 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 IT, security and privacy teams that need untracked web research and a searchable record of what was found use it to solve "staff research runs through tracked search services, so queries, sources and findings are scattered, profiled and hard to reuse or audit"?
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 research briefs per reviewer hour and repeat use of stored findings.
4. Measure, then decide. Track accepted research briefs per reviewer hour and repeat use of stored findings; 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 approved index configuration and permitted source set; final source verification and privacy judgments remain human. Implement one approved input format, a bounded representative case set and the first two task modules: run private searches without tracking or profiling; store no personal search history by default. Support the third module with operator review: query an independent index rather than a single major provider. 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 a searchable, permissioned research library with source references. Retain the explicit scope boundary: One approved index configuration and permitted source set; final source verification and privacy judgments remain human.

What the build depends on. Source upload and preview, asynchronous search jobs, editable version history, reviewer access and tested export formats. High-fidelity privacy work requires specialist security QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved index configuration and permitted source set; final source verification and privacy judgments 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 research package. Offer a monthly research allowance after repeat demand. Quote complex integrations or specialist privacy review separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable, permissioned research library with source references. 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 exposure of research activity while keeping findings searchable and reusable. Demonstrate a concrete searchable, permissioned research library with source references using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

IT, security and privacy teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable, permissioned research library with source references from a small authorized input set, with a transparent calculation of accepted research briefs per reviewer hour and repeat use of stored findings and no promised savings.

The first 30 days

1. Week 1: interview five IT, security and privacy teams that need untracked web research and a searchable record of what was found and inspect a recent example of staff research running through tracked search services, so queries, sources and findings are scattered, profiled and hard to reuse or audit.
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 accepted research briefs per reviewer hour and repeat use of stored findings, 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 research briefs per reviewer hour and repeat use of stored findings. 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 research briefs per reviewer hour and repeat use of stored findings; 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, permissioned research library with source references. 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 sources, ranking rules and review examples, together with reliable delivery for a narrow privacy-focused niche. Build a permissioned library of representative research cases, reviewer corrections and verified operating constraints for IT, security and privacy teams that need untracked web research and a searchable record of what was found. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Brave Search, DuckDuckGo AI and Arc Search, plus the buyer's present mix of tracked search, browser bookmarks and manual notes. Compare this product with the buyer's present method on accepted research briefs per reviewer hour and repeat use of stored findings. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Search and index queries, storage, reviewer hours, client revision rounds and licensed source access. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of a searchable, permissioned research library with source references. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, quotation accuracy and usage permissions. Named owners approve substantive changes and publication scope. One approved index configuration and permitted source set; final source verification and privacy judgments remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.