LLMOps: industrializing generative AI in production
Move from POC to production with proven LLMOps practices: testing, security, monitoring and continuous improvement.
Book a 30-min callRéponse courte
LLMOps brings together the practices for taking an AI assistant from POC to production: regression testing, anti prompt injection security, quality/cost/latency monitoring and continuous improvement. It's the difference between a prototype and a reliable 24/7 system.
In 30 seconds
LLMOps brings together the practices needed to take an AI assistant into production: regression testing, security (prompt injection, data leaks), supervision, quality/cost/latency monitoring, and continuous improvement through evaluation and logs. It's the difference between an impressive POC and a reliable system that runs 24/7.
Typical problems
The signs that this solution is right for you.
The POC works, but not in production
90% of AI POCs never make it to production. The gap between "it works on my machine" and "it runs 24/7 for 500 users" is huge.
No testing or monitoring
Your AI assistant works... but you don't know when it makes mistakes, how much it costs or whether it drifts over time.
Uncontrolled costs
LLM API calls are expensive at scale. Without monitoring, the bill explodes without anyone noticing.
Untreated vulnerabilities
Prompt injection, data extraction, bypassing guardrails — an assistant in production is an attack surface that must be secured.
Our approach
A proven method, in clear steps.
Audit of the existing setup
We assess your existing POC or assistant: architecture, answer quality, costs, security, functional coverage.
Testing pipeline
We set up automated regression tests: reference test sets, systematic evaluation on every update.
Security and guardrails
We secure the assistant: input filtering, prompt injection detection, output control, exhaustive logging.
Monitoring in production
We deploy a real-time dashboard: answer quality, latency, cost per request, usage rate, drift alerts.
Continuous improvement
We analyze the logs, identify the weak points and continuously improve the prompts, sources and guardrails.
What you get
- Regression testing pipeline
- Anti prompt injection hardening
- Monitoring dashboard (quality, costs, latency)
- Alerting and drift system
- LLMOps documentation for the team
- Continuous improvement plan
The gap between POC and production
An AI POC shows that "it's possible". Production shows that "it lasts over time". Between the two: systematic testing, security, monitoring, cost management and a continuous improvement process. That's what we call LLMOps.
The 4 pillars of LLMOps
Testing: reference test sets, automatic evaluation, CI/CD. Security: input filtering, attack detection, output control. Monitoring: quality, latency, costs, usage. Improvement: log analysis, prompt optimization, source updates. Without these 4 pillars, an assistant in production is a time bomb.
Related offers
The Digit-AI services that complement this solution.
Related solutions
Enterprise RAG: making AI answer with your sources
Give AI access to your internal documents for sourced, traceable and reliable answers — without hallucinations.
Enterprise AI agents: from assistant to autonomous system
Understand what an AI agent is, when to use it, how to architect it and deploy it in production — with guardrails.
Articles on this topic
Dig deeper with our detailed analyses.
Claude Mythos Preview: Anthropic builds an AI too powerful to be made public
Anthropic unveils Claude Mythos Preview, a model able to detect previously unknown security flaws. But it is reserved for a club of 40 tech giants. What this means for SMB cybersecurity and unequal access to the best technologies.
Read the article
'Go production' checklist for an AI assistant
Before deploying your AI assistant to production, check these 25 points: answer quality, security, monitoring, compliance, scalability. The complete checklist for a calm and controlled move to production.
Read the article
The AI Act comes into force: what changes for businesses
The European AI regulation (AI Act) comes into force. Which systems are affected, which obligations apply and how to prepare right now.
Read the article