Agnostic builds and manages enterprise‑grade data platforms centered on high‑performance ClickHouse clusters, delivering real‑time streaming ingestion, low‑latency analytics, and integrated AI pipelines for large‑scale event data. Their end‑to‑end service includes architecture design, zero‑downtime migration, continuous performance monitoring, and cost optimization, enabling data‑driven enterprises to run analytics dashboards, ML feature stores, and AI assistants on fresh, governed data.
Funding
$2.2M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Enterprises with large‑scale event streams often struggle to achieve sub‑second query latency, maintain low operational costs, and integrate real‑time data with machine‑learning or large‑language‑model workloads. Existing on‑prem or cloud solutions can be complex to provision, migrate, and operate at the required scale.
Solution
Agnostic delivers end‑to‑end data and AI platforms built around high‑performance ClickHouse clusters. The company assesses the client’s data landscape, designs a cloud‑optimized or hybrid architecture, and implements zero‑downtime migrations. Real‑time pipelines ingest data via Kafka, apply custom SQL and user‑defined functions, and ensure data quality and validation. Integrated AI pipelines prepare LLM‑ready datasets, generate embeddings, and log model inference, while semantic search and retrieval‑augmented generation (RAG) layers provide hybrid vector‑plus‑keyword search. Ongoing monitoring, cost optimization, and iterative tuning keep systems performant and affordable, enabling businesses to run analytics dashboards, ML feature stores, and AI assistants on fresh, governed data.
Target Audience
Primary customers are data‑driven enterprises—such as SaaS providers, e‑commerce platforms, ad‑tech firms, and organizations building AI‑powered assistants—that require real‑time analytics, low‑latency query performance, and seamless integration of data with machine‑learning pipelines.
Features
- Design and deployment of scalable ClickHouse clusters with multi‑region replication, backup, and security controls
- Real‑time streaming ingestion and ETL/ELT pipelines using Kafka, custom SQL, and UDFs
- Automated data validation, quality checks, and zero‑downtime migration from legacy systems
- LLM‑ready data preparation, embedding generation, and feature pipeline integration for production AI models
- Hybrid semantic search combining vector indexes and BM25 for retrieval‑augmented generation
- Continuous performance monitoring, query tuning, and cost‑optimization services
- Managed operations including SLA verification, disaster recovery, and iterative evolution of the data platform