Build & Adoption
From platform to production value.
We build your business agents, deploy them on your sovereign infrastructure and support your teams through to real adoption.
Understanding the difference
AI Tool vs AI Agent
The vast majority of AI deployments today are tools, powerful, but limited. Agents represent the next qualitative leap.
Operating mode
AI Tool
Reacts to a one-off command
AI Agent
Plans and chains actions autonomously
Initiative
AI Tool
Waits to be asked
AI Agent
Detects situations and acts proactively
Duration
AI Tool
Single interaction, no memory
AI Agent
Persistent context over a long task
Integration
AI Tool
Isolated interface
AI Agent
Connected to your tools (CRM, ERP, APIs, databases)
Control
AI Tool
You validate each step manually
AI Agent
Built-in guardrails: thresholds, alerts, human escalation
What we build
Five agent typologies, one common framework.
Each agent is built on the same proven architecture (tool-using, guardrails and sovereignty), but specialized for its business domain.
Research & synthesis agent
Autonomous exploration of internal documents, knowledge bases, reports and information flows. Structured synthesis delivered in minutes rather than hours.
Operational agent
Direct connection to your CRM, ERP, databases and APIs. The agent reads, writes and orchestrates your systems, with business guardrails defined by you.
Internal copilot
AI assistant specialized by function (sales, HR, finance, legal). Trained on your data, aligned with your processes, deployed on your infrastructure.
Monitoring & alerting agent
Continuous monitoring of your key indicators, anomaly detection and proactive escalation to concerned teams, without permanent human intervention.
Multi-agent orchestrator
Coordination of multiple specialized agents for complex workflows. The orchestrator breaks down the task, delegates, consolidates and delivers a coherent result.
Architecture
Four pillars that make an agent trustworthy.
A powerful but uncontrollable agent is a risk. Our technical approach guarantees both performance and control.
Tool-using
Each agent has a defined set of tools: database read/write, API calls, document generation, notification sending. No action outside the authorized scope.
Guardrails & alignment
Business rules are encoded at every level: input filters, output validation, confidence thresholds, automatic human escalation. The agent never makes a critical decision without validation.
Sovereignty by design
Agents are hosted on your infrastructure or in a dedicated private cloud environment. Your data never transits through public LLMs without your explicit consent.
Observability & continuous improvement
Every agent decision is traceable and audited. Performance dashboards, drift detection, feedback loops, your agent improves in production.
Sovereign deployment
Where your AI runs, under your control.
The Nexus platform deploys on the infrastructure of your choice. We rigorously assemble the four layers that form a controlled whole, with no hidden dependency.
Private & open-source LLMs
Deployment of open-source language models (Mistral, Llama, Qwen, Gemma…) in your environment. Fine-tuning on your proprietary data. Model choice and change at any time.
Private cloud & on-premise
Architecture adapted to your context: dedicated private cloud (no resource sharing), on-premise deployment in your datacenters, or hybrid architecture with sensitive data isolation.
Data security & compliance
Encryption at rest and in transit, granular access control, model access logging. Loi 09-08 compliance (Morocco) and GDPR for European subsidiaries.
Industrialization & LLMOps
Automated deployment pipelines, model versioning, drift monitoring, continuous evaluation. Your AI in production stays reliable, measurable and scalable.
LLMOps: industrialize your AI production.
A model in production is not an end, it is a beginning. AI56's AI Factory equips you with the tools and processes to manage the complete lifecycle of your models.
Without LLMOps, your models drift, inference costs explode and your Data Science teams spend their time firefighting. With a well-built AI Factory, your AI becomes a managed, auditable and scalable industrial asset.
Discuss your AI FactoryTraining & fine-tuning pipelines
Reproducible orchestration of training cycles, experiment tracking, annotated dataset management.
Model registry & versioning
Centralized catalog of your models with versions, evaluation metrics and production promotion policy.
Serving & scalability
Model deployment for inference with load management, version A/B testing and automatic fallback.
Monitoring & drift detection
Continuous performance monitoring, behavioral drift detection, alerts and retraining loops.
Evaluation & internal benchmarks
Custom evaluation suites on your business use cases. No generic benchmark disconnected from your reality.
Governance & traceability
Complete audit trail of model decisions, per-model access policies, inference logs for compliance.
How we work
From design to production in 8 weeks.
Our delivery formula is structured to minimize risk and maximize value from the first weeks.
Scoping
- Use case and KPI identification
- Mapping of systems to connect
- Scope and guardrail definition
Prototype
- Functional prototype on real dataset
- Demo with business teams
- Adjustments and final scope validation
Deployment
- Full integration into your infrastructure
- Load, security and compliance testing
- Production launch and initial monitoring
Adoption
- End-user team training
- Performance and usage dashboard
- Continuous optimization and possible extension
Which use case to deploy first?
A 2-hour scoping workshop is often enough to identify the highest-impact use case. Let's plan it.