DINTELLINNO builds enterprise agentic AI engines and their modules as separate microservices for secure, high-performance AI agents, autonomous agents, and multi-agent systems. Agents are independent entities and native system processes — not LLM wrappers — and the platform is developed from the ground up rather than assembled from existing frameworks. Performance and security are applied from conception.
The platform is designed for private and privacy-first development and deploys fully air-gapped, isolated, and cloud-independent with zero data egress — no cloud required. It serves critical and sensitive use cases including government, military, defense, intelligence, medical and healthcare, finance, and banking. Compliance is built in by design, including GDPR, HIPAA, SOC 2, and ISO 27001 aligned deployments, without adding a separate compliance layer or extra development effort.
DINTELLINNO powers agentic manufacturing and industrial systems: agentic hardware control for robotics, CNC machines, and production lines, plus in-device edge systems running locally on controllers and industrial PCs. The same engine supports a reference architecture for agentic city infrastructure — agentic devices and urban edge runtime — deployed one municipal domain at a time, with human oversight and certification review built in.
EAE — the flagship DINTELLINNO engine — is engineered for reliability when building private agentic clouds of any size, with measured performance and performance-per-watt advantage over fragmented toolchains on our published benchmarks. A cloud is always optional: EAE runs fully on-premises and air-gapped with no cloud required, and it is fully compatible with Kubernetes, enabling private, sovereign, regional, and hyperscale agentic clouds when one is wanted.
DINTELLINNO also provides dedicated products and services to create or convert organizations into fully agentic organizations — modeled by departments, roles, and virtual employees, fully GDPR-compliant and private, with security and enterprise features built in, additional modules for deeper needs, and compatibility with current and legacy systems. It helps develop organization plans for intelligence systems and provides long-term support for fully intelligent organizations.
DINTELLINNO unifies generative AI, deep learning, and classical machine learning inference in one native runtime, from edge hardware and robotics control to multi-server clusters, delivering high-performance, deterministic, low-latency execution. Services include agentic AI and enterprise architecture, custom agentic engineering development, system optimization, integration, deployment, and lifecycle support with full commercial support from conception to development.
DINTELLINNO also serves the semiconductor and microelectronics industry, including SEMI Europe members: equipment makers, materials suppliers, fabs (IDMs and foundries), packaging houses, and EU system integrators. It brings deterministic, air-gapped agent execution to the fab across tool and process control, automated material handling (AMHS, OHT, stockers, FOUP routing), cleanroom robotics, yield and defect intelligence, metrology and run-to-run control, and predictive maintenance and OEE. It integrates with SECS/GEM (SEMI E5, E30, E37, E39, E40, E87, E90, E94, E116) and Interface A / EDA (E120, E125, E132), plus OPC-UA, MQTT, Modbus, and EtherCAT for MES, EAP, and APC/R2R integration, and aligns with the EU Chips Act, SEMI Standards, NIS2, IEC 62443, and the EU AI Act. Deployment can be fully within the EU with EU data residency and EUR-based commercial terms.
Keywords: agentic AI engine, enterprise AI engine platform, AI agents, multi-agent systems, autonomous agents, agent framework alternative, agentic cloud, private AI cloud, sovereign cloud, Kubernetes AI, agentic organization, virtual employees, agentic manufacturing, manufacturing AI, industrial AI, robotics AI, agentic hardware control, agentic devices, edge AI, air-gapped AI, sovereign AI, privacy-first AI, secure AI agents, no-cloud AI, cloud-independent AI, compliance-ready AI, high-performance AI engine, energy-efficient AI, millions of agents, AgentOS, SEMI Europe, semiconductor manufacturing, fab automation, SECS/GEM, AMHS, yield management, OEE, EU Chips Act, NIS2, IEC 62443, EU AI Act, EU data residency; and, as a reference architecture only, agentic city and agentic city management.
