Vector search library and data stewardship console

For

Product and platform teams embedding semantic search, recommendations and retrieval into their own applications

Solves

Vector search is spread across several rented services, so embeddings, indexes, filters, ranking and access rules live in different places and cannot be searched or governed as one library.

Delivers

Searchable vector library with filters, ranking and access rules

Built in

about 5 weeks of creation time, MVP in 6 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 the number of rented vector services while keeping one searchable, governed store of embeddings and metadata.

1. Ingest vectors from text, images, audio and other unstructured data.
2. Run vector similarity search against a query embedding.
3. Apply custom filters to search results.
4. Re-rank results with hosted models.
5. Index data automatically as it arrives.
6. Keep data synchronized in real time across devices and platforms.
7. Expose a RESTful API and multi-language examples.
8. Provide an interactive playground for test requests.
9. Connect AI frameworks and SDKs such as LangChain and LlamaIndex.
10. Support natural language queries over the library.
11. Show customizable dashboards and usage tracking.
12. Enforce encryption, access control and compliance rules.
13. Run the same API on cloud, on-prem and edge devices.
14. Back up and shard data automatically.
15. Compare the reviewed result with the recorded baseline and value assumptions.
16. Capture corrections and named-owner approval before consequential use.
17. Export a versioned searchable vector library with source references and unresolved questions.

Everything these tools do, in one app

• Vector similarity search Finds items that are similar to a query using vector embeddings.Found in SemaDB, Pinecone Serverless, Zilliz Cloud and 6 more
• Fully managed service Runs the database for you so you don't have to manage servers or infrastructure.Found in SemaDB, Pinecone Serverless, Zilliz Cloud and 3 more
• Automatic scaling Adjusts resources automatically as your data and query load grow.Found in SemaDB, Pinecone Serverless, Zilliz Cloud and 3 more
• No manual tuning Avoids the need to configure schemas, pod sizes, or search algorithms.Found in SemaDB, Asimov
• Automatic backups Backs up your data automatically to protect against loss.Found in SemaDB, Pinecone Serverless
• Automatic sharding Distributes data across multiple nodes automatically for performance.Found in SemaDB
• RESTful API Lets you interact with the service using standard HTTP requests.Found in SemaDB, Asimov, Zilliz Cloud Serverless and 3 more
• Interactive playground Provides a web-based tool to test API requests and see examples.Found in SemaDB, Asimov
• Multi-language examples Offers example requests in many programming languages to help you get started.Found in SemaDB
• Free tier Allows you to start using the service without upfront cost.Found in SemaDB, Asimov, Actian VectorAI DB and 2 more
• Usage-based pricing Charges based on the amount of data processed and queries executed.Found in Pinecone Serverless, Zilliz Cloud, Zilliz Cloud Serverless and 1 more
• Automatic indexing Indexes data automatically to speed up search and streamline development.Found in Pinecone Serverless, SuperDuperDB
• Integration with AI frameworks Works with popular machine learning and data processing tools.Found in Pinecone Serverless, Zilliz Cloud, Zilliz Cloud Serverless and 1 more
• Support for multiple data types Handles vectors from images, text, audio, and other unstructured data.Found in Zilliz Cloud, Marqo
• Security and compliance Provides encryption, access control, and compliance certifications for enterprise use.Found in Zilliz Cloud, Zilliz Cloud Serverless, Actian VectorAI DB
• Hosted re-ranking Uses hosted models to re-rank search results for better relevance.Found in Asimov
• Custom filtering Lets you filter search results using custom parameters.Found in Asimov, Meilisearch AI
• Usage tracking Tracks usage and events to help you monitor and debug.Found in Asimov
• 24/7 support Provides round-the-clock support for production workloads.Found in Asimov
• Firebase integration Integrates with Firebase services like Firestore and Authentication.Found in Firebase Vector Search
• Real-time synchronization Keeps data updated in real time across devices and platforms.Found in Firebase Vector Search, SuperDuperDB
• Portable deployment Runs on edge devices, on-prem servers, and cloud with the same API.Found in Actian VectorAI DB
• Edge and offline operation Supports local, low-bandwidth, or disconnected use cases.Found in Actian VectorAI DB
• SDKs and integrations Provides SDKs and compatibility with tools like LangChain and LlamaIndex.Found in Actian VectorAI DB
• Container and orchestration support Distributed as a Docker container and compatible with Kubernetes, Helm, and Terraform.Found in Actian VectorAI DB
• Open-source and self-hosted Allows you to host the search engine on your own infrastructure with no licensing fees.Found in Meilisearch AI
• AI-powered ranking Uses machine learning to rank search results for better relevance.Found in Meilisearch AI
• Natural language query Lets you query the database using plain English.Found in SuperDuperDB
• Customizable dashboards Provides dashboards and reporting tools for data visualization.Found in SuperDuperDB

