AI that ships, not slides

We build LLM products, custom agents, and predictive models that move metrics in production. No prompt-engineering demos — real systems with evals, fallbacks, and ROI.

35+
AI Features Shipped
94%
Avg. Accuracy
<800ms
P50 Latency

What we build with AI

AI/01

LLM-Powered Apps

Custom copilots, chatbots, and assistants tuned for your domain.

AI/02

RAG Systems

Knowledge bases over your docs. Hybrid search, re-ranking, citations.

AI/03

Custom Agents

Tool-using agents that automate multi-step workflows.

AI/04

Fine-tuning & Evals

Fine-tuned models with rigorous eval suites. We measure what matters.

AI/05

Computer Vision

Object detection, OCR, image classification, video analysis.

AI/06

Predictive ML

Churn, demand, scoring, recommendations — classical ML where it wins.

From prompt to production

01

Use case fit

What problem? What data? What's the win? Honest answers before code.

02

Eval-first

Define success metrics. Build the eval before the model.

03

Iterate

Try simple → complex. Prompt → RAG → fine-tune. Stop when it works.

04

Ship & monitor

Observability, fallbacks, cost controls, drift detection.

Models & tools

🧠Claude
🤖OpenAI
🦙Llama
Gemini
🔗LangChain
🐍Python
🌶Pinecone
📡Weaviate
🔥PyTorch
📊HuggingFace
Modal
📈Weights&Biases

Common questions

Both, often. Default to frontier APIs (Claude/OpenAI) for quality. Use open-source (Llama, Mistral) when data residency, cost, or fine-tuning needs demand it.
Grounding via RAG, citation requirements, structured outputs, guardrails (LLM-as-judge), and human-in-the-loop for high-stakes decisions.
SOC 2 / HIPAA / GDPR aware by default. Options: zero-data-retention API tiers, on-prem inference, or private cloud deploys.

AI for your domain

Tell us your use case. We'll come back with a feasibility memo, a prototype scope, and a price.