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AIBound Tech Stack

Control plane for securing and deploying LLM applications

Computer and Network Security San Francisco, CA 11–50 employees Privately Held

AIBound is building infrastructure for safe AI deployment, with a tech stack centered on LLM serving (vLLM, FastAPI, GCP), vector databases (Pinecone, Weaviate, Milvus), and orchestration (Kubernetes, Airflow, Dagster). The project list—adversarial testing, red-teaming, security scanning, RAG pipelines—reveals a company focused on the gap between LLM capability and production safety. Pain points cluster around model reliability, adversarial robustness, and cost control, suggesting AIBound is solving the operational brittleness that blocks enterprise AI adoption.

Tech Stack 25 technologies

Core StackPython TensorFlow PyTorch LangChain Pinecone Weaviate Kubernetes Docker FastAPI Apache Airflow Dagster Kafka RAG React TypeScript JavaScript Milvus FAISS GCP vLLM IAM SQL Pub/Sub Beam Styled Components

What AIBound Is Building

◆Challenges

Ai security gaps
Adversarial attacks on ai models
Ensuring ai system reliability
Cost-efficient llm serving
Secure ai workflows
Cost-controlled ai services
High-performance serving
Safe rollout strategies
High-integrity data flows
Reliable llm pipelines

▲Active Projects

High-performance llm serving with vllm
Ai security scanning platform
Adversarial testing framework
Ai red-teaming program
Rag pipeline design and scaling
Llm inference service deployment on gcp
Llm inference services production
Llm and agentic data backbone
End-to-end rag data pipelines
Feedback loops for retrieval improvement

Hiring Activity

Accelerating5 roles · 4 in 30d

Department

Engineering

4

Data

1

Seniority

Senior

2

Junior

1

Lead

1

Mid

1

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About AIBound

AIBound is a San Francisco-based security and infrastructure startup building a control plane for AI applications. The company serves engineering teams deploying large language models and agentic systems in production, focusing on three core problems: securing LLM inference against adversarial attacks, ensuring reliable and cost-efficient serving at scale, and maintaining data integrity across retrieval-augmented generation (RAG) pipelines. The 11–50-person team is engineering-focused, currently hiring senior and mid-level roles across infrastructure and data, with development activity in India.

HeadquartersSan Francisco, CA

Company Size11–50 employees

Hiring MarketsIndia

Frequently Asked Questions

What is AIBound's tech stack?

Python, TensorFlow, PyTorch, LangChain, and vector databases (Pinecone, Weaviate, Milvus, FAISS). Infrastructure: GCP, Kubernetes, vLLM, FastAPI. Data pipeline: Airflow, Dagster, Kafka, Pub/Sub, Beam. Frontend: React, TypeScript.

What is AIBound working on?

LLM inference serving optimization, adversarial testing and red-teaming frameworks, AI security scanning, RAG pipeline design and scaling, and production deployment of LLM and agentic systems on GCP.