What goes in, what comes out

The workflow

1. In
   Start with

   Permitted documents, images, audio and event data, embedding model choice, filter and ranking rules, access policies

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect permitted documents

4. 3

   Images

5. 4

   Audio and event data

6. 5

   Then follow this sequence: 1

7. Out
   Finish with

   Searchable vector library with filters, ranking and access rules

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 embedding model and index configuration; final relevance and access checks remain with the owning team. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Data source and ingestion, Searchable library console, Access and usage review. Use a thumbnail gallery for collections, a large central search and result canvas, and a right-hand panel for filters, ranking and comments. Let users compare result sets side by side. Display draft, indexed and approved states. Provide a client preview link with comments anchored to the relevant record. Make the task-specific outcome a searchable vector library with filters, ranking and access rules visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset 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

Customer-owned documents, authorized media and permitted event data. Cloud asset storage, AI framework and SDK connections, and application 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: ingest vectors from text, images, audio and other unstructured data; run vector similarity search against a query embedding. 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 product and platform teams embedding semantic search, recommendations and retrieval into their own applications use it to solve "vector search is spread across several rented services, so embeddings, indexes, filters, ranking and access rules live in different places and cannot be searched or governed as one library"?
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 search results per query and retrieval errors after release.
4. Measure, then decide. Track accepted search results per query and retrieval errors 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 fixed embedding model and index configuration; final relevance and access checks remain with the owning team. Implement one approved input format, a bounded representative case set and the first two task modules: ingest vectors from text, images, audio and other unstructured data; run vector similarity search against a query embedding. Support the third module with operator review: apply custom filters to search 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 the searchable vector library with filters, ranking and access rules. Retain the explicit scope boundary: One fixed embedding model and index configuration; final relevance and access checks remain with the owning team.

What the build depends on. Asset upload and preview, asynchronous indexing jobs, editable version history, reviewer access and tested export formats. High-fidelity retrieval requires specialist relevance QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One fixed embedding model and index configuration; final relevance and access checks remain with the owning team.

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 package. Offer a monthly production allowance after repeat demand. Quote complex video, 3D or specialist retrieval separately. These are test prices, not market benchmarks. Package the initial sale as one bounded searchable vector library with filters, ranking and access rules. 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 the number of rented vector services while keeping one searchable, governed store of embeddings and metadata. Demonstrate a concrete searchable vector library with filters, ranking and access rules using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and platform teams embedding semantic search, recommendations and retrieval into their own applications professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample searchable vector library with filters, ranking and access rules from a small authorized input set, with a transparent calculation of accepted search results per query and retrieval errors after release and no promised savings.

The first 30 days

1. Week 1: interview five product and platform teams embedding semantic search, recommendations and retrieval into their own applications and inspect a recent example of vector search spread across several rented services, so embeddings, indexes, filters, ranking and access rules live in different places and cannot be searched or governed as one library.
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 search results per query and retrieval errors 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 search results per query and retrieval errors 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 search results per query and retrieval errors 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 a searchable vector library with filters, ranking and access rules. 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 collections, filter and ranking 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 product and platform teams embedding semantic search, recommendations and retrieval into their own applications. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

SemaDB, Pinecone Serverless, Zilliz Cloud, Asimov, Zilliz Cloud Serverless, Firebase Vector Search, Marqo, Actian VectorAI DB, Meilisearch AI and SuperDuperDB are what buyers use today. Compare this product with the buyer's present method on accepted search results per query and retrieval errors after release. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Embedding and re-ranking calls, 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 the searchable vector library with filters, ranking and access rules. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve source attribution, embedding provenance, access permissions and usage rights. The owning team approves substantive changes and publication scope. One fixed embedding model and index configuration; final relevance and access checks remain with the owning team. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.