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AI & Data
Useful AI, applied to your data and your business.
We identify the use cases where AI delivers real gains, prepare the data they require, then deploy reliable, secure and measurable solutions.
Internal assistant — Example
Company data
What is the average complaint handling time this quarter, by branch?
The average time is 3.4 days, down 18% from the previous quarter. The Rabat branch remains above average.
CasaRabatTangierFezAgadir
Source: CRM“Complaints Q3” table
Sourced, verifiable answersData hosted on your side
Use cases
Start from a business problem, not a technology.
A few examples of what AI and data make possible today, function by function.
Recruit and manage talent
- CV analysis and ranking
- Answers to employees' HR questions
- Skills mapping
Improve reliability, anticipate
- Automatic invoice data extraction
- Reconciliations and anomaly detection
- Cash flow forecasting
Focus efforts where they count
- Lead prioritisation
- Sales forecasting
- AI-assisted proposal writing
Produce and deliver smoothly
- Demand and inventory forecasting
- Predictive maintenance
- Route and schedule optimisation
Decide on reliable figures
- Consolidated dashboards
- Natural-language questions on your data
- Alerts on key indicators
How to get started
Prove the value before you invest.
Each stage ends with a decision: continue, adjust or stop. You only invest once the value has been demonstrated.
Use case workshop
Identify and prioritise high-impact use cases.
- Workshops with your business teams
- Assessment of available data
- Use cases ranked by gain and feasibility
Fixed feeOn quotation
Proof of concept
Test a use case on your real data.
- Working prototype
- Measurement of results achieved
- Deployment cost estimate
Fixed feeOn quote
Deployment
Move from prototype to a solution used every day.
- Integration with your tools
- Security, access and monitoring
- User training
ProjectOn quote
Data foundation
Clean, accessible data: the basis of every AI project.
- Data warehouse and pipelines
- Quality and governance
- BI dashboards
Project or monthly feeOn quote
Our approach
Data first, AI second.
An AI project rarely succeeds without reliable data. We handle both together.
1
IdentifyThe right problem
A specific use case, with a success metric defined together with you.
2
PrepareThe data
Collection, cleaning, access rights and compliance.
3
PrototypeAnd measure
A prototype tested by real users, with quantified results.
4
DeployAnd monitor
Go-live, monitoring of answer quality, continuous improvement.
Trustworthy AI
Your data stays under your control.
Confidentiality is the main barrier to AI adoption in business. Our solutions are designed to address it from day one.
Controlled hostingOn your premises, in your cloud or in Morocco, depending on your requirements.
No training on your dataYour documents are never used to train public models.
Sourced answersEvery answer shows the documents or data it relied on.
ComplianceAn approach aligned with Law 09-08 and CNDP requirements.
We use it ourselves
AI in production, in our own product.
Pintalent, the recruitment platform we built, analyzes CVs with AI every day. It explains why a profile does or does not match a position, and always leaves the final decision to the recruiter.
It is this hands-on experience, with its constraints of reliability, cost and confidentiality, that we bring to your projects.
AI analysis — Data engineer84% match
Python, SQL · 5 yearsRequired ✓
Spark / Airflow · projects citedRequired ✓
Cloud (GCP) · no experienceTo check
English · fluentPreferred ✓
Technologies
The right models for every need.
Generative AI
OpenAI GPTClaudeGeminiMistralLlamaRAGLangChainAzure OpenAIAWS BedrockAI agents
Machine learning & MLOps
PythonPyTorchTensorFlowscikit-learnHugging FaceMLflowDatabricksVertex AI
Data & BI
Power BIMicrosoft FabricSnowflakeDatabricksBigQuerydbtAirflowSpark
FAQ
What clients ask us about AI.
Is our data sent to services such as ChatGPT?
Not without your consent. Depending on your requirements, we use business offerings that do not use your data for training, or models hosted in your own environment.
Do we need a lot of data to get started?
Not necessarily. Many use cases, such as document assistants or document data extraction, work with the documents you already have. The workshop lets us check this before you invest.
How much does an AI project cost?
It depends on the use case. That is why we start with a workshop, then a fixed-price proof of concept, which gives a reliable estimate of deployment and running costs.
What happens if the AI makes a mistake?
Our solutions cite their sources and leave the decision to a human on sensitive matters. We also put answer-quality monitoring in place after deployment.
Can you train our teams?
Yes. We offer training tailored to each audience: AI awareness for business teams, hands-on workshops on generative AI tools, and technical training for data and IT teams, on-site or remote.
Get started
What problem do you want to solve?
Describe your idea or your challenge. We will tell you frankly whether AI is the right answer.
- Reply within 24 business hours
- First conversation with no commitment
- Non-disclosure agreement on request