What do you do that ChatGPT can't?

ChatGPT is a tool. We build systems. We connect AI to your data, your apps, and your workflows. You get a custom solution that works for your business, not just a chatbox.

How long does a project take?

Most projects take 2 to 6 weeks. Quick automations take 2–3 weeks. Complex AI systems take 4–6 weeks. We'll give you a clear timeline on our first call.

What if I don't know anything about AI?

That's fine. Most of our clients don't. You tell us the problem. We figure out the fix. We speak plain English, not tech jargon.

Do you offer ongoing support?

Yes! We offer monthly support plans. We fix bugs, add features, and keep things running smooth. We want to be your long-term partner, not a one-time vendor.

How much does it cost?

Projects start at $1,000 USD. Quick automations run $1k–$3k, custom RAG systems $3k–$10k, and complex AI builds $10k+. You get a clear quote before we start. No hidden fees.

What's the Free AI MVP program?

We select 3 qualified businesses each month to build a free, small-scale AI prototype in 2–3 weeks. It lets you test our engineering and see real value before committing to a full build.

Vector Database Setup

Production Vector Databases Built to Scale

We design, deploy, and optimize vector databases for AI production environments. From Pinecone and Weaviate to pgvector and Qdrant — configured correctly the first time, with embedding pipelines and retrieval tuning included.

Pinecone · Weaviate · Qdrant · Chroma · pgvector · Starting at $800

Vector Databases We Work With

Pinecone

Managed Cloud

Managed cloud vector database — best for teams who want zero infrastructure maintenance and production-grade SLAs.

Weaviate

Open Source

Open-source with built-in vectorization modules and hybrid search. Excellent for complex enterprise RAG systems.

Qdrant

High Performance

High-performance Rust-based vector DB with rich filtering and payload storage. Best for speed-critical applications.

Chroma

Dev Friendly

Lightweight, developer-friendly DB for prototyping and smaller RAG applications. Full Python integration.

pgvector

Postgres-Native

Vector search as a PostgreSQL extension — ideal for teams already on Postgres who don't want a separate DB.

MongoDB Atlas

Atlas Search

Vector search built into MongoDB Atlas. Best for teams with existing MongoDB infrastructure.

Our Vector DB Setup Process

Requirements Analysis

We assess your dataset size, query patterns, latency requirements, budget, and existing infrastructure to recommend the right vector DB.

Embedding Model Selection

We select the optimal embedding model — OpenAI text-embedding-3-large, Cohere Embed, or open-source E5/BGE models — based on your domain.

Schema & Index Design

We design collection schemas, metadata fields, filtering strategies, and indexing configurations for optimal retrieval performance.

Data Ingestion Pipeline

We build the data ingestion pipeline that processes, chunks, embeds, and upserts your documents into the vector store at scale.

Retrieval Optimization

We tune similarity metrics, reranking (Cohere Rerank), hybrid search weights, and top-k values to maximize retrieval quality.

Get Your Free AI Audit

Set Up Your Vector Database

Tell us your AI use case and dataset size. We'll recommend the right vector DB and show you the retrieval performance you can expect.

Free consultation • No obligation • Reply within 24 hours

Request your free AI audit

Prefer a call?

Book a free 30-minute discovery call to discuss your automation needs.

Book a Call

Reach us

[email protected]

Location

Remote team serving US, UK, Europe & Australia

Reply Time

Within 24 hours

Global Coverage

Remote-first agency serving US (EST/PST), UK (GMT), and AUS (AEDT) timezones. We adapt to your schedule.