Data for the AI Era
Clean, labeled, and structured data is a prerequisite for Generative AI. We excel in transforming unstructured data into meaningful datasets that power LLM applications and unlock immediate value.
Ingest data and transform inputs for use in LLMs and other AI tools.
Everything you need to prep for AI projects
End-to-end data enablement
We don't just clean data—we build production-ready infrastructure. From unstructured documents to vector embeddings, we prepare your data for LLM applications so you can focus on building value, not wrestling with data formats.
- Data Assessment
- Comprehensive audit of your existing data to identify AI-ready assets and prioritize high-impact use cases for Gen-AI applications.
- Data Structuring
- Transform PDFs, logs, and documents into structured formats using OCR, NLP, and semantic parsing for AI consumption.
- Labeling Workflows
- Build scalable labeling pipelines with quality control, ensuring training data meets the standards required for accurate AI models.
- Embedding Pipelines
- Deploy vector databases (Pinecone, Weaviate, pgvector) with automated embedding generation for semantic search and retrieval.
- RAG Architecture
- Build retrieval-augmented generation systems that ground LLM responses in your proprietary data for accurate, context-aware answers.
- LLM Integration
- Connect your data pipelines to OpenAI, Anthropic, or open-source models with proper prompt engineering and guardrails.
Who This Helps
Transform operations with AI-powered workflows
Scale-ups
Deploy AI-powered workflows that scale operations without scaling headcount. Automate processes while maintaining quality and compliance.
Enterprise
Embed AI into existing workflows at scale. Get data governance and security controls that pass enterprise compliance requirements.
Unblock your AI goals
Let's assess your data landscape and build an AI-ready foundation that enables immediate experimentation with measurable results.