DOSSIER · REVIEW
PORTFOLIO

Oriol Martinez Ribas· AI Engineering · Barcelona

AI systems
for real decisions.

Systems across data, models and product, built around real decision problems.

Selected evidence
01 7.0M+ flights processed 50,453-flight untouched test · 1.64× top-decile lift · 28.0% precision NOAA weather · temporal validation · probability calibration
02 Multi-seed Gaussian HMM stability 5 assets · 8 engineered features · k=2–5 candidates · 29 backend tests BIC selection · posterior probabilities · entropy traceback
03 143/143 tests · 7/7 routing cases 40 labelled RAG queries · 100 accounts per API batch Hybrid RAG · MCP · source provenance
AI engineering opportunities

Selected work · 06 systems

Selected AI systems

Six systems across operational ML, market inference, pricing, B2B research, NLP and spatial optimisation. Each case includes the interface, validation and implementation evidence.

TL;DR
01Flight Delay Risk7.0M+ U.S. flights · 64% better high-risk prioritisation vs baseline
02Quant Regime Tracer5 markets · stable regime detection across repeated runs
03Price Decision EnginePricing from demand elasticity · margin/volume constraints · tested on later unseen periods
04Evidence RouteB2B account research with hybrid RAG · evidence-backed routing · 7/7 scenarios correct
05Narrative ShiftMaps AI Act narratives across public sources · compares actor framing with probabilistic topic models
06Warehouse OptimizerHackUPC · warehouse optimisation with 2D/3D inspection
REF FDR · v1.5.0 Independent projectExhibit 01 / 06

Flight Delay Risk

Pre-departure delay prediction across U.S. flights.

A supervised learning system that scores scheduled flights using only pre-departure data. The pipeline compares model families, integrates point-in-time NOAA weather, calibrates probabilities and validates on chronological holdouts before producing a ranked operations queue.

[01]
1.64×
Top-decile prioritisation lift
[01]
28.0%
Precision in the priority queue
[01]
1.3 pp
Final-test calibration gap
[01]
50,453
Flights in untouched temporal test
Machine LearningFlight Delay PredictionModel ComparisonTemporal ValidationProbability CalibrationOperational Decision Support
Interface evidence

Product screens

05 frames
Product walkthrough

See the system in motion

Demo 01
Click to open playerPre-departure workflow
REF QRT · Regime Traceback Independent projectExhibit 02 / 06

Quant Regime Tracer

Interpretable market regime detection with probabilistic time-series models.

A probabilistic time-series platform for five real assets using Gaussian Hidden Markov Models, engineered risk features and BIC-based model selection. Chronological validation and multi-seed stability checks assess robustness, while posterior probabilities, entropy and per-date feature evidence make each regime assignment inspectable.

[02]
5
Real assets validated
[02]
8
Engineered risk features
[02]
k=2–5
Regime candidates compared
[02]
29
Backend tests passed
Probabilistic Machine LearningTime-Series AnalysisHidden Markov ModelsMarket Regime DetectionModel SelectionExplainable Inference
Interface evidence

Product screens

05 frames
Product walkthrough

See the system in motion

Demo 02
Click to open playerModel-to-interface flow
REF PDE · Governed pricing Independent projectExhibit 03 / 06

Price Decision Engine

Governed pricing optimisation from demand elasticity to executable price.

A decision-support system that estimates product-level price elasticity, simulates commercially feasible candidates and optimises under margin and volume constraints. Shared policy checks then determine whether a recommendation can execute automatically or must pause for persistent human approval, while FastAPI, Streamlit and the copilot converge on the same deterministic pricing and governance layers.

[03]
0.06
Mean absolute elasticity error
[03]
93%
True elasticity inside estimated 95% CI
[03]
6.4%
Median temporal holdout MAPE
[03]
43 · 4
Passed · integration skipped
Pricing OptimisationDemand ElasticityConstrained OptimisationLangGraphHuman-in-the-loopFastAPIDecision Support
Decision-support evidence

Pricing workspace

04 frames
REF ER · v2.4.0 Independent projectExhibit 04 / 06

Evidence Route

Evidence-backed account research and routing with agents, RAG and MCP.

