SummarAIse for future clinical deployment
Designed to combine purpose-built small language models, healthcare connectors, ontology-driven extraction, and clinician feedback within NHS-controlled environments.
Privacy-first AI infrastructure
AI that stays
in your control.
Newral.net orchestrates traditional machine learning, optimization, and agentic AI on infrastructure you control—so sensitive data, models, and operational knowledge stay inside your security boundary.
Streaming outputPartial results without waiting
Domain ontologyMedical · legal · finance
Foundation workflow
RUNNING / PRIVATE
inputEnterprise data
nodeTransform + route
modelLocal LLM
toolPredictive ML
toolOptimization
reviewHuman scoring
outputGrounded action
async · spray/gather · choice routing
Local by designModels run where the data lives
Model agnosticUse the right model for each task
Human governedReview, scoring, and audit paths
Domain adaptableOntologies for specialist workflows
The platform
One system. The right model for every decision.
Language models are powerful interfaces, but enterprise decisions also require prediction, verification, constraints, and control. Newral.net brings those capabilities into one governed workflow.
Agentic AI
Interpret requests, retrieve private context, coordinate tools, and generate outputs with explicit boundaries.
- Private RAG and knowledge workflows
- Tool use and choice routing
- Human review and evaluation
Traditional ML
Forecast, classify, rank, detect anomalies, and quantify uncertainty with purpose-built models.
- Time-series and regression
- Neural and probabilistic models
- Explainability and validation
Optimization
Turn predictions into feasible actions using solvers that respect business and policy constraints.
- LP, MILP, and constraint solvers
- Allocation, planning, and routing
- Deterministic guardrails
Foundation
A graph executor built for real AI workflows.
Foundation coordinates data, models, tools, people, and outputs as a configurable graph—supporting complex workflows without hard-coding a single industry or model stack.
01
Asynchronous execution
Run independent nodes concurrently and keep long-running workflows responsive.
02
Advanced dataflow
Spray/gather fan-out, streaming, partial results, and conditional choice routing.
03
Composable nodes
Data transformation, inference, local training, scoring, chat, voice, and solvers.
Interfaces + applicationsClinical, legal, financial, and enterprise workflows
Orchestration graphRouting · streaming · fan-out · human review
Models + decision toolsLLMs · forecasting · classifiers · Bayesian models · solvers
Private infrastructureOn-premises · VPC · air-gapped · organization-controlled registry
Deployment
Keep data, models, and IP inside your boundary.
Newral.net is designed for organizations where privacy, control, and domain specificity are non-negotiable.
Private cloud
Deploy in an organization-controlled virtual private cloud with network and identity controls.
On-premises
Operate on local servers close to sensitive data and existing enterprise systems.
Air-gapped
Support disconnected environments with offline software and model update packages.
Controlled lifecycle
Pin model versions, evaluation sets, registries, access policies, and audit records.
Applications
Built for specialist work, not generic demos.
Newral.net is being developed for domains where sensitive information, complex workflows, and professional judgment matter.
Private analysis for invention and prior-art workflows
Domain-specific document intelligence for sensitive patent research, technical evidence, and structured legal reasoning.
Chronologies across thousands of pages
Reconstruct events, order records by date, expand clinical terminology, and surface relevant evidence for expert review.
Private intelligence over operational data
Combine language interfaces, predictive models, anomaly detection, and governed access to reason over finance and ERP information.
Design principles
Enterprise AI should reason, calculate, verify, and act.
01 / RELIABILITY
Use specialized tools, not invented answers.
Agents invoke approved forecasting models, classifiers, and solvers instead of guessing operational results.
02 / CONTROL
Define the security boundary first.
Deployment, identity, logging, model access, and update paths are architecture decisions—not afterthoughts.
03 / EVIDENCE
Evaluate before scale.
Accuracy, uncertainty, faithfulness, constraint satisfaction, latency, and human feedback are measurable product surfaces.
04 / ECONOMICS
Choose the smallest capable model.
Purpose-built models can be faster, more predictable, easier to customize, and more practical for private deployment.
Bring AI closer to your data.
Discuss a private deployment, a specialist workflow, or how Newral.net can combine generative AI with the models and constraints your business already relies on.
Start a conversation