AI that actually works
RAG pipelines that retrieve the right context. Multi-agent systems that coordinate like a well-oiled team. Fine-tuned models that don't hallucinate (most of the time). We build AI that does real work — not chatbot demos that fall apart outside a sandbox.
Custom AI & LLM Solutions
What we build with AI
Four areas where we've shipped production-grade AI systems.
RAG Pipelines
Retrieval-Augmented Generation systems that find and surface the right information from your documents, databases, and knowledge bases — with citations you can verify.
Multi-Agent Systems
Coordinated AI agents that work together — one plans, one executes, one verifies. Like a team of specialists, not a single generalist.
Fine-Tuned Models
Custom-trained models on your data. Better accuracy, lower latency, and full control — without the generic responses of off-the-shelf models.
Real-Time Inference
Low-latency AI that runs in production — processing thousands of requests with sub-200ms response times. Fast enough for fraud detection, search, and chat.
Our AI tech stack
The tools we actually reach for — not every library we've ever installed.
How we build AI
Our AI-specific process — from data to deployment.
Data Discovery
We find and understand your data — structure, quality, and what it actually means for your use case.
Model Selection
Choose the right model — off-the-shelf, fine-tuned, or custom. We test and validate before committing.
RAG / Agent Design
Architect the retrieval pipeline or agent coordination — the "brain" of your AI system.
Deploy & Monitor
Production-grade deployment with monitoring, logging, and continuous improvement.
Multi-agent fraud detection system
A regional fintech platform was drowning in transaction reviews. Manual review couldn't keep pace with volume, and they were losing legitimate business while flagging false positives.
We built a RAG-powered triage system with LangChain, Pinecone vector search, and real-time API integration — processing 5,000+ transactions/hour with sub-200ms latency.
Fintech · 12 week build
LangChain · Pinecone · FastAPI · React
Frequently asked questions
Quick answers to common questions about AI development.
RAG retrieves relevant context from your data at query time — it's like giving the model a search engine. Fine-tuning trains the model on your specific data — it's like teaching the model your domain. We use both, depending on your needs.
We use enterprise-grade encryption, access controls, and we never train on your data without explicit permission. We can also deploy models in your own VPC if required.
It depends on complexity. A simple RAG pipeline can take 4-6 weeks. Multi-agent systems or custom fine-tuning typically take 8-12 weeks. We'll give you a realistic timeline during discovery.
Yes, we work with your data. We can help you structure, clean, and prepare it for AI. If you don't have data yet, we can help you figure out what to collect and how.
Ready to build an AI that actually works?
Let's talk about your data, your use case, and what's actually achievable.