What does Neural Forge do?
Neural Forge is an applied machine learning and AI company based in Santa Clara-a-Nova, Almodôvar, in the Alentejo region of Portugal. We build end-to-end ML systems for environmental prediction, document understanding, conversational AI, and public-sector MLOps, covering everything from data collection and feature engineering through model training to deployed inference and the GPU infrastructure they run on. In practice that means we take on the whole problem rather than one slice of it: the ingestion pipelines, the models, the serving stack, the Kubernetes cluster underneath, and the interface people actually use. Four deployed systems, including a public wildfire risk map for continental Portugal and an eleven-model document AI pipeline for a municipal archive. We work in Portuguese and English, and most of our clients are Portuguese public bodies and institutes.
Where in Portugal is Neural Forge based?
Neural Forge is based in Santa Clara-a-Nova, in the municipality of Almodôvar, district of Beja, Baixo Alentejo, Portugal. We serve clients throughout Portugal and Europe remotely, and work on site with organisations across the Alentejo and the Algarve, and anywhere a larger project calls for presence. Being based in the interior of the Alentejo is deliberate rather than incidental: the wildfire risk, land, and heritage problems we work on are concentrated in exactly this kind of territory, and the institutions that own them (câmaras municipais, institutes, regional bodies) are often poorly served by Lisbon- and Porto-centric consultancies. Engagements typically run remote-first with periodic on-site work, and the infrastructure we build can be hosted on the client's own hardware.
How does machine learning predict wildfires?
Neural Forge's Tutela Ignis system combines satellite imagery, ERA5-Land weather reanalysis, NASA FIRMS active fire detections, terrain features, and ICNF historical fire records into a calibrated gradient-boosted ensemble that forecasts daily wildfire risk across a 1 km grid for continental Portugal. It is live at tutela.land. The pipeline engineers features across weather, terrain, land cover, and vegetation moisture, and the ensemble is trained seasonally, because the drivers of fire in February are not the drivers in August. Raw model scores are not risk probabilities, so the system applies post-hoc probability calibration and a Bayesian prior shift at serving time. Validation is prospective and public: a live ledger on tutela.land tracks every forecast against real ignitions, and at matched ignition coverage the system flags 1.7 to 3.4 times less area than the top alert levels of the official systems.
Does Neural Forge work with Portuguese municipalities and public bodies?
Yes. Public-sector work is the majority of what we do. Neural Forge builds and operates AI systems for Portuguese local government, including a GPU-backed k3s Kubernetes cluster with Flux CD GitOps automation and in-cluster CI/CD, an archival document AI pipeline running eleven ML models over a municipal archive, and a real-time multilingual conversational avatar for citizen services that answers in six languages. We understand what makes public-sector AI different from a startup prototype: procurement realities, archival standards such as ISAD(G), the need for systems that a small internal team can still operate in three years, and the fact that citizen data usually cannot leave the building. That last constraint is why we build on self-hosted GPU infrastructure rather than commercial AI APIs.
What machine learning services does Neural Forge offer in Portugal?
Machine learning research and model development, data engineering for multi-source pipelines, computer vision and OCR for document digitisation, LLM and RAG systems, and applied AI infrastructure: GPU clusters, Kubernetes, and MLOps. Services are delivered in Portuguese or English, remotely across Portugal and Europe, on site in the Alentejo and the Algarve, and anywhere a larger project calls for presence. We are equally comfortable being handed a messy dataset and asked what is in it, being handed a trained model and asked to get it reliably into production, or being handed bare GPU hardware and asked to turn it into a cluster that serves inference. What we do not do is sell a fixed product: every engagement starts from the client's actual data and constraints, and every model has to earn its place through validation before it ships.
Does Neural Forge work with large language models and generative AI?
Yes. We run LLMs in production today: Qwen3 32B enriches archival metadata and Qwen3-VL 30B produces visual descriptions in our document AI pipeline, a LangGraph RAG chatbot answers questions over an entire municipal archive using multi-vector retrieval across Qdrant, and a custom self-hosted persona model drives a real-time conversational avatar for citizen services. Our approach to generative AI is retrieval-grounded rather than open-ended: models answer from a specific, curated corpus so their output can be traced back to a source document, which is what makes them usable in a public-service setting. We self-host on our own GPU hardware with Ollama rather than calling commercial AI APIs, so client data never leaves infrastructure the client controls.
Can Neural Forge run AI on-premise instead of in the cloud?
Yes, and it is our default. Every ML system we have deployed runs on hardware we or the client own: a GPU-backed k3s Kubernetes cluster for municipal AI services, and a single self-hosted edge node serving the Tutela Ignis wildfire model. LLM inference runs on native Ollama, services are exposed securely through Cloudflare Tunnel with authenticated access rather than open ports, node provisioning is automated with Ansible, and the whole cluster is managed through Flux CD GitOps so its state is version-controlled and reproducible. For Portuguese public bodies this matters practically as well as legally: sensitive citizen and archival data stays in the country, on machines the institution controls, with no per-token dependency on an external AI provider.
Does Neural Forge only work with the public sector?
No. Public bodies are the majority of our portfolio (municipalities, institutes, and national-scale environmental work), but the underlying capability is not specific to government. If you run an industrial operation, an agricultural or land-management business, or a company sitting on a dataset nobody has modelled yet, the work looks the same: understand the data, build the pipeline, train and validate the models, and deploy them somewhere they will keep running. We are a small team, so we take on a limited number of engagements and prefer problems where the data is genuinely difficult and the outcome is measurable. If you are not sure whether your problem is a machine learning problem at all, that is a reasonable first conversation to have.
