No evaluation, no truth
Without a test set, “it seems to work” is the only metric. A regression is discovered the day a customer reports it.
AI systems built
to last in production.
We design artificial intelligence systems to production standards: evaluated, observed, maintainable. Diagnostics, engineering, training: every engagement starts with a measurement and ends with a handover.
EvaluationObservabilityCostsSecurityScalability
The problem
A demo is not a product.
According to MIT, 95% of enterprise AI pilots deliver no measurable return. The cause is rarely technical. What is missing is method.
No observability, no control
What is not traced is endured: latency, drift, costs. The bill arrives at month end, unexplained.
No handover, no longevity
A system only one person understands disappears with them. The knowledge has to live in your teams.
Our services
Three services, one goal: production.
Train your teams, measure what you have, take one use case to production. Every engagement is scoped, priced, and stands on its own.
01Training
Get everyone to the same level.
Three in-house modules built on real production cases. Decision-makers and technical teams leave with the same vocabulary and the same reflexes.
- Decision-makers · half a day
- Building AI systems · one day
- Evaluating and observing · one day
02Diagnostic
Measure before you invest.
Five days to read what exists, build an evaluation baseline and cost out what production will require. A prioritised plan, not an impression.
- Inventory and review of the real code
- A reproducible evaluation baseline
- A costed roadmap
03Production delivery
One use case, all the way.
Fifteen to twenty days to take a chosen use case to production: evaluations, traces, cost tracking, documentation. Your teams take it over.
- The system, in your repository
- Evaluations wired into your CI
- A documented handover
Our method
Understand, measure, deliver.
Our three services follow this order. Each is useful on its own; together they form the shortest path to a system that lasts.
01 · Understand
Training
One day to share an honest definition of what AI can do, what it costs and where it fails. The decisions that follow are made on facts.
02 · Measure
Diagnostic
Five days to read what actually runs, build an evaluation set and cost out what production will require. Then invest where the return is demonstrable.
03 · Deliver
Production delivery
Fifteen to twenty days to take a use case to production: evaluations, traces, cost tracking, documentation. The system runs, your teams take it over.
The team
Meet the co-founders.
The people who scope the work are the people who build it. What the meeting promises, the implementation keeps.
Naji Najari, Ph.D.
Co-founder
Naji brings more than eight years of AI experience to the systems Mélops delivers. A PhD in machine learning with four international publications (ECML-PKDD, IEEE) and two patents, he did his research at Orange before leading a data team at Forvia, the world's fourth-largest automotive supplier. Today at Brevo he builds multi-agent assistants in production: RAG, LangGraph and Google ADK orchestration, evaluation, observability. His dual background, research and production, guarantees rigorous systems that hold up at scale.
Adham Azenbar
Co-founder
Adham brings five years of experience in data and generative AI, from Société Générale and M6 to Brevo's AI Lab, where he designs knowledge-management agents, MCP servers and evaluation frameworks for agentic systems. He built RagMaker end to end, an agentic SaaS platform for querying documents, and shipped generative AI applications for insurance and telecoms. That product culture, from backend to deployment, guarantees that what Mélops designs gets shipped and stays maintained.
Let's talk
Let's start with a conversation.
Describe your situation in a few lines. We will tell you frankly whether we can help, and how. A reply within one business day, first call with no commitment.