Own-data AI search and retrieval console

For

Teams running AI search and retrieval over their own documents and records

Solves

Search and retrieval over internal data is split across rented tools, so indexes, filters, pipelines and answers live in separate places with unclear data ownership.

Delivers

Reviewed, source-linked answers and a maintained index

Built in

about 5 weeks of creation time, MVP in 6 days

Investment

$13,000 for the MVP, $44,000 for the full product

Run it

Inside your business, or as part of your offer to clients

Run contextual search, indexing and retrieval-augmented answers over your own data in one owned console.

1. Search by intent beyond keyword matching.
2. Index large data volumes with near real-time updates.
3. Ingest and refresh data automatically from connected storage.
4. Apply customizable filters to tailor results.
5. Run a managed retrieval pipeline with maintenance handled.
6. Support retrieval-augmented generation and agent workflows.
7. Run small language models locally or in a private cloud.
8. Provide an open library of example applications and models.
9. Expose answers through API calls and worker scripts.
10. Connect to common data management platforms and APIs.
11. Integrate with object storage, vector indexes, model gateways and edge workers.
12. Set up through a guided console with few steps.
13. Show search trends and index health in an analytics dashboard.
14. Score answer confidence and flag likely hallucinations.
15. Keep the interface usable with minimal training.
16. Run on standard Intel-based enterprise laptops.
17. Compare the reviewed result with the recorded baseline and value assumptions.
18. Capture corrections and named-owner approval before consequential use.
19. Export a versioned reviewed answer set with source references and unresolved questions.

Everything these tools do, in one app

• Contextual AI Search Search that understands user intent beyond simple keyword matching.Found in LiquidIndex 2.0
• High-Speed Indexing Enables near real-time updates and searches across large data volumes.Found in LiquidIndex 2.0
• Automated Continuous Indexing Automatically ingests and updates data from connected storage to keep AI responses relevant.Found in Cloudflare AutoRAG
• Customizable Filters Allows tailored search results based on specific user needs.Found in LiquidIndex 2.0
• Fully Managed Pipeline Handles the entire infrastructure and maintenance needed for RAG pipelines, reducing operational overhead.Found in Cloudflare AutoRAG
• RAG Framework Provides a framework for Retrieval-Augmented Generation and AI agent workflow automation.Found in LLMWare
• Local Model Support Supports small language models optimized for local or private cloud deployment.Found in LLMWare
• Open-Source Library Offers an open-source library with over 100 example applications and 75+ models available on Hugging Face.Found in LLMWare
• API and Worker Bindings Offers flexible querying options to fetch AI-generated answers via API calls or integrated Worker scripts.Found in Cloudflare AutoRAG
• Platform Integrations Integrates with popular data management platforms and APIs for seamless workflow incorporation.Found in LiquidIndex 2.0
• Cloudflare Ecosystem Integration Built natively on Cloudflare’s stack, integrating with R2 storage, Vectorize, Workers AI, and AI Gateway.Found in Cloudflare AutoRAG
• Simple Setup Enables quick onboarding with just a few clicks through the Cloudflare dashboard, minimizing time to deployment.Found in Cloudflare AutoRAG
• Analytics Dashboard Monitors search trends and helps optimize data organization.Found in LiquidIndex 2.0
• Confidence Scoring Verifies responses and reduces hallucinations.Found in LLMWare
• User-Friendly Interface Requires minimal training to operate effectively.Found in LiquidIndex 2.0
• Intel Laptop Compatibility Compatible with Intel-based enterprise laptops for straightforward deployment.Found in LLMWare

What goes in, what comes out

The workflow

1. In
   Start with

   Connected storage, document collections, access rules and query logs

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect connected storage

4. 3

   Document collections

5. 4

   Access rules and query logs

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Reviewed, source-linked answers and a maintained index

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers for the stated task modules. Use deterministic code for arithmetic, schema validation, access rules and reproducible tests. Review source-linked explanations and confidence scores before accepting results. One approved data scope and access model; final data classification and release decisions remain with the data owner. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data sources and index status, Search and answer workspace, Stewardship and analytics. Use a source list with sync state, a central query and answer view with citations, and a right-hand panel for filters, confidence and review notes. Let users compare retrieved passages side by side. Display draft, changes requested and approved states. Provide a shareable answer link with comments anchored to the cited passage. Make the task-specific outcome reviewed, source-linked answers and a maintained index visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, access boundaries, approval states, usage allowances, query 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

Customer-owned storage, document systems and permitted data sources. Object storage, vector indexes, model gateways, worker runtimes and data management APIs. 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: search by intent beyond keyword matching; index large data volumes with near real-time updates. 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 teams running AI search and retrieval over their own documents and records use it to solve "search and retrieval over internal data is split across rented tools, so indexes, filters, pipelines and answers live in separate places with unclear data ownership"?
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 reviewer hour and retrieval errors found after release.
4. Measure, then decide. Track accepted answers per reviewer hour and retrieval errors found after release; 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 data scope and access model; final data classification and release decisions remain with the data owner. Implement one approved input format, a bounded representative case set and the first two task modules: search by intent beyond keyword matching; index large data volumes with near real-time updates. Support the remaining modules with operator review: ingest and refresh data automatically from connected storage; apply customizable filters to tailor results. 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 reviewed, source-linked answers and a maintained index. Retain the explicit scope boundary: One approved data scope and access model; final data classification and release decisions remain with the data owner.

What the build depends on. Source upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity retrieval requires specialist data QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved data scope and access model; final data classification and release decisions remain with the data owner.

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 data scope. Offer a monthly retrieval allowance after repeat demand. Quote complex multi-source or private-cloud deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed, source-linked answers and a maintained index. 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

Run contextual search, indexing and retrieval-augmented answers over your own data in one owned console. Demonstrate a concrete reviewed, source-linked answers and a maintained index using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Teams running AI search and retrieval over their own documents and records 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 a maintained index from a small authorized input set, with a transparent calculation of accepted answers per reviewer hour and retrieval errors found after release and no promised savings.

The first 30 days

1. Week 1: interview five teams running AI search and retrieval over their own documents and records and inspect a recent example of search and retrieval over internal data is split across rented tools, so indexes, filters, pipelines and answers live in separate places with unclear data ownership.
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 answers per reviewer hour and retrieval errors found after release, 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 reviewer hour and retrieval errors found after release. 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 reviewer hour and retrieval errors found after release; 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 a maintained index. 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 data scopes, access rules and review examples, together with reliable delivery for a narrow retrieval niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for teams running AI search and retrieval over their own documents and records. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

LiquidIndex 2.0, Cloudflare AutoRAG and LLMWare, plus internal scripts and generic search tools. Compare this product with the buyer's present method on accepted answers per reviewer hour and retrieval errors found after release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Indexing compute, model inference, storage, reviewer hours, client revision rounds and licensed source data. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed, source-linked answers and a maintained index. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve data ownership, source attribution, access boundaries and usage permissions. Data owners approve substantive changes and release scope. One approved data scope and access model; final data classification and release decisions remain with the data owner. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.