Vector search consulting
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Vector Search Database Consulting Support Services
- Our expertise
Strategic Advice: Evaluate your present search environment and pinpoint areas where vector search could be used. - Technology Selection: Suggest the best vector search engine based on your infrastructure and particular requirements.
- Seamless Integration: For a productive and easy workflow, incorporate vector search solutions with your current systems.
- Performance Optimization: Optimize your vector search engine to provide the best user experience and results.
- Support and Training: Equip your staff with the know-how to efficiently use vector search.
- Vector Search Services
To produce embeddings for the particular search domain, choose, construct, or adjust a model.
· Select the right number of dimensions, query options, and quantization.
- For both filtering and relevancy, combine lexical and semantic search.
- Adjust the hardware profile, OS and JVM settings, index structure, and merging policy, among other things.
- Create and manage a relevance testing tool for hybrid, lexical, and vector search queries.
- troubleshooting, mentoring, and code reviews
· Choosing a foundational model based on how well it fits the use case.
- Adjusting for accuracy in your particular field.
- Reranking, multi-stage retrieval, and scoring functions.
- Evaluating and improving computational resources.
- Model performance is tested and measured for your use scenarios.
- Develop a plan and evaluate your existing search environment to find areas where vector search could be used.
- design for a highly scalable solution that can handle both present and future volume demands.
- choosing and optimizing a foundational model for your use cases and business area.
- Integration services for incorporating vector search into search frameworks and systems, both new and old.
- Hybrid search system implementation and evaluation.
· Indexing, query execution, and infrastructure are optimized and scaled for maximum availability and performance.
· comprehensive reporting and analysis of usage and search data.
Vector Database Consulting Support Services
- Prepare the sample data
- Prepare the data on
- Cloud Storage
- Build and deploy a
- Vector Search index
- Create index endpoint and deploy the index
- Run a query with Vector Search
- Get an embedding to run a query
- Run a query
- Create an index
- Migrate a pod-based index to serverless
- Backup, restore index
- Delete index
- Configure index
- Choose a pod type and size
- Scale pod-based indexes
Why should you develop your Vector Search application using Nextbrick ?
As a technology supplier and systems integrator, we understand that contemporary AI applications need more than just keyword matching. Vector search enables us to put into practice:
- Enhanced Relevance: Our technologies identify contextually relevant material to user queries by utilizing vector embeddings.
- Increase Search Relevance: Regardless of the data format (text, graphics, code, etc.), provide precise and informative results.
- Improved User Experience: Provide more insightful and user-friendly search results that correspond with the user’s intent.
- Scalability and Performance: Elasticsearch can index and search massive datasets at rapid speeds because to its strong infrastructure.
- Hybrid Search: Vector Search and conventional Keyword Search can be combined effectively with Elasticsearch.
- Unlock Hidden Connections: Discover relationships in your data that were previously unknown, encouraging creativity and well-informed decision-making.
- Simplify Complex Queries: Improve user experience by using natural language queries to navigate large datasets with ease.
- Get a Competitive Edge: Keep up with the latest developments in technology that provide data-driven insights.
Our Proficiency in AI-Powered Search Engine Optimization
Our area of expertise is developing AI-powered search applications that make use of Elasticsearch’s vector search features. Our group of professionals offers comprehensive services:
- Consulting and Strategy Development: Evaluate your requirements and design a customized plan.
- Custom Integration: Create new solutions from scratch or seamlessly integrate vector search into your current technology stack.
- AI and LLM Integration: Use the appropriate Large Language Models (LLMs) to generate vector embeddings.
- Performance Optimization: Optimize the use of LLM and Elasticsearch clusters for both cost and performance.
- Complementary Technology: Use xxx platform to create and launch your application more quickly and affordably.
Advantages of Collaborating with Our Team
Working with a systems integrator who is knowledgeable with both cutting-edge AI and conventional search tools benefits you in the following ways:
- Shorter Development Time: Deployment is accelerated by our extensive experience.
Custom Solutions: Made to fit your unique business needs and industry. - Continuous Support : We’re here to make sure everything runs well, from updating your system to scaling up your solution.
Is vector search the answer ?
Vector search has the potential to completely change the search industry by converting the source data and search query into multidimensional vectors. However, is this a diversion or a silver bullet for AI? We can assist you in determining how your company may profit from these novel approaches and in developing effective vector search systems to address practical business issues.
For a number of years, the Nextbrick team has been monitoring the development of these novel approaches.
Vector Search is surrounded by a haze of jargon!
- The true functioning of vector search
- What possible uses does vector search have?
- What technologies to take into consideration
- How to use embeddings to “vectorize” your source data
- How to leverage user input to generate vector queries
- How to integrate conventional text search with vector search
- How to quantify vector search’s impact
What we plan to do
Explore the possibilities.
We will guide you through the foundations of AI and ML. Data Generation, Labeling , Curation, Enrichment. Transformers like Bert. Model selection LLMs like Gemini, Claude, OpenAI GPT, Llama, Nemotron, Milvus SLMs . Evaluate parameters such as accuracy, use case , latency, cost.
Analyze the use case.
Vector search should not be used to solve every search issue. We’ll determine which of your problems—long tail searches, multimodal search (text and images), misspellings, and language mismatch—can be solved using vector search and assist you in developing prototypes.
Proceed proof of concept of vectors
Start a regular and quick cycle of search improvement evaluated against KPIs that drive your business by evaluating vector search in an offline setting when frequent measurement and testing are in place.
Control the hybrid
For many organizations, a combination of vector and classic search methods will be the ideal option. But it’s challenging to combine the output of two very distinct systems; we’ve done it successfully at the xxx , and we can assist you in doing the same.
Proceed to Production
Let us assist you in making plans for success and stability since vector search presents a whole new set of issues, such as how frequently to retrain models, whether these models will require fine tuning, and higher processing and storage requirements.
Create a search team.
Let us assist you in creating and filling the necessary positions, creating efficient procedures, and encouraging teamwork since we know how to set up a search team for success. Let’s cover the gaps while you assemble a productive team.
~ Testimonials ~
Here’s what our customers have said.
Empowering Businesses with Exceptional Technology Consulting
~ Case Studies~
Vector search Case Studies
Vector Search : what is it?
Keep these embeddings in a database.
Similar to this, turn a query into a vector and run an index search.
While vector search uses semantic meaning to locate documents that are most similar to the query, traditional search is centered around keywords.
See vector search in Solr, Elasticsearch . Learn how we incorporated vectors, how they can transform e-commerce search, and how you can give it a try.
During Vector Search consulting we are asked the following questions:
- Which technology should I pick?
- Will conventional text search be replaced by this?
- Can I include this into my current search engine?