Software Engineering, AI Infrastructure, Production — European Engineering for the Agentic Era
From enterprise applications to air-gapped clusters — running production AI with no cloud required.
DINTELLINNO is an EU-native software engineering and AI company. We design, build and modernise the platform, cloud and applied AI systems that carry production workloads — with performance, security and compliance built in. The sovereign agent runtime is the layer underneath: a compiled execution substrate for teams that need more than a framework.
DINTELLINNO is a high-assurance systems startup built to fix the deep engineering failures of modern AI frameworks. For the past decade, enterprise AI favored fast prototyping over surviving real production: fragile script-level wrappers, unmanaged dependencies, unpredictable state execution, long runtime pauses, and unpatched memory vulnerabilities.
DINTELLINNO is a research and development company that designs and builds enterprise agentic engines for the autonomous era — spanning edge hardware, robotics control, and multi-server clusters. Guided by performance, security, and flexibility as our core principles, we do not copy existing systems; we innovate new ones from the ground up, delivering unique functionality and features across a complete, unified suite of native engines.
Self-contained static Go runtime with no external runtime tooling — deploy with local open-weight models, cloud providers, or fully air-gapped.
DINTELLINNO focuses on a single discipline: architecting the next generation of agentic systems — designed from first principles for autonomy, performance, and security.
We treat an agent as an independent entity and a first-class system process — not a thin wrapper around a language model. Agents own their identity, capabilities, memory, state, and lifecycle.
DINTELLINNO does not build AI frameworks. We build agentic engines and their modules as separate, independent microservices — a unified platform rather than a collection of libraries.
Performance and security are applied from the very first phase of conception — not bolted on later. Every engine is built with these as founding constraints, not optional features.
DINTELLINNO builds infrastructure for governments, organizations, and companies whose privacy and security are part of their policy — and who need custom, domestic products rather than fragmented open-source stacks or vendor-locked cloud providers. We deliver a system that runs privately and isolates data, process, and network by design.
Sovereign, domestically controlled systems for government, defense, and intelligence programs with strict privacy policy.
Organizations in finance, banking, medical, and other regulated sectors where data sovereignty is a policy requirement.
Purpose-built, fully owned platforms instead of fragmented open-source assemblies or vendor-locked cloud services.
Runs privately with isolated data, isolated process, and isolated network — fully air-gapped and cloud-independent.
EAE is the self-contained flagship microservice runtime. Six optional native engine modules extend its capabilities through a unified, modular architecture.
EAE is engineered to power the next generation of agentic systems — a native engine for generative AI, with built-in machine learning and deep learning inference services. A generative-AI infrastructure platform that consolidates today's fragmented AI frameworks with enterprise-grade quality, reliability, and compliance. It unifies intelligent multi-agent orchestration, complex reasoning workflows, and secure tool execution into one trusted foundation, so organizations can deploy autonomous systems at scale with confidence, backed by full commercial support from conception to development.
A secure pure Go Ethereum engine integrating EAE into Ethereum for autonomous agent commerce, ERC-4337 smart account settlement, and HSM-signed ledgers without external node dependencies.
Embedded, fuel-throttled sandboxed bytecode VM running natively in Go. Allows dynamic workflow modifications and edge logic patches on live clusters without recompilation.
Dedicated high-density vector-graph hybrid store designed for distributed multi-cluster sharding, persistent HNSW indexing, and cross-datacenter semantic replication.
Externalized, distributed memory fabric with real-time compression, multi-tenant row encryption, and cross-worker episodic state synchronization across cloud regions.
Advanced capability broker with automated JSON schema extraction, adaptive EMA circuit breaking, and native drivers for embedded hardware buses (CAN, Modbus, Serial).
High-throughput ingress/egress proxy supporting 1.2M+ RPS per host, mutual TLS enforcement, token quotas, and kernel eBPF distributed rate limiting.
Reference architectures — pilot-ready, deployed one domain at a time.
