Three Products.
One Cognitive Stack.
Three integrated layers — a programming language, a cognitive architecture, and a deployment platform — unified into one system purpose-built for autonomous machines that need to think, adapt, and scale.
Premise Agent
Language
Stigmergic AI software language that gives machines the ability to perceive, reason, and decide in real time.
GIL Cognitive Architecture
Cognitive systems that evolve with every deployment cycle — getting smarter and more precise over time.
Mind Server
Cloud Registry
From single-unit deployments to fleet-wide cognitive infrastructure — built to grow with your operation.
A language built for minds, not just machines.
Premise is a domain-specific programming language purpose-built for defining the cognitive behavior of intelligent agents — how they perceive, decide, learn, and act.
Traditional programming languages are built for deterministic logic. Premise is built for adaptive intelligence. It gives developers a structured, expressive syntax for defining the full cognitive lifecycle of an agent — from sensory input to decision to action — in a way that translates directly into runtime behavior.
Write your agent’s mind once. Premise handles the rest: execution across GILs, hardware portability, and scalable deployment via the Mind Server — without modification.
3x
Faster Prototype vs Python
DSL
Purpose Built for Cognition
4.9x
Less code than Java for identical behavior
Key Features
Cognitive-First Syntax
Native constructs for perception, memory, deliberation, and action. Express agent behavior the way you think about it — not the way a general-purpose language forces you to.
Behavior Lifecycle Control
Define full agent lifecycles — from passive sensing and active reasoning to reactive decision-making and autonomous execution — within a single coherent language model.
Hardware-Agnostic Portability
Write once, deploy anywhere. Premise compiles to GILs runtime-compatible instruction sets, making your agent logic fully portable across robotic platforms, devices, and environments.
Modular Agent Design
Compose minds from reusable cognitive modules — sensory processing, emotional analogs, goal hierarchies, learning loops — and assemble them into agents of any complexity.
Extensible Integrations
Bridge Premise with existing codebases, ML frameworks, and external APIs. Purpose-built AI doesn't mean isolated AI — Premise plays well with the broader stack.
Agent Marketplace Ready
Built with sharing and exchange in mind. Premise modules and agent definitions are structured for distribution, reuse, and commercialization in a cognitive AI marketplace.
Specifications
Type
Domain-specific language (DSL) for cognitive agent programming
Paradigm
Cognitive / agent-oriented — perception → deliberation → action
Runtime
Executes on GILs cognitive engine; hardware-agnostic compilation target
Module System
Composable cognitive modules: sensors, memory, goals, learners, actuators
Interoperability
FFI bridges to Python, C++, ROS2, and major ML frameworks
Tooling
CLI compiler, VS Code extension, simulation sandbox, agent debugger
Target Audience
Robotics
Define navigation, manipulation, and social interaction behaviors
Drones & UAVs
Autonomous mission planning and real-time adaptation
Industrial Automation
Process-aware agents for manufacturing lines
Game AI
Non-deterministic NPC and world behavior systems
AI App Developers
Compiler Engineers
Defense Tech
CTOs & Technical Founders
Research Labs
DSL Designers
Autonomous Systems
Cognitive Computing
The cognitive engine that makes machines think.
GILs is the runtime cognitive architecture that executes Premise Language agents — orchestrating perception, memory, learning, and decision-making in real time, across any hardware environment.
Where most AI systems follow fixed decision trees or probabilistic models, GILs provides a full cognitive loop: input perception, working and long-term memory management, goal prioritization, deliberation, and adaptive action selection — all operating concurrently and continuously.
The result is a machine that doesn’t just respond to its environment. It understands it, remembers it, and adapts to it — getting smarter with every operational cycle without requiring retraining or redeployment.
3
Deploy for: Mobile, Web, Robotics
CHC
Intelligence Milestone Benchmarks
100%
Hardware-agnostic. Runs on any device.
Key Features
Multi-Modal Perception
Process sensory input from cameras, LiDAR, audio, environmental sensors, and data feeds simultaneously — building a coherent real-time model of the operational environment.2
Layered Memory Architecture
Working memory for immediate context, episodic memory for situational history, and semantic memory for learned world knowledge — all accessible during live decision cycles.
