Build AI that actually works.
From multi-agent orchestration to AI security testing — comprehensive services built with LangChain, LangGraph, and modern AI frameworks. We scope in a week, ship in weeks after that, and hand off with runbooks.
Capabilities on this page: multi-agent systems with tool use and shared memory; retrieval-augmented generation and enterprise search; LLM evaluation, hallucination checks, and prompt regression; AI security as a red team and a blue team; observability for prompts, cost, and latency; process automation; custom development from fine-tuning to MLOps; AI HRMS for attendance and payroll; and AI facilities workflows for day-one onboarding.
The delivery rhythm is discovery in week one, architecture in week two, build across weeks three to eight, and production deployment with monitoring and guardrails around weeks eight to ten. You see a working system in days, not a quarterly reveal.
Platforms we implement on include OpenAI, Claude, Vertex AI, AWS Bedrock, and Azure OpenAI, with Pinecone, Weaviate, and Chroma for retrieval, and LangSmith for traces. Security tooling includes Model Armor, Bedrock Guardrails, NeMo Guardrails, and LLM Guard.
Published operating figures for this practice: 90% fewer AI incidents in production on scoped programmes, 5× faster issue detection, 60% lower validation cost, and 2× faster delivery. These are the figures printed on the AI services page, not a new claim. Pair the build with MrProbe before anything customer-facing ships.
- AI red teaming services
- AI governance and EU AI Act readiness
- Enterprise AI, data, and cloud
- MrProbe — AI agent red teaming platform
- Start an AI engineering project
Frequently asked questions
What is included in QDEX AI engineering services?
Multi-agent systems, retrieval-augmented generation, AI testing, red and blue team security, observability, process automation, custom model development, and applied systems such as AI HRMS and facilities workflows. We scope in a week, build in weeks, and hand off with runbooks.
Which models and platforms do you implement on?
LangChain, LangGraph, OpenAI, Claude, Vertex AI, AWS Bedrock, Azure OpenAI, and Hugging Face, with vector stores such as Pinecone, Weaviate, and Chroma. Guardrails include Model Armor, Bedrock Guardrails, NeMo Guardrails, and LLM Guard where the architecture needs them.
How do you keep a custom AI system from shipping unsafe?
Security testing is part of the build, not a slide at the end. We red-team agents with MrProbe — 648+ techniques across 17 categories — and validate behavior on golden datasets before production. Runtime guardrails are deployed with the system.
Can you work alongside our team?
Yes. Discovery needs your subject-matter experts. After that, QDEX builds and you review on a weekly cadence. We also staff dedicated engineers when you want the capability to stay in-house after handoff.