AI-Powered Engineering
We build retrieval-augmented systems, evaluator pipelines, model orchestration, and production AI workflows that operate reliably, safely, and cost-effectively — not prototypes, but real systems shipped to production.
Building AI Systems That Perform in Production
Codexium engineers design AI systems that survive real-world load: evaluation pipelines, grounding, structured prompting, context strategies, and orchestration patterns that keep behavior predictable and controllable.
We specialize in building retrieval-augmented generation (RAG) systems, domain-specific assistants, and multi-step workflows that integrate into your internal tools, APIs, and operational processes.
RAG + Retrieval
Hybrid search, vector stores, chunking strategies, evaluation sets, grounding and context windows optimized for your domain.
Model Orchestration
Workflow engines, tool-use patterns, structured actions, parallel agents, and deterministic execution paths.
Evaluation & Guardrails
Human preference modeling, rubric-scored evaluations, test harnesses, safety filters, and continuous monitoring.
What we typically build in an AI engineering engagement
- End-to-end RAG systems with robust retrieval strategies
- Model evaluator pipelines for scoring and behavior validation
- Multi-agent and tool-use workflows with clear state transitions
- Secure API integration with internal and external systems
- LLM observability: traces, metrics, and prompt evaluation logs
- AI lifecycle automation: dataset building, tests, regression checks
Why AI-Focused Engineering Requires Senior Infrastructure Thinking
AI systems fail when orchestration, evaluation, data pipelines, and guardrails are treated as afterthoughts. We design for reliability from the start — safe defaults, deterministic fallbacks, and clear error boundaries.
When Codexium is the right AI partner
- Your team needs production-grade AI workflows, not demos
- You want RAG systems that actually perform under load
- You need orchestration beyond “single-prompt” apps
- You require safety, evaluators, guardrails, and logging
Operational Safety
Deterministic flows, safe fallback paths, audit logs, error thresholds, and rate-limit protection.
Data Pipelines
Embedding pipelines, chunking, vector stores, hybrid retrieval, and scheduled dataset refresh cycles.
Cost Optimization
Token monitoring, caching layers, distillation strategies, and compute-efficient architectures.