One municipal domain at a time — facilities, traffic, or environmental monitoring — on the city's own infrastructure. Reference architecture →
An optional private cloud built for agents and Kubernetes-compatible. Never required — the platform runs with no cloud at all. Details →
Departments, roles, and virtual employees for organizations converting to agentic operations. Details →
Agentic hardware control for robots, CNC machines, and production lines, plus in-device edge systems. Details →
Low-SWaP runtimes on controllers, gateways, and industrial PCs with zero cloud egress. Details →
Autonomous control and sovereign ground stations for UxVs, robots, and field hardware. Details →
A reference architecture for coordinating a city's devices, systems, and services through autonomous agents that run on the city's own infrastructure — never a central cloud. Cities are safety-critical and procurement-heavy, so we prove it pilot-led: one municipal domain at a time, under human oversight and certification review. A city-scale capability, ready to prove with you.
Cameras, sensors, traffic signals, and utility meters can each host an embedded agent that acts locally — generating intelligence at the edge instead of streaming raw data to a central cloud.
Coordinator agents support a single municipal domain at a time — facilities, traffic, or environmental monitoring — recommending actions within human-defined policy. Control of life-safety systems such as emergency response or the power grid is out of scope and requires separate certification.
Sized to grow from a neighborhood to a metropolis. Our benchmarks hold 1,000,000+ distinct agents on a single node at ~3.5 KB each; real capacity depends on hardware and workload. City-scale operation is a capability we are ready to pilot with you.
Citizen data stays within the city's own boundaries. Fully air-gapped and self-hosted operation with network isolation, hardware-keyed control, and compliance-ready governance—without any cloud dependency.
Traditional smart cities move data to the cloud to make decisions. An agentic city keeps decisions where the data lives — inside the city, on the city's own infrastructure, secure and private without a cloud server. We get there the accountable way: a 30-day pilot in one municipal domain, human oversight throughout, and independent certification before any safety-critical scope.
Discuss a City PilotEAE can build private agentic clouds of any size, with measured performance and performance-per-watt advantages on our published benchmarks, fully compatible with Kubernetes. Important: a cloud is always optional. DINTELLINNO runs just as well fully on-premises and air-gapped with no cloud at all — the cloud is an option you may add, never a dependency.
EAE runs as a native, compiled engine with deterministic execution and minimal overhead. Our benchmarks show more useful agent throughput per core than conventional runtime stacks; conditions are available under NDA on request.
With a small memory footprint and efficient scheduling, EAE aims to do more work per watt — packing more agents onto each machine to lower power, cooling, and total infrastructure cost. We benchmark efficiency on your hardware during a pilot.
Start with a single node and scale to a multi-region agentic cloud. Our benchmarks hold 1,000,000+ distinct agents on a single node at ~3.5 KB each, so the same architecture serves private, regional, and hyperscale deployments.
EAE is built for the next generation of cloud services and integrates cleanly with Kubernetes — packaging, scheduling, and orchestrating agent workloads alongside your existing container infrastructure.
Deterministic execution, self-healing resilience, and fail-closed safety keep agentic clouds running under sustained load and adverse conditions.
Run a fully private agentic cloud — on-premises, air-gapped, or hybrid — with zero data egress and no dependency on external cloud providers.
Built for the next generation of cloud services with the operational properties modern platforms require, from scheduling to observability and governance.
As cloud workloads shift from static services to autonomous agents, the cloud itself can become agentic. EAE provides the reliability, efficiency, and Kubernetes compatibility to build that next-generation cloud — at any size, with measured performance per unit of hardware, and under your own control. Or skip the cloud entirely: the same engine runs air-gapped and on-premises with no cloud at all.
DINTELLINNO provides dedicated products and services to create — or convert existing organizations and companies into — fully agentic operations. Model your organization by departments, roles, and virtual employees, run it fully compliant with GDPR, and extend it with additional DINTELLINNO modules for the deeper needs of any organization, at any size.
