Scattered data, a model to deploy or an assistant to integrate: we look for the freelance consultant who knows your sources and your architecture. You choose, they work with your teams.
More expertise in the IT & Digital universe
Connect business sources to an operational platform: ingestion, transformations, data models and quality checks. Then say who owns each dataset, who may access it, and who answers for it in operation.
Prepare the transition from a trained model to a maintained service: reference data and versioning first, then testing, deployment and monitoring. The expert works with business and operations teams to set the conditions for use and handover.
Design an assistant based on a defined document collection, access rights and specific tasks. The work runs from document retrieval through to integration into business tools, by way of evaluating the responses it generates.
The use case is set: we find the consultant who maps the data to it.
Build under way: your agent tracks the milestones and adjusts the brief with you.
Service live: we organise the handover and, if needed, the permanent hire.
Describe the task and the available sources: applications, databases or documents. Tell us who can reach them, how often they are updated and who actually knows them.
We look for these profiles for our clients on a regular basis. Tell us about your achievements and the projects you have led: a talent agent calls you back to understand what you are looking for.
A data engineer works on data flows and their operation. A data scientist develops and evaluates models or analyses. If data access and quality are blocking the project, define this work before extending the search to modelling.
Yes. Tell us which sources you know and what is missing. We can look for a professional to start with an inventory and quality checks before you decide on the next stage of the project.
No. An exploratory project demonstrates what is possible under specific conditions; production deployment adds integration and testing, security, monitoring and rollback, then handover to your teams. The brief must specify which of these responsibilities belong to the professional.
Identify the documents, users and questions to address, then prepare a few cases with their answers. That leaves access permissions, document updates and response evaluation: discuss those with the teams that manage them.
A short description of the use case, data and team can be enough. The data and AI brief guide helps you gather useful information and identify open questions.
