Vector Database Services That Make Your AI Smarter, Faster and Production-Ready

KriraAI designs, builds and manages AI data infrastructure, from vector search to RAG pipelines, backed by an in-house AI research team and 5+ years of AI delivery.

Vector database and AI data infrastructure services.

Years of AI & Software Delivery

100+

Enterprise & Startup Projects Delivered

In-House

Dedicated AI Research Team

Global

Clients Across India, USA & Global Markets

Trusted by Bajaj Allianz, Indian Navy, Mittsure, PM KUSUM and Teams Worldwide

Your AI Is Only as Good as the Data Behind It

The problem

Chatbots that hallucinate or give wrong answers

Slow search across thousands of documents

Data that will not scale beyond the prototype

Rising cloud costs with every new query

The solution

A production-ready vector database built for your data

Accurate, grounded answers with RAG pipelines

Search results in milliseconds, at any scale

Optimized infrastructure that keeps costs predictable

Fix My AI Data Stack

Our Vector Database & AI Data Infrastructure Services

End-to-end services, from strategy to scale.

Vector Database Consulting

Pick the right vector database and architecture before you write a line of code.

Vector Database Implementation

Schema, embeddings, indexing and APIs, built for speed and accuracy.

AI Data Infrastructure Setup

Ingestion, chunking and embedding pipelines that run reliably in production.

RAG Pipeline Development

Grounded LLM answers from your own documents, with hybrid search and re-ranking.

Vector Search & Semantic Search

Search that understands intent for websites, apps and knowledge bases.

Managed Vector Database

Monitoring, tuning, scaling and cost optimization, fully handled.

What Is a Vector Database?

A vector database stores data as vector embeddings and finds results by meaning, not exact keywords. It is the memory layer that powers RAG, semantic search and recommendations in modern AI applications.

Your Data
Embedding Model
Vector Database
AI App / Chatbot

Vector Search & RAG Solutions That Drive Results

AI Chatbots & Assistants

Support bots that answer from your documents using retrieval augmented generation.

Semantic Site Search

Users find the right result, even when they use different words.

Recommendation Engines

Personalized products, content and jobs based on similarity.

Enterprise Knowledge Search

Instant answers across PDFs, contracts, policies and wikis.

Image & Similarity Search

Find visually similar products, images and media in milliseconds.

AI Agents with Memory

Agents that remember past context and conversations.

Vector Database Technologies We Master

Vendor-neutral advice. We pick what fits your data, not what fits us.

DevelopmentPinecone

DevelopmentMilvus

DevelopmentWeaviate

DevelopmentQdrant

Developmentpgvector

Why Teams Choose KriraAI for AI Data Infrastructure

5+ Years of AI Delivery

100+ enterprise and startup projects shipped to production, not just demos.

From Idea to Production in

6 Steps

Discover

Audit your data, goals and use cases.

Design

Choose the vector database, embedding model and architecture.

Build

Clean, chunk, embed and index your data.

Integrate

Connect vector search to your app, chatbot or platform.

Optimize

Tune accuracy, speed and cost.

Launch & Support

Go live with ongoing monitoring.

Vector Database vs Traditional Database

Factor

Traditional Database

Vector Database

Searches by

Exact match

Meaning and similarity

Best for

Transactions and records

AI, RAG and semantic search

Unstructured data

Limited

Excellent

Examples

MySQL, MongoDB

Pinecone, Milvus, Qdrant

Not sure which vector database fits your project? Talk to KriraAI's research-backed engineering team and get a clear platform recommendation for your data.

Results Our Clients Talk About

View All

Government / Defense · On-Premise AI · Secure Document Retrieval

How the Indian Navy Replaced Manual Document Search With a Secure, On-Premise AI Assistant

Built entirely inside the client's own infrastructure, the system grounds every answer in verified internal documents instead of open internet knowledge.

Read full case study

What Our Clients Say

We were mainly concerned about AI accuracy for our use case. After testing, the solution achieved around 98% accuracy on our evaluated workflow. KriraAI also worked closely with us during testing and fine-tuning, which made a big difference.
Prashant Gupta

Working with KriraAI from another country was surprisingly smooth. The team communicated clearly, understood our requirements without too much back and forth, and was comfortable working across time zones. The quality of the AI development was exactly what we were looking for.
Alexander Weber

The technical knowledge of the KriraAI team was probably the best part of the project. We could discuss AI models, APIs, data, architecture, integrations, and deployment with the same team. They were able to explain technical decisions clearly without making things unnecessarily complicated.
Rohan Shah

KriraAI delivered a custom AI solution that matched our business requirements well. From AI architecture and development to integration and deployment, the team handled the project professionally. We particularly appreciated their focus on reliability, scalability, and making the technology useful for our actual business.
William Harris

I'll be honest, I wasn't sure any team could really handle the technical complexity of our customer support workflow. KriraAI proved me wrong. They took the time to understand our process, built a solution that fit our actual needs, and tested it thoroughly before going live. The result was about a 30% improvement in processing time, but what impressed me more was how accurate and reliable it was from day one. My concerns about technical skill turned out to be completely unfounded.
Roshan Patel

Ready to Build AI That Actually Knows Your Business?

Tell us what you are building. KriraAI's research-backed engineering team will propose an AI data infrastructure roadmap with clear milestones and measurable outcomes.

Vector Database

FAQs

A vector database is used to store embeddings and search them by meaning. It powers RAG chatbots, semantic search, recommendation engines, image similarity search and memory for AI agents.

Vector database services include consulting, platform selection, implementation, RAG pipeline development, integration and ongoing management. KriraAI handles the full lifecycle, from first design to production support.

AI data infrastructure is the stack that collects, prepares, stores and serves data to AI models. It covers ingestion, chunking, embedding pipelines, vector databases and monitoring.

It depends on your needs. Pinecone suits fully managed setups, Milvus suits large self-hosted workloads, Weaviate and Qdrant excel at hybrid search, and pgvector fits teams already on PostgreSQL.

Choose Pinecone for a serverless, fully managed service with fast setup. Choose Milvus for open-source control and billion-scale self-hosting at a lower long-term cost.

Your documents are turned into embeddings and stored in the vector database. When a user asks a question, the most relevant chunks are retrieved and sent to the LLM, which answers using your data.

Keyword search matches exact words. Vector search matches meaning, so "cheap flights" also finds "budget airfare." Hybrid search combines both for the best accuracy.

Cost depends on data volume, query load, hosting choice and project scope. A simple RAG chatbot can start at a few thousand dollars, while enterprise-grade AI data platforms can reach six figures. KriraAI gives a transparent, scope-based quote after understanding your requirements.

Most KriraAI projects take 4 to 16 weeks from discovery to deployment, depending on data complexity and integrations. A focused proof of concept can be ready much sooner, followed by ongoing optimization.

Yes. We integrate vector search into existing web apps, mobile apps, CRMs and internal tools through APIs. If you use PostgreSQL, pgvector adds it without a new database.