LLM applications, RAG systems, AI agents, and computer vision pipelines. Provider-agnostic, production-grade, and cost-aware from the first sprint.
Where it fits in our builds
The model is one box in a bigger system. User input and product data feed LLM features wrapped in guardrails, prompts, and evals, and the whole loop ships as one production release.
Our stance
We don't bet on a single LLM provider; we build provider-agnostic architectures with routing and fallbacks. Our focus is application-layer engineering, not custom model training. We've shipped production AI at scale, where caching, cost, latency, and evals matter as much as model choice. And we treat AI like any other dependency: monitored, versioned, and fail-safed.
Capabilities
OpenAI, Anthropic, Google Vertex AI, AWS Bedrock. Provider-agnostic, with routing and fallbacks.
Vector DBs (Pinecone, Weaviate, pgvector, Qdrant), embedding pipelines, and retrieval orchestration.
Multi-agent systems, tool use, state management, and observability.
Object detection, OCR, image classification, and real-time inference pipelines.
Model serving, GPU optimization, cost monitoring, caching strategies, and latency optimization.
LLM evals, test harnesses, output monitoring, drift detection, and safety filters.
Infrastructure that holds up when traffic arrives
Tooling
Hard lines
Saying no to these is part of the service. Each one is a production incident we would rather not relive.
A provider changes pricing or breaks an API. We build with routing from day 1.
Prompts are code. Evals are tests. Untested prompts break silently.
Hallucinations don't help users. Citations and confidence scores are required.
99% of funded startups don't need custom training. We'll tell you when you do.
Sometimes a rule, regex, or classic ML model is the right answer. AI is a tool, not a religion.
Ad platforms, outbound engines, AEO tools, and content engines.
Underwriting, risk, fraud, and document-intelligence pipelines.
Clinical assistants, triage, and medical-document workflows.
We built a minimum viable product to connect disparate advertising APIs into a single, intuitive user interface.
We built the technical foundation for a SaaS platform that automates complex legal document creation with usage-based pricing system
We engineered a voice-first AI companion with a therapeutic framework to validate a new model for mental health support.
Need to extend your team with engineers who have shipped production AI? We embed senior AI/ML engineers into your team: provider-agnostic, eval-driven, and cost-aware from day one.
Production engineering
We pick tools because they ship, not because they trend. Every technology we list is one we have deployed to production, debugged under load, and would choose again for the right problem.
Straight answers on providers, training, hallucinations, and cost control.
We are provider-agnostic. We build with routing and fallbacks across OpenAI, Anthropic, Google Vertex AI, and AWS Bedrock, and pick per workload based on cost, latency, and capability. Never locked to one vendor.
No account managers. No discovery theatre. A direct conversation about your AI stack and what it takes to ship it to production.