Custom LLM agents, RAG pipelines, and machine learning systems engineered for production load. Most 'AI features' never make it past a prototype. 4xCode's AI development services are built specifically for production: systems that hold up under real traffic, real edge cases, and real user expectations. We design custom LLM agents that reason through multi-step tasks, retrieval-augmented generation (RAG) pipelines that ground responses in your actual data, and fine-tuned models tailored to your domain — all engineered with the same rigor we bring to traditional software, because an AI feature that breaks in production is worse than no AI feature at all.
Foundation models and function-calling agent orchestration.
Custom model training and fine-tuning workflows.
Chaining retrieval, tools, and multi-step agent logic.
Vector storage and low-latency semantic search at scale.
Core Philosophy
We build deterministic pipelines around LLMs instead of relying on fragile prompt chains, so behavior stays predictable as you scale.
Every agent ships with evaluation and monitoring, so you know when output quality drifts — before your users do.
Our AI work is integrated with full-stack engineering, so the AI feature actually connects cleanly to your existing product instead of living in a separate sandbox.
Capability Matrix
Multi-step reasoning agents that can call tools, query APIs, and complete real workflows, not just answer single-turn questions.
Retrieval systems that connect your LLM to your actual knowledge base, documents, or product data, with chunking and re-ranking strategies tuned for accuracy.
Model fine-tuning for domain-specific tone, accuracy, or task performance when prompting alone isn't precise enough.
Production-grade vector search infrastructure for semantic search, recommendations, and retrieval at scale.
Engineering Stack
Operational Delivery Flow
Parsing unformatted file structures and setting deterministic structural embedding pipelines.
Configuring pipeline prompts and local guardrails to compress latency and token usage.
Setting strict boundary rules, secure API abstractions, and real-world system analytics.
Technical Validation
"We build deterministic pipelines around LLMs instead of relying on fragile prompt chains, so behavior stays predictable as you scale."
"Every agent ships with evaluation and monitoring, so you know when output quality drifts — before your users do."
"Our AI work is integrated with full-stack engineering, so the AI feature actually connects cleanly to your existing product instead of living in a separate sandbox."
Adjacent Capabilities
Tell us the goal. We'll tell you the path.