RAG Systems
Vector search and document intelligence pipelines.
Building an AI proof-of-concept takes days. Building one that handles production traffic reliably, costs predictably, and can be debugged when something goes wrong - that's engineering. We've done the latter.

From intelligent retrieval and agent workflows to monitoring and optimization, we engineer AI systems that perform reliably in real-world production environments.
Vector search and document intelligence pipelines.
AI capabilities embedded into existing products.
Coordinated agents with tools, memory, and workflows.
Domain-specific models optimized for specialized tasks.
Training, evaluation, deployment, and model operations.
Monitoring performance, costs, and production behavior.
Versioning, testing, and optimization of prompts.
Caching, routing, and inference cost reduction.
Every recommendation is grounded in business impact and feasibility. We do not start with technology, products, or predetermined solutions.
Ground AI responses with RAG, fact-checking chains, structured output enforcement, and confidence thresholding.
Reduce response times with streaming, parallel tool calls, caching, and asynchronous processing.
Control spend through token tracking, request caching, intelligent model routing, and per-feature cost dashboards.
Protect AI systems with prompt-injection defenses, PII detection and scrubbing, output filtering, and data-residency controls.
Every production AI system gets traces, spans, metrics, and monitoring so you know what's happening — and when performance degrades.
Whether you're adding AI to an existing product or building a new AI-native platform, we'll help you move from prototype to a system your users and engineering team can depend on.