Forward-deployed expertise for production-grade ML systems

Probabl deploys data science and machine learning experts directly alongside your teams to design, validate, and industrialize ML systems.
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We step in to unblock hard problems, enable and step out once your team is ready to move forward independently.

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When is Forward deployed engineering the right answer?

Support helps you run, accelerate and sustain existing ML systems. Our FDE practice helps you build, transform, or unblock them.

You are launching a new ML or AI initiative and need an expert sparring partner

You need to leverage multiple AI disciplines in concert together

You have a mix of data that is heterogeneous, siloed, or unstructured

You need to validate feasibility or ROI quickly

You must move from prototype to production

You face scale, reliability, or regulatory constraints

Selected forward-deployed engagements

Deep involvement in open standards and open source. Experts trusted by millions of practitioners.

Enterprise Knowledge Graph

Probabl deployed experts forward to design and build an enterprise-scale Knowledge Graph integrating heterogeneous archaeological data sources.

Using the open-source Synaptix framework, scientific publications, excavation reports, GIS data, HR records, and artifact databases were harmonized into a unified semantic graph aligned with European research standards (PACTOLS).

The resulting system powers archipel.inrap.fr, enabling API access and exploratory textual and cartographic search over the largest archaeological knowledge base in Europe.

Engagement type: Experiment → Scale · Forward-deployed build

ML practices & codebase audit

Probabl embedded alongside CHANEL’s Data Science team to review ML software engineering practices and code quality.

Through individual interviews, code reviews, and methodological analysis, we identified concrete improvements to better align models, tooling, and workflows with business needs.

The mission concluded with a written audit and feedback session, strengthening the team’s practices while preserving full ownership.

Engagement type: Audit · Forward-deployed review

Our engagement model

Inside your context

Our experts embed themselves seamlessly inside your context
(your data, infrastructure, constraints, and teams)

Time-bound and outcome driven

Engagements are time-bound and outcome-driven with clear deliverables — not staff augmentation, not outsourcing

Unblocking

We focus on unblocking hard technical and organizational decisions, not owning delivery forever

Autonomy

Every engagement ends with:
- Working code or production-ready systems
- Clear architectural decisions
- Documentation and knowledge transfer
- A clear handoff to your teams or to Probabl Support

Our service model: modular or end-to-end

Each phase can be engaged independently or sequentially.

Audit

Understand where you are. Identify where value is blocked.

Audit report

Technical recommendations

Roadmap suggestion

Ideate

Align teams and co-design the right solution.

Collaborative design workshops

Functional and technical framing

KPI and ROI definition

Experiment

Validate feasibility with real systems — not slides.

Source code

Hosted demonstrators

Synthesis and recommendations

Scale

From prototype to production-grade systems.

Batch and real-time ingestion

API design (REST, GraphQL, messaging)

CI/CD, testing, deployment automation

Reliability, security, fault tolerance

Maintenance and support handoff