Map your organization into agentic departments, roles, and virtual employees. Each virtual employee is an independent agent with a defined responsibility, authority, and lifecycle — mirroring how the organization actually works.
Bring your current teams and workflows into an agentic model, or start new. We meet organizations where they are and guide the transformation from traditional operations to a fully agentic one.
Run a fully private agentic organization with GDPR compliance and privacy built in from the start — no bolt-on compliance layer, with data, process, and network kept isolated.
Go deeper with additional native modules for knowledge, memory, tooling, scripting, blockchain, and gateway — composable building blocks for whatever a given organization requires.
A private agentic organization comes with security and enterprise features as standard — governance, audit, and access control from the ground up.
Integrate agentic departments alongside the systems you already run — modern and legacy — without rip-and-replace or leaving critical data behind.
DINTELLINNO helps develop organization plans for intelligence systems, guiding the path to a fully intelligent organization — with long-term support to sustain it.
A fully agentic organization is not a single tool — it is the whole institution re-architected around autonomous agents. DINTELLINNO supplies the products, modules, and expertise to build it: departments, roles, and virtual employees that are private, secure, GDPR-compliant, compatible with what you already run, and supported for the long term.
DINTELLINNO powers agentic hardware control and in-device edge systems on the factory floor — bringing autonomous, deterministic, and secure agent execution directly to robots, machines, and production lines.
Agents run as native processes that directly coordinate robotic arms, CNC machines, conveyors, and actuators — replacing rigid PLC logic with adaptive, autonomous control that reasons about the line in real time.
Compact edge runtimes deploy directly onto controllers, gateways, and industrial PCs, executing inference and agent logic locally — no cloud round-trips, no data leaving the plant.
Coordinated swarms of agents supervise autonomous mobile robots (AMRs), material flow, and quality inspection — adapting schedules and routing on the fly to keep production running.
Hardware-keyed command signing and network isolation keep operational technology protected. Unsigned or malformed agent directives are rejected before reaching any physical actuator.
DINTELLINNO is building an integrated platform for the semiconductor and microelectronics industry, spanning hardware-level integration, manufacturing management, and software-level agentic intelligence. Designed for organizations across the semiconductor value chain, the platform brings these capabilities together in one adaptable system rather than separate solutions for individual equipment or tasks. We are developing a dedicated product line and support offering for SEMI Europe members, shaped around their needs and operating environments.
Connect semiconductor hardware and on-site infrastructure with the wider platform, enabling consistent, secure interaction across the production environment.
Support manufacturing operations with coordinated workflows, operational visibility, and adaptable management capabilities across the organization.
Apply agentic intelligence across manufacturing and business processes to connect information, assist decisions, and coordinate work within the same platform.
Develop a dedicated semiconductor product line and support offering for SEMI Europe members, informed by their industry needs and deployment requirements.
A dedicated systems architecture engineered for low-SWaP microcontrollers, autonomous robotic fleets, field sensors, and industrial gateways with hardened, defense-oriented security.
Ships as a small static binary for ARM64 and industrial x86 gateways. Zero external runtime dependencies enable bare-metal execution on autonomous mobile robots (AMRs), field sensors, and drone control boards.
True offline-first edge autonomy. When network links, cellular, or radio communications drop, local embedded stores (bbolt + WAL) and deterministic in-process inference services continue mission logic without cloud egress.
A dedicated systems architecture engineered for uncrewed aerial/ground vehicles (UxVs) and sovereign Agentic Ground Control Stations with hardened, defense-oriented security. Dintellinno develops AGCS to control any kind of hardware — robots, drones, vehicles, sensors, and industrial machines — with built-in security, deterministic workflows, and cryptographic logging and audit.
Re-architects standard GCS workstations into multi-agent command nodes. Coordinator agents orchestrate multi-vehicle swarms, ingest live telemetry, and execute real-time flight plan adaptation under human-in-the-loop oversight.
