Contact Us

Technology Built for Trust and Intelligence

A robust technical foundation combining symbolic reasoning, neural intelligence, and cloud-native infrastructure.

Our Technical Approach

AI&sense.ai builds AI systems that are explainable, adaptive, and secure. We combine multiple AI paradigms—not because complexity is the goal, but because real-world problems demand nuanced solutions. Our technology stack reflects research applied to practical, enterprise-grade systems.

Core Technologies

The building blocks of our explainable AI systems

Agentic RAG / Multi-Agent Systems

Our systems use Retrieval-Augmented Generation combined with multi-agent architectures. Multiple specialized agents collaborate to solve complex problems, each contributing domain expertise while maintaining coherent, explainable outputs.

Symbolic Reasoning + Bayesian Learning

We combine the precision of symbolic AI with the adaptability of Bayesian learning. Symbolic reasoning provides logical structure and interpretability. Bayesian methods enable systems to learn from uncertainty and update beliefs as new evidence emerges.

Semantic Ontology & Knowledge Graphs

Knowledge graphs form the backbone of our explainability. Built on standards like RDF and OWL, our ontologies capture organizational knowledge in structured, queryable formats. Every reasoning pathway can be traced and audited.

Hybrid Symbolic-Neural RL

Our RL Policy Optimizer combines knowledge-graph reasoning with policy gradient optimization. The HybridRANOptimizer processes metrics, topology data, and simulator feedback to produce validated configuration actions.

Semantic Validation Loop

Every decision validated, every action auditable

Reinforcement Learning Layer

Intelligent optimization with knowledge-graph reasoning

RANPolicyNetwork

Neural network for policy gradient optimization in RAN configuration decisions

RLStateBuilder

Constructs state representations from DME metrics and Neo4j/Neptune topology

HybridRANOptimizer

Combines symbolic constraints with neural policy outputs for
validated actions

SemanticValidator

Ensures all RL outputs
comply with SHACL shapes and business constraints

VendorProfile Ontology

Maps proprietary parameters to
3GPP standards for
multi-vendor support

Intent Translator

Converts business goals (CoverageIntent) into PolicyGoal and TargetKPI objects

SafeLoop Engine

Intelligent Knowledge Graphs for Safe AI Systems

SafeLoop Engine is an enterprise-grade knowledge graph platform that transforms unstructured data into semantically-rich, queryable intelligence. Built on the open-source Cognee framework, SafeLoop extends it with domain-specific reasoning, advanced search capabilities, and AI safety compliance features.

SafeLoop enables organizations to build AI systems that don’t just process data—they understand it.

T

Telco Domain

O-RAN, 3GPP, Viavi, RADP support with pre-loaded ontologies

M

Medical Domain

Healthcare entity extraction and clinical reasoning support

+

Plugin Architecture

Extensible custom domains with ontologies and reasoning rules

AgentI Framework Architecture

Modular, extensible, and secure agent-based systems

Base Agents

Abstract foundation defining core capabilities

Routed Agents

Orchestration and routing layer

Worker Agents

Specialized task execution

Infrastructure & Deployment

Enterprise-grade, cloud-native architecture

Cloud-Native Architecture

Built on modern containerization principles for scalability and reliability.

  • Docker containerization
  • Kubernetes orchestration
  • Microservices design
  • Auto-scaling support

Platform Compatibility

Seamless integration with enterprise platforms and telecom standards.

  • AWS Bedrock
  • O-RAN architectures
  • 3GPP TS 28.541
  • JSON-RPC / MCP Protocol

Security & Compliance

Built with compliance from day one for regulated industries.

  • HIPAA compliance
  • GDPR requirements
  • EU AI Act ready
  • Audit trail provenance

Performance Benchmarks

SafeLoop RAN validation performance metrics

Trusted Partners

Explainability First

We optimize for accuracy and explainability together. Every decision has a traceable reasoning pathway.

Hybrid Intelligence

Symbolic, neural, and statistical methods work together
each contributing what it does best.

Human Oversight

SafeLoop architecture ensures humans remain in control. Automation handles routine; humans handle strategy.

Standards-Based

Built on RDF, OWL, 3GPP, and cloud-native patterns
for interoperability and maintainability.

See Our Technology in Action

Explore how SafeLoop and AgentI apply these technologies to real-world challenges.

View Solutions