Vector Databases SoftBrixAI Stack

Enterprise Weaviate Engineering

The open-source, schema-driven vector database, built to store and search data objects and vectors. We design, optimize, and deploy production-grade architectures utilizing the full capabilities of the Weaviate ecosystem.

Missing SVG Weaviate

Weaviate

Production Certified

Architectural Overview

Weaviate is our vector database of choice for deployments requiring local hosting and strict schema structures. We build schema models, configure hybrid search indexes, and deploy Weaviate instances on Kubernetes.

Capabilities

Our Weaviate Engineering Services

We deliver highly specialized, production-ready systems tailored to your technical requirements.

schema

Weaviate Schema Design

We design Weaviate classes, mapping properties, data types, and configuring vector index settings.

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On-Premise Deployment

We host Weaviate instances locally using Docker or Kubernetes, keeping vector data within your secure VPC.

search

Hybrid & BM25 Search

We configure Weaviate's hybrid search, combining vector calculations with keyword BM25 algorithms.

cloud_upload

Data Object Upserts

We build batch upsert pipelines in Weaviate, importing text and vector files.

Ecosystem

Weaviate Tooling & Stack Integrations

We operate across the entire modern ecosystem surrounding Weaviate, deploying optimized dependencies and configurations.

Weaviate Stack

Core Weaviate clients and query tools.

weaviate-client GraphQL API v3/v4 SDK Weaviate Console

Module Integrations

Internal Weaviate vectorizer modules.

text2vec-openai text2vec-cohere text2vec-transformers

Hosting & Operations

Tools used to host Weaviate servers.

Kubernetes (EKS/GKE) Docker Compose Helm Charts
Why Choose SoftBrixAI

Production-Grade Engineers

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engineering

Top 1% Seniority

Experienced database engineers specializing in Weaviate schemas.

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bolt

Immediate Velocity

Successful on-premise deployments of Weaviate inside secure VPCs.

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Compliance Native

Optimized query structures utilizing GraphQL endpoints.

Case Studies

Proven Results with Weaviate

Explore how we leverage Weaviate to build business-critical platforms and achieve operational milestones.

Legal Verified Output

Weaviate Search Index Powers Contract Audit Tool

We hosted Weaviate locally inside a private VPC, indexing legal document clauses and keeping queries secure.

#Weaviate #Docker #Python #React
Precision Medicine Verified Output

Medical Document Index Reconciles Patient Records

Built a Weaviate schema to index clinical case reports, running semantic search queries across patient notes.

#Weaviate #LlamaIndex #AWS #Python
Flexible Cooperation

Flexible Engagement Models

Scale your engineering capacity dynamically. We integrate seamlessly into your operations with three battle-tested engagement models.

Model 01

Staff Augmentation

Inject senior AI and MLOps engineers directly into your active squads. Rapidly scale resources with dedicated support under your management.

Scale in 48 Hours chevron_right
Model 02

Dedicated Team

A self-governing team of engineers, project managers, and QA specialists built specifically to design, build, and support your proprietary AI pipelines.

Turnkey Operations chevron_right
Model 03

Full Build & Deliver

Fixed-scope or milestone-driven development. We take ownership from requirements definition and MVP design to final production handoff.

Milestone Guaranteed chevron_right
Enterprise Trust & Security

Compliance-First Deployment Standards

We integrate safety controls natively. Every Weaviate application is architected to satisfy strict global compliance and privacy policies.

verified_user

SOC 2 Type II

Logical isolation & logs

health_and_safety

HIPAA PHI

PHI data de-identification

gavel

ISO/IEC 27001

International safeguards

FAQ

Common Weaviate Questions

Why do you use Weaviate over Pinecone? expand_more
We recommend Weaviate when you want to host the vector database locally in your VPC, or when you require schema-level properties.
What is hybrid search in Weaviate? expand_more
It combines dense vector search with sparse keyword search (BM25), allowing you to adjust weights to optimize relevance.
How do you secure Weaviate databases? expand_more
We configure API key authentication, enable TLS certificates, and host Weaviate behind private load balancers inside your VPC.
What vectorizers does Weaviate support? expand_more
It integrates with OpenAI, Cohere, Hugging Face transformers, or allows you to upload custom pre-computed vectors.
How do you query Weaviate? expand_more
We query Weaviate using GraphQL statements via the Weaviate SDK, which allows you to retrieve data objects and vector properties in a single call.

Accelerate Your AI Project with Weaviate

Schedule an architectural blueprint review session with our senior Weaviate engineers to map your database, compliance, and MLOps strategies.

Schedule Architecture Session