The real problem
The gap between an AI demo and a production AI feature is where most projects stall. LLM latency in a user-facing flow requires careful streaming and timeout handling. RAG systems that work in evaluation fail in production because the chunking strategy doesn't match query patterns. Fine-tuned models degrade without a feedback loop and retraining pipeline. We build AI features as engineering problems, not research projects. That means cost per query tracked from day one, fallback logic for model outages, prompt version control, and evaluation pipelines that let you know when things regress. We work with companies integrating LLMs into existing products, building custom ML models on proprietary data, and launching AI-native applications from scratch.
What we build
OpenAI, Anthropic, and open-source model integrations with streaming, function calling, structured output, cost tracking, and fallback handling. Production-ready, not prototype-grade.
Retrieval-augmented generation pipelines with vector databases (Pinecone, Weaviate, pgvector), document ingestion, chunking strategies, and relevance evaluation.
Domain-specific models trained on your data. Classification, regression, NLP, and computer vision models with training pipelines, versioning, and production deployment.
Agentic workflows with tool use, multi-step reasoning, and human-in-the-loop checkpoints. Document processing, data extraction, and decision-support systems.
Training pipelines, model registries, A/B testing for model variants, monitoring for model drift, and data labelling infrastructure for continuous improvement.
Technology