Elhadji Ngom, founder and Chief AI Architect of Omicron AI Labs, is an agentic AI consultant based in Paris. We design, industrialise and deploy AI agents, multi-agent systems and RAG architectures in production for mid-market and enterprise teams in France: from use-case audit to operations, with KPIs defined at framing.
A short answer, then the criteria for choosing between a chatbot, automation and an agent.
An AI agent is a system that pursues a goal: it plans, calls tools and APIs, queries your data and acts inside your information systems, with guardrails and human validation on sensitive actions. Agentic AI is the discipline of designing these agents, alone or as multi-agent systems.
An agentic AI consultant's job is to determine where an agent brings measurable value, and where classic automation is enough.
| Criterion | Chatbot | Classic automation | AI agent |
|---|---|---|---|
| What it does | Answers a question | Runs a predefined sequence of steps | Pursues a goal and chooses its own steps |
| Access to systems | Read-only, often limited to a knowledge base | Fixed connectors | Tools, APIs and data through controlled interfaces (MCP, OpenAPI) |
| Handling the unexpected | Rephrases or escalates | Fails outside the planned case | Replans, with human validation on risky actions |
| Best suited when | The need is to get information | The process is stable and deterministic | The process varies, draws on several sources or tools and requires judgement |
From use-case framing to production operations, with a single senior point of contact.
Identify the processes where an agent brings measurable ROI, assess feasibility (data, systems, security) and define success KPIs.
Design the target architecture: single agent or multi-agent, RAG / GraphRAG, tools exposed through MCP, coordination through A2A, integration with your information system.
Build production-ready agents on AWS Bedrock AgentCore and Strands Agents, exposed through APIs, deployed with CI/CD and continuously observed.
Evaluate agents (DeepEval), test their robustness and bound their actions: scoped access rights, human validation, logging, GDPR and EU AI Act alignment.
Four steps, one concrete deliverable at each.
High-ROI use case identification, feasibility analysis and success KPI definition. Deliverable: audit report and an AI roadmap prioritised over 6–18 months.
Rapid proof of concept with RAG or agents in two to four weeks. Technical assumptions validated with your teams before any large-scale commitment.
Rigorous model evaluation (DeepEval), robustness testing and GDPR / EU AI Act alignment. Production-ready, documented and compliant code before any deployment.
MLOps deployment on AWS or Azure, production monitoring and team training. Scaling with KPIs measured at delivery and lasting skills transfer.
The full method and the firm's complete offer: Omicron AI Labs, agentic AI consulting firm.
Delivered systems, with indicators measured at delivery.
Predictive intelligence for clinical trial oversight. The autonomous agent reads sample-collection metrics, produces 36-month forecasts and automates 13 CRUD operations over OpenAPI — so clinical teams decide on live data.
Biomedical analysis accelerator for the QuartzBio EDP platform. The agent converts natural-language queries into validated SolveBio expressions with automatic verification built in — sharply cutting time-to-insight for data science teams.
Agentic assistant for tier-1 support. −45% resolution time, −20% tier-1 load, with a full audit trail on every answer.
Natural-language questions translated into validated SQL, opening data access to business teams without a technical bottleneck.
All projects: the firm's case studies.
The choice depends on your context: cloud, sovereignty, existing systems, GDPR and EU AI Act requirements.
A single senior point of contact, with the framework of a firm.
Elhadji Ngom is an agentic AI consultant, founder and Chief AI Architect of Omicron AI Labs in Paris. An expert in applied mathematics and artificial intelligence, Elhadji brings over 8 years of Python and machine learning experience and over 3 years of GenAI in production.
Elhadji's assignments have taken place in Paris, Luxembourg and New York, across investment finance, healthcare and pharma, B2B SaaS and the public sector. Every engagement includes the documentation, training and support your teams need to become autonomous.
Answers to the questions most often asked before starting an AI agent project.
An agentic AI consultant is a senior expert who works directly on designing and deploying agents. A firm generally staffs several profiles on an engagement. Omicron AI Labs offers a single senior point of contact, Elhadji Ngom, with the framework of a firm: a four-step method, documented deliverables, KPIs defined at framing and skills transfer.
With a 30-minute framing session to scope the process you want to improve, the data available, security constraints and the expected outcome. Next come the use-case and KPI audit, then a proof of concept in 2 to 4 weeks to validate assumptions before any large-scale deployment.
A proof of concept takes 2 to 4 weeks. Going to production depends on scope: integrations with the information system, security requirements, evaluation and operations. Our Production-First method aims at 8 to 10 weeks to turn a validated MVP into a stable production system, as detailed in the article “POC Purgatory” (in French).
On the number of systems and tools the agent must use, the quality and accessibility of the data, the level of security and compliance requirements (GDPR, EU AI Act), the evaluation effort and production operations. The budget is scoped after the framing session, with KPIs defined upfront.
That is a design principle: privacy-by-design architecture, encryption, granular access control and a full audit trail. Deployments can run in private VPCs, with open-source alternatives if your sovereignty constraints require it.
Healthcare and pharma (clinical trials, patient feedback analysis), investment finance, B2B SaaS (customer support, automated BI) and the public sector (INFPC virtual assistants in Luxembourg).
A 30-minute framing session to identify your high-ROI use case, confirm the prerequisites and choose the next step. No commitment.
Analysis of your processes and recommendations built for them
Solutions focused on measurable business impact
GDPR compliance and protection of your sensitive data
Technical and strategic analysis for CTOs, Heads of Data and digital decision-makers at mid-market and enterprise companies. Currently published in French.
The three structural causes behind failed AI PoCs, and the Production-First method for turning a validated MVP into a stable production system.
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