Agentic AI · LLM · Automation
Agentic AI and LLM in regulated production.
Not demos. Autonomous systems that reason, coordinate, and act inside your processes — governed, auditable, compliant.
From Casablanca, Coda Solutions designs AI agents and LLM systems for regulated production in Morocco: business assistants, document search (RAG), back-office automation. Every system is governed, auditable and connected to your real systems — CRM, ERP, databases — with least-privilege access and data handling compliant with law 09-08.
What we build
Agents that actually work
Multi-agent systems
Design and deployment of autonomous agent architectures (LangGraph, AutoGen, CrewAI). Coordination, memory, and error handling in production.
Enterprise RAG
Retrieval-Augmented Generation at scale: massive corpus indexing, strategic chunking, reranking, continuous relevance evaluation.
LLM integration into your systems
Connecting models to your existing systems via APIs and tools. Agents that act in your databases, CRM, ERP — not just in a sandbox.
Fine-tuning and adaptation
Adapting foundation models to your business domain. PEFT, LoRA, instruction tuning — when RAG alone is not enough.
Governance and auditability
Decision traceability, compliance filters, regulatory guardrails. Mandatory for banking, healthcare, and public sector.
Sovereign LLM infrastructure
Deployment on AWS Bedrock, Azure OpenAI, Mistral AI, or on-premise. Data sovereignty guaranteed by design.
Our position
Regulated agentic AI is an engineering problem, not a prompting problem.
Prompt chains aren't architecture. We treat agents as software components — versioned, tested, monitored, and reversible — and engineer them the way we'd engineer any production system.
Free resource
Agentic AI in production: the 12-point readiness checklist
The questions auditors, regulators, and your security team will ask when your AI system goes live in a regulated environment.
- 01Sensitive data inventory
- 02Decision traceability
- 03Model governance
- 04Data sovereignty
- 05Retrieval relevance evaluation (RAG)
- 06Output guardrails
- 07Rollback plan
- 08Automated regression tests
- 09Drift monitoring
- 10Enterprise system integration
- 11Inference cost management
- 12AI incident protocol
How an engagement runs
Four stages, a deliverable at each
Use case
A use case with measurable value, available data and identified risks before any code is written.
Real-world pilot
A pilot on your data and systems, with an evaluation set that measures answer quality.
Industrialisation
Guardrails, traceability of every decision, cost and drift monitoring, rollback plan.
Governed operations
Model registry, regular reviews and an AI-specific incident protocol.
FAQ
Frequently asked questions
What is an AI agent, concretely?
A system that uses a language model to decide which actions to take — query a database, fill in a form, prepare a case file — within defined limits. The difference with a simple chatbot: the agent acts on your systems. That is why its permissions, guardrails and traceability matter as much as the model itself.
Can our data be sent to a model hosted abroad?
If they contain personal data, sending them outside Morocco is a transfer under law 09-08, subject to CNDP authorisation with some exceptions. Depending on the case, we anonymise data before sending, use models hosted in Morocco or on your infrastructure, or restrict the agent to non-personal data.
How do you know a RAG system answers correctly?
By measuring it, not by testing it by hand. With your teams we build a set of questions and expected answers, then measure retrieval and answer quality on every change. Without evaluation, you cannot tell whether a change improves or degrades the system.
How does an engagement with Coda Solutions start?
With a 30-minute first conversation with a senior engineer from our Casablanca team, not a salesperson. If the need is confirmed, we scope it, then propose an engagement model: fixed scope, embedded team or architecture advisory. You know who will work on your project before you sign.