Titan Tech Stack

Banking-native AI platform with compliance-grade security and domain models

Software Development New York, NY 11–50 employees Founded 2025 Privately Held

Titan is a 11–50 person AI platform built specifically for banking, FinTech, and credit unions. The stack is heavily ML-forward—Python, PyTorch, TensorFlow, Hugging Face, and vector databases (Pinecone, Weaviate, Milvus, FAISS)—paired with RAG infrastructure (LangChain, LlamaIndex, Haystack) and backend services on FastAPI/Django. The founding team has deep banking operations and regulatory compliance experience, and their project roadmap centers on domain-specific banking models and agents rather than generic LLM wrapping, suggesting they're solving the core tension in financial services: how to adopt AI without violating compliance or security boundaries.

Tech Stack 34 technologies

Core StackPython PyTorch TensorFlow Pinecone Weaviate LangChain Neo4j FastAPI Django Flask TypeScript React PostgreSQL MySQL Tailwind CSS AWS Docker Kubernetes Hugging Face Transformers SQL Milvus Vespa FAISS LlamaIndex Haystack ArangoDB Radix UI Zustand React Router Azure+4 more

What Titan Is Building

◆Challenges

Lack of secure ai tools for banking
Unsafe ai in banking
Ai tools purpose-built for banking
Banks lack purpose-built ai tools
Banks cannot safely use bleeding edge ai tools

▲Active Projects

Titan foundry
Banking-reasoning models
Banking agents
Client-specific rag pipelines
Retrieval backbone for autonomous banking
Ai application development for banks
Backend services and apis for ai platform
Web/mobile client interfaces for ai platform
Ai agent framework design
Multi-hop reasoning implementation

Hiring Activity

Decelerating7 roles · 1 in 30d

Department

Engineering

Sales

Data

Seniority

Senior

Mid

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About Titan

Titan provides an AI platform purpose-built for the banking and financial-services sector. The platform offers three main capabilities: secure access to multiple foundational models with explainability tooling and bank-grade security; banking-specific reasoning models trained to emulate bank operators and regulatory thinking; and function-specific AI agents for critical workflows like lending and payments. They operate across banks, FinTechs, and credit unions. Their tech stack reflects the complexity of the domain: vector stores and RAG pipelines for retrieval-augmented generation on financial data, graph databases (Neo4j, ArangoDB) for relationship reasoning, and infrastructure automation on AWS, Azure, Docker, and Kubernetes.

HeadquartersNew York, NY

Company Size11–50 employees

Founded2025

Hiring MarketsUnited States

Frequently Asked Questions

What AI and ML technologies does Titan use?

Titan's stack includes PyTorch, TensorFlow, and Hugging Face Transformers for model training; vector databases (Pinecone, Weaviate, Milvus, FAISS) for embeddings; and RAG frameworks (LangChain, LlamaIndex, Haystack). Backend services run on FastAPI/Django with PostgreSQL and MySQL for persistence.

What is Titan's core product focus?

Titan is building banking agents, banking-reasoning models, client-specific RAG pipelines, and autonomous banking infrastructure. Projects emphasize domain-specific AI agents and multi-hop reasoning designed for critical financial workflows rather than generic AI tooling.