We build with Pinecone
We use Pinecone to build fast vector search for semantic search, recommendations, and retrieval-augmented AI that supports your business goals. From index design and embedding pipelines to query tuning and scaling, we create products designed to evolve with your needs.
DISCUSS YOUR PROJECTFast Similarity Search
We return nearest-neighbor matches in milliseconds at scale.
Serverless Architecture
No index infrastructure to provision, patch, or manage manually.
Hybrid Query Filtering
We combine vector similarity with structured metadata filters.
BENEFITS OF PINECONE technology
We use Pinecone to power semantic search, ground LLM responses in relevant context, and scale indexes automatically.
BUILD
[01]- Design vector indexes
- Configure embedding pipelines
- Set similarity metrics
- Define metadata schemas
ENGAGE
[02]- Power semantic search
- Ground LLM responses
- Support RAG pipelines
- Filter by metadata
GROW
[03]- Scale indexes automatically
- Optimize query latency
- Add new namespaces
- Extend to new use cases
Our Pinecone Technology Stack
We combine Pinecone with LangChain, LlamaIndex, and embedding models from OpenAI and Cohere for retrieval-augmented generation, semantic search, and recommendation systems tailored to your data.
Custom Pinecone development company
With our Pinecone development services, we build retrieval-augmented generation systems, semantic search engines, and AI chatbot knowledge bases, tailored to your boldest business goals. Having years of experience with vector databases and embedding pipelines, our engineers harness Pinecone's full potential to deliver fast, relevant, and scalable AI-powered features across industries and company sizes. Whether it's adding vector search to an existing application or building a new RAG pipeline from scratch, we design indexes and embedding strategies that scale with your product. Our range of Pinecone development services spans consulting, index design, embedding pipeline integration, and ongoing optimization. We work as your technical partner and ensure complete transparency and comfortable communication throughout the process, bringing deep AI infrastructure expertise and a pragmatic approach to vector search and problem-solving. All this to make sure your application returns relevant results fast as usage and data grow, and moves your business forward.
OUR PINECONE SERVICES
We build, modernize, and support Pinecone search systems around your product goals.
Your roadmap is growing faster than your team. Add senior engineering capacity and deliver more without sacrificing quality.
Vector schemas and namespace strategies built around your embedding models and real query patterns.
Retrieval-augmented generation pipelines that ground LLM responses in your own data.
Semantic search and recommendation features connected to your product's existing data sources.
Migrate from another vector store or optimize an existing Pinecone index for cost and speed.
Meet our Pinecone experts
A curated selection of senior specialists currently available for new engagements.
We Turn Technology Into Results
Partner with a team that blends technical precision, creative design, and business insight. We’ll help you launch, scale, and dominate your digital niche.
Frequently Asked Questions
Common questions about how we use Pinecone and what it can bring to your project. Have a specific requirement?
How does SoftDoes use Pinecone?
We use Pinecone to build vector search backends for semantic search, RAG systems, and AI chatbot knowledge bases. We design indexes and embedding pipelines around your data and query patterns.
What types of applications do you build with Pinecone?
We build retrieval-augmented generation systems, semantic search engines, recommendation features, and AI chatbots that ground responses in your own content and documents.
Can Pinecone integrate with our existing AI stack?
Yes. Pinecone works well alongside LangChain, LlamaIndex, and embedding models from providers like OpenAI and Cohere, and can sit alongside your existing databases and application logic.
Do you help design embedding and indexing strategies?
Yes. We help choose embedding models, define namespace and metadata structures, and set similarity metrics based on how your application queries data.
Can you migrate us from another vector database to Pinecone?
Yes. We assess your current index structure and access patterns, then migrate your vectors and metadata to Pinecone with minimal disruption to your application.
When should we use Pinecone instead of a self-hosted vector store?
Pinecone fits well when you need managed scaling, low query latency, and minimal operational overhead. For smaller datasets or tight infrastructure control requirements, a self-hosted option like pgvector may still be worth considering.
How do you decide whether Pinecone fits a project?
We look at your data volume, query patterns, latency requirements, and existing AI stack, then confirm Pinecone is the right fit before starting development.
