AI Integration
Production AI is harder than the demo. We know because we've shipped it.
We build AI-powered features that actually ship. From LLM integration to full RAG pipelines, we handle the hard parts — latency, cost, guardrails, monitoring — so you can focus on your product.
Services
LLM Integration
Connect any model to your product. Prompt engineering, output parsing, streaming responses, and fallback chains.
RAG Pipeline Setup
Document ingestion, chunking, embedding, vector search, and retrieval-augmented generation — end to end.
AI Agent Development
Autonomous agents with tool use, memory, planning, and multi-step reasoning. Built for reliability at scale.
Fine-Tuning
Custom model fine-tuning on your data. Dataset curation, training, evaluation, and deployment.
Evaluation
Systematic evaluation frameworks to measure accuracy, latency, cost, and safety. Automated regression testing for AI.
Models supported
All through ee.ai — one interface, any model.
Use cases
AI Chatbots
Context-aware conversational interfaces with memory, tool use, and domain knowledge.
Document Analysis
Extract, classify, and summarize information from PDFs, contracts, reports, and manuals.
Code Generation
Custom code generation tools for your codebase, APIs, and internal frameworks.
Content Moderation
AI-powered content filtering with customizable policies, appeals, and audit logging.
Recommendation Engines
Personalized recommendations powered by embeddings, user behavior, and collaborative filtering.
Complete RAG pipeline in 15 lines
import { ee } from "@vertexstudio/sdk";
// 1. Ingest documents
await ee.ai.ingest({
source: "s3://docs-bucket/manuals/",
chunking: "semantic",
embedModel: "text-embedding-3-large",
});
// 2. Query with RAG
const answer = await ee.ai.generate({
model: "claude-sonnet-4-20250514",
prompt: query,
rag: { collection: "manuals", topK: 5 },
guardrails: ["pii-filter", "hallucination-check"],
});Production concerns
Latency
Streaming responses, edge caching, model routing, and prompt optimization to hit your latency targets.
Cost Optimization
Model selection, prompt compression, caching, and batching to minimize per-request costs.
Guardrails
Input validation, output filtering, PII detection, and content safety checks on every request.
Monitoring
Token usage tracking, latency percentiles, error rates, and quality scores in real time.
Fallbacks
Automatic model fallbacks, retry logic, and graceful degradation when primary models are unavailable.
Case Study
Nova shipped an AI SaaS product in 6 weeks
Nova needed to launch an AI-powered document analysis platform fast. Using our AI integration service, they went from concept to production in 6 weeks — complete with RAG pipelines, guardrails, usage-based billing, and multi-model fallbacks. The product now processes thousands of documents daily.