NuronIQ builds enterprise intelligence systems that transform data into decisions — and decisions into autonomous action across digital and physical environments.
Every enterprise will become autonomous. The next generation won't merely automate workflows — they will continuously perceive, reason, decide, and act across digital and physical environments. NuronIQ exists to build the intelligence layer that makes this transformation possible.
Ingest signal from systems, sensors, documents, and events — structured or not — into a live model of the enterprise.
Ground large-model reasoning in enterprise knowledge, constraints, and physics so conclusions hold in production.
Evaluate options against policy, risk, and objectives — with humans in, on, or out of the loop by design.
Execute through agents, APIs, and machines — instrumented, reversible, and observable end to end.
We engineer each layer to production standards — from demo to dependable, from pilot to production.
Everything we build maps to one of four engineering disciplines — and they're designed to compose.
Knowledge plus reasoning. We build enterprise knowledge systems that ground frontier models in your data, your constraints, and your domain — so reasoning is accurate, current, and auditable.
AI agents and multi-agent orchestration. Long-running, tool-using agents coordinated as systems — with task decomposition, handoffs, memory, and recovery engineered in from the start.
Robots, factories, IoT, and edge AI. We extend the intelligence loop into the physical world — digital twins, physics-informed models, and edge inference that close the loop on real machines.
Security, governance, and observability. Autonomy without accountability is a liability. Every NuronIQ system ships with identity, policy enforcement, audit trails, and full-loop tracing.
Battle-tested scaffolds and reference architectures that compress months of platform engineering into weeks. Some are open source; the rest ship with an engagement.
Enterprise Claude Code — a configuration and workflow accelerator for taking Claude Code from individual use to governed, production-grade enterprise adoption.
github.com/affaan-m/ECC ↗A reference architecture for agent-driven software delivery — spec, plan, build, evaluate, and ship with agents embedded at every stage of the lifecycle.
Request the blueprint →Evaluation scaffolding for agent systems: task suites, LLM-as-judge patterns, regression gates, and CI integration — so quality is measured, not assumed.
Request access →Production-ready templates for building MCP servers over enterprise systems — auth, rate limiting, observability, and typed tool schemas included.
Request access →Digital twin starter for industrial assets — OpenUSD scene structure, sensor data pipelines, and physics-informed surrogate model scaffolding.
Request access →Patterns for running agents at the edge: constrained inference, offline-tolerant loops, and safe actuation interfaces for IoT and robotics workloads.
Request the blueprint →A staged path that de-risks autonomy — each phase ships working systems, not slideware.
We map your decision loops, data surfaces, and constraints, then design the target architecture — knowledge, agents, runtime, and trust — against measurable outcomes.
We engineer the platform with our accelerators: agent harnesses, eval gates, MCP tooling, and twins — instrumented for observability from the first commit.
We expand the loop across teams, sites, and machines — raising autonomy levels only as evals, governance, and telemetry prove the system is ready.
Bring us a decision loop worth closing — we'll bring the intelligence layer, the accelerators, and the engineering discipline to run it in production.