AI Agents For Your Products & Processes
From architecture to deployment, we design and implement secure AI agents that you own and control. We help you integrate agents into your product and operations, connect them to your domain-specific data and workflows, and improve them through evaluation, feedback loops, and post-training methods.
Case studies
Crossfader
End-to-end RAG for an online DJ school
We helped Crossfader transform its extensive library of DJ courses and articles into an AI-powered semantic search and learning assistant.
We designed and deployed an end-to-end RAG system using Qdrant, OpenAI, FastAPI, and AWS, enabling users to receive fast, context-aware answers grounded in Crossfader’s proprietary educational content.
Simplica
AI Agent integration into an existing production software framework
We designed and implemented Simplica’s agentic AI foundation, including a multi-agent architecture, evaluation framework, guardrails, fine-tuning pipelines, and experimentation infrastructure.
The system combines OpenAI agents SDK, custom evaluators, structured prompt orchestration, and tool-based agents to enable creation and execution of application within this framework.
Siemens Energy
Agentic Order Validation System
We designed and implemented a multi-agent order validation system for Siemens Energy that automated the verification and traceability of high-value industrial orders. Processes previously handled through lengthy manual reviews could now be validated before execution with greater speed, consistency, and reliability.
Our Services
Our solutions for your specific needs
AI Agents, Built for Your Product & Processes
We design and build secure, company-owned AI agents that work inside your real systems — not generic models. They can use your tools, retrieve your data, follow your workflows, and support product or operational tasks with human oversight where needed.
From architecture and integration to evaluation, post-training, deployment, and ongoing improvement, Calibrion helps you turn domain-specific workflows into reliable agentic systems.
Model Post-training
Off-the-shelf models rarely perform well enough on specialized business tasks without adaptation. Post-training helps align a model with your domain, product behavior, terminology, quality standards, and user expectations.
Calibrion helps you improve LLM performance through high-quality training data, supervised fine-tuning, preference data, evaluation sets, feedback loops, and systematic experiments. We help you decide when post-training is worth it, which model to use, what data to collect, and how to measure whether the result is actually better.
RAG Systems That Ground AI in Your Data
RAG (Retrieval Augmented Generation) enables AI answer from your actual content (your docs, content, tickets, codebase, product data, etc.) instead of guessing. It cuts hallucinations and keeps answers current as your data changes.
But implementing RAG effectively is often challenging, especially with fragmented knowledge sources, weak retrieval quality, poor chunking, missing metadata, and infrastructure that is not set up for reliable search. At Calibrion, we design and implement RAG solutions tailored to your product, data, and infrastructure, using the right architecture, databases, and retrieval methods for durable performance.
Custom Evaluators for Reliable Agents
One of the biggest bottlenecks in putting AI agents into production is knowing whether they're actually doing a good job. Without a clear way to measure quality, teams end up shipping agents they can't fully trust, catching problems only after users hit them, and making changes without knowing if things are getting better or worse. That's where evaluation comes in.
Calibrion designs and implements evaluation systems tailored to your product, domain, and risk profile. Backed by years of applied AI experience and deep expertise in LLMs, ML, and evaluation methods, we build evaluators that help you measure performance with clarity, whether through statistical methods, domain-specific ML evaluators, LLM-as-a-judge, or multi-judge LLM juries.
Synthetic Data Generation
Manual data labeling is slow, expensive, and rarely covers the edge cases your AI actually fails on. Synthetic data generation can close that gap — producing training and evaluation data at scale, including the rare cases real datasets miss.
Calibrion designs synthetic data pipelines for your specific use case, whether you need training data for a new model, eval sets that stress-test edge cases, or privacy-safe data for testing. We pick the right generation method, build in quality controls so the data is actually useful, and integrate the pipeline with your training and evaluation workflows.
Advise & Strategy
Most AI initiatives fail not because the technology doesn't work, but because teams pick the wrong problem, choose the wrong architecture, or build something they can't reliably operate.
Calibrion advises product and engineering leaders from the practitioner's seat not from slides. We help you decide what to build (and what not to), choose the right architecture and stack, scope realistic timelines, and avoid the failure modes we've seen kill AI projects. Engagements range from a focused opportunity audit to ongoing technical advisory through implementation.