Goal-Driven Deliberation
Agents maintain and prioritize multi-level goal hierarchies — balancing immediate objectives against longer-term missions in real time, even under conflicting constraints.
Reflective Behavior
GILs monitor their own performance, reflect on outcomes, and self-correct — a critical capability for autonomous systems operating in dynamic, unpredictable environments.
Continuous Memory Consolidation
Long-term memory is not bolted on — it is architectural. GILs consolidate experience over time, integrating past missions into an evolving knowledge base that sharpens future decisions.
Measurable Intelligence
Unlike black-box AI, GIL progress is benchmarked against Bailey, Piagetian, and CHC cognitive milestones — giving teams a verifiable, structured measure of capability growth over time.
Specifications
Type
Real-time cognitive runtime engine for Premise Language agents
Memory Layers
Working · Episodic · Semantic — concurrent read/write during execution
Deliberation Model
Goal-driven, multi-threaded, constraint-aware planning engine
Learning Mode
Continuous in-field adaptation — no retraining or redeployment required
Hardware Support
x86, ARM, RISC-V, edge TPUs — agnostic to underlying silicon
Safety Layer
Hard-coded behavioral boundaries; constraint-checked at every decision cycle
Target Audience
Surgical Robotics
Real-time spatial reasoning and adaptive instrument control
Precision Agriculture
Adaptive sensing and autonomous field-level decision-making
Emergency Response
Goal-driven navigation in unstructured, dynamic disaster environments
Energy Infrastructure
Autonomous inspection and anomaly detection with continuous learning
Healthcare AI
EdTech Platforms
Drone Teams
Robot Manufacturers
HRI Researchers
Device OEMs
First Responder Tech
Defense AI Teams
One Control Plane For Your Entire Cognitive Fleet.
Think of it as the operating system for your robot fleet — with enterprise-grade observability, API-first architecture, and Kubernetes-native scalability baked in from day one.
K8s
Kubernetes-Deployable
API
API-First Architecture
1:1
One dedicated GIL per registered device
Key Features
Agent Registration & Identity
Every cognitive agent in the fleet gets a unique registered identity — enabling granular assignment, monitoring, and control at the individual device level, regardless of fleet size.
Dynamic Mind Assignment
Assign, reassign, or update the cognitive profile of any device in the fleet remotely — pushing new Premise Language configurations to agents in the field without physical access.
Real-Time Fleet Monitoring
Live telemetry dashboards across your entire fleet — cognitive state, decision logs, performance metrics, anomaly flags, and operational health, all visible in one place.
Remote Device Puppeting
Issue real-time commands to any registered agent — override, redirect, pause, or terminate cognitive operations remotely, with full audit logging of every control action.
API-First Architecture
Every Mind Server function — registration, assignment, monitoring, control — is accessible via clean REST and WebSocket APIs, integrating into existing DevOps and enterprise pipelines without friction.
Kubernetes-Native Scalability
Deploy on your own managed cluster or cloud provider of choice. Mind Server is architected for enterprise scale — from proof-of-concept to global fleet operations without re-architecture.
Deployment Model
Cloud-native · Kubernetes-deployable · self-hosted or managed
Fleet Scale
10 to 10,000+ devices — single control plane across all agents
Agent Control
Registration · Assignment · Monitoring · Remote Command · Audit Log
API Surface
REST + WebSocket APIs; SDKs for Python, Node.js, Go
Observability
Live telemetry, cognitive state streams, decision audit trails, anomaly detection
Enterprise Features
RBAC, SSO/SAML, encrypted communications, compliance audit export
Target Audience
Enterprise AI Ops
Centralized governance for large-scale cognitive deployments
Global Fleet Ops
Multi-geography device management from a single platform
Infrastructure AI
Coordinated inspection robot fleets across national networks
Startup Scale-up
Production-grade infrastructure from day one, without the overhead
Robotics Companies
IoT Platform Builders
Enterprise IT & DevOps
Physical AI Teams
Fleet Operations
Autonomous Vehicle Platforms
Smart Infrastructure
Defense Technology
Ready To Build Machines That Think?
Explore the Premise source, read the documentation, or pick up the books.