Vector Database Setup (Pinecone, Weaviate, Qdrant, Chroma)

Overview

What we deliver

We design, deploy, and tune vector databases on Pinecone, Weaviate, Qdrant, and Chroma so AI systems retrieve the right data fast.

We set up vector databases for teams building AI search, RAG systems, recommendation engines, and semantic features. Our team helps you pick the right platform across Pinecone, Weaviate, Qdrant, and Chroma based on scale, latency, hosting model, and budget. We then handle the full setup: index design, embedding model selection, chunking strategy, metadata schema, and access controls. We build the ingestion pipeline that keeps the database in sync with your source content, and we implement hybrid search where keyword and vector retrieval need to work together. We tune index parameters, reranking, and filters to hit your accuracy and latency targets, and we benchmark the system against your real queries. After launch we monitor index health, query performance, and cost, and we support migrations between platforms when needs change. Teams get a database that scales with their AI roadmap.

Fit Check

Built for teams like yours

Who it's for

AI engineering teams
Product teams adding semantic search
RAG system owners
Recommendation engine teams
Data platform teams

Pain points we solve

Slow or inaccurate semantic search
Index design mistakes
Sync drift between source and database
High vector database costs
Latency issues at scale

What's included

Capabilities

Everything we cover in this engagement.

Platform selection
Index and schema design
Embedding model selection
Ingestion and sync pipelines
Hybrid search setup
Reranking and filtering
Performance and cost tuning
Migration support

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We map use case, scale, and constraints.

02

Selection

We pick the right platform and embedding model.

03

Build

We deploy the database and ingestion pipeline.

04

Tune

We benchmark and optimize accuracy and latency.

05

Operate

We monitor health, performance, and cost.

What you get

Deliverables & outcomes

What you get

Configured vector database
Ingestion pipeline
Index and schema documentation
Benchmark report
Monitoring dashboard
Operations runbook

Outcomes you can expect

Faster retrieval
Higher search accuracy
Lower infrastructure cost
Reliable data sync
Scalable AI foundation

Timeline

3 to 8 weeks

Engagement

Monthly retainer, Project, Sprint

Tools we use

Pinecone, Weaviate, Qdrant, Chroma, OpenAI

KPIs we track

Query latency, recall, precision, index size, cost per query

Client stories

What clients say

"

Two weeks before our seed round we still did not have a defensible model. Their fractional CFO rebuilt our three-statement forecast, pressure-tested the assumptions, and walked me through every line before the partner meeting. We closed 1.4M on the terms we wanted. The investor specifically called out how clean the financials looked compared to the last five decks she had seen.

Hannah B.

"

My books were 90 days behind and I was avoiding my accountant. They cleaned up nine months of mis-categorized Shopify and Stripe entries, set up proper rules in QuickBooks, and now my close lands on day four of every month. First time in three years I opened a P&L without wincing. Cash forecasting actually makes sense now.

D.R.

Proof