An LLM-powered account intelligence workflow that combines first-party research with hybrid RAG, separates verified claims from hypotheses and routes accounts through structured decision rules. Its MCP-compatible agent interface preserves source provenance, evaluates retrieval on labelled queries and exports executive-ready briefs.

[04]
143
Automated tests passed
[04]
40
Labelled RAG queries evaluated
[04]
7 / 7
Canonical routing cases correct
[04]
100
Accounts per API batch
AI AgentsLLMsHybrid RAGModel Context ProtocolEvidence GroundingB2B Account Intelligence
Interface evidence

Product screens

04 frames
Product walkthrough

See the system in motion

Demo 04
Click to open playerResearch-to-routing flow
REF NS · v0.2.0 Independent projectExhibit 05 / 06

Narrative Shift

Probabilistic NLP for tracking narratives across the European AI Act debate.

An NLP pipeline that learns latent topics from attributable European AI Act sources using a custom collapsed-Gibbs LDA implementation. Source-held-out validation and cross-seed alignment test topic stability, while source-balanced profiles and Jensen-Shannon divergence compare narratives across actors in an interactive React atlas.

[05]
20
Attributable public sources
[05]
1,035
Retained corpus sentences
[05]
0.781
Cross-seed topic stability
[05]
44
Automated tests passed
Natural Language ProcessingTopic ModelingProbabilistic ModelingLatent Dirichlet AllocationSource-held-out ValidationNarrative Intelligence
Interface evidence

Product screens

03 frames
Product walkthrough

See the system in motion

Demo 05
Click to open playerCorpus-to-atlas flow
REF MWO · HackUPC 2026 HackUPC team project · Competition prototypeExhibit 06 / 06

Warehouse Optimizer

Constraint-aware rack placement for the HackUPC 2026 Mecalux challenge.

A team-built hackathon prototype that converts warehouse boundaries, obstacles, ceiling profiles and bay catalogues into feasible rack layouts under a fixed runtime budget. Its heuristic search combines multi-strategy shelf packing, access-gap reuse, residual-space filling and local repair, while the FastAPI interface provides 2D/3D inspection and evaluator-ready CSV export.

[06]
5
Public challenge cases included
[06]
4
Constraint input files per solve
[06]
2D / 3D
Interactive layout inspection
[06]
CSV
Evaluator-ready placement export
Heuristic OptimisationSpatial SearchConstraint ValidationFastAPIThree.jsHackUPC 2026
Competition build

Interface and output

04 frames
Competition walkthrough

See the solver in motion

Demo 06
Click to open playerCompetition workflow

How I work

A model can perform well and still fail as a system.

The questions I keep returning to are simple: can the output be defended, can someone actually use it, and is it relevant to a real decision problem?

01 Evidence

Can I defend the output?

“All models are wrong, but some are useful.” — George E. P. Box

A model is a simplification of the real world. What matters is knowing when it is useful, where it fails and what evidence supports its output.

That is why I build validation into the system from the start: held-out tests, benchmarks, calibration and source provenance. If I cannot trace a result back to evidence, I know there is still work to do.

02 Systems

Can someone actually use it?

The model is only one component. Data pipelines, validation, APIs, interfaces and review workflows determine whether it becomes useful software.

03 Decisions

Does it change what happens next?

The final test is whether the output is useful for a concrete decision under realistic constraints.

Profile

Engineering background and current focus.

Before studying Artificial Intelligence at UPC, I worked on backend services and APIs at Inetum. That stage gave me a practical foundation in software engineering and experience building services and applications in a professional environment.

Over time, my focus shifted toward applied AI systems. Today I am especially interested in how models connect with validation, APIs, interfaces and decision-making processes.

An academic programme at Huazhong University of Science and Technology (HUST) in Wuhan broadened that perspective through international collaboration and direct exposure to applied AI, robotics and China’s technology ecosystem.

I am particularly interested in AI engineering, AI solutions and technology consulting roles.

Outside work

I usually end up reading about technology, markets and space, or studying Mandarin. I also try to keep an active life. I’m drawn to things that make me understand how a system works, whether it is software, a machine or an idea.

Get in touch

Open to AI engineering opportunities.

Happy to discuss the work, the trade-offs or a potential role.