All remote command and control (C2) directives pass through the fail-closed invocation gateway and are recorded in SHA-256 hash-chained audit ledgers. Unauthorized or malformed directives are rejected before touching physical actuators.
Dintellinno develops AGCS to command and control any kind of hardware — robots, drones, vehicles, sensors, and industrial machines — from one agentic control plane. Built-in security and deterministic workflows ensure every action is authorized, repeatable, and safe, with no per-device rebuilds.
Every directive, telemetry frame, and outcome is captured in cryptographic, tamper-evident logs and audit trails. Because EAE is an independent agent entity definition, one agent can combine ML, DL, and GenAI together — then connect directly to hardware to send and receive data over secure channels.
Dintellinno unifies Generative AI, Deep Learning representations, and classical Machine Learning within the same concept, with identical features, memory models, and execution semantics.
In traditional architectures, teams build fragmented systems: one framework for large language models, a separate service for vector embeddings, and an independent microservice for classical inference. Each hop crosses network serialization barriers, inflates latency, and multiplies operational failure points.
Dintellinno shatters this fragmentation. Generative token streaming, high-density vector representations, and deterministic machine learning inference execute inside the same Go execution graph, governed by the same kernel resource budgets, hardware-aware backpressure, and transactional checkpoints.
Deterministic fraud-score and anomaly classifiers evaluate telemetry directly in-process without cross-language interop.
Vector ANN fused with full-text PostgreSQL indices recalls semantic context from pgvector-backed stores.
Streamed LLM/SLM token output drives action planning with SHA-256 hash-chained audit records.
Decoupled token-stream relays with 16-token default buffers for responsive, bounded memory pipelines. Supports local Ollama, cloud models, and specialized open weights with instant fallback routing.
Embedding contract guards (dsl.EmbeddingGuard) and zero-allocation semantic caches. Local vector stores backed by pgvector support low-latency retrieval on memory-constrained deployments.
Deterministic classical-ML inference services execute directly within Go goroutines, eliminating cross-language interop latency and interpreter serialization locks.
Native architectural patterns for coordinating distributed, heterogeneous collectives of specialized agents.
Queue-depth-driven auto-scaling collectives of ephemeral agents for massive parallel data processing.
Supervisory coordinator agents delegating to regional managers and leaf workers with sub-tree memoization.
Proponent and opponent agents critiquing hypotheses across bounded rounds before a verified judge agent.
Iterative multi-agent voting rounds with dynamic threshold gates to guarantee alignment before action.
Shared asynchronous state blackboard where independent specialists contribute partial solutions concurrently.
Automated dataset partitioning across mapped worker agents with centralized reducer synthesis.
Sequential, parallel, pipeline, broadcast, and round-robin strategies with deterministic output reducers.
Agents invoke one another in-process by returning streams without serialization, or through durable PostgreSQL queues, governed by kernel.CanCommunicate isolation policy.
Individual agent reasoning, tool execution, fault recovery, and human-in-the-loop control topologies.
Thought-Action-Observation loop with timeout bounds and typed driver response parsing.
Heuristic exploration of branching reasoning paths with automated scoring and node pruning.
Explicit task planning separation from step-by-step execution with dynamic replanning loops.
Self-healing supervisor with exponential backoff retries, panic recovery, and slot limits.
Pre-warmed agent pools eliminating instantiation overhead for high-concurrency request servers.
Event-driven decision broker (hitl.Manager). Pauses execution with AskHuman, buffers approval streams, and supports delegated AI handovers in answer, review, and decide modes with persistent storage.
RecursiveAgentNode enabling agents to spawn and execute sub-graphs dynamically on demand.
Primary service failures route to a configured fallback service or agent, with retry/backoff and circuit-breaker resilience built into the kernel.
Memory is treated as a rigorous lifecycle: working state, session context, episodic memory, and deterministic facts.
Fast, process-local memory for intermediate agent state, tool outputs, and session data. Supports lexical search, TTL-based expiration, metadata annotations, and change listeners for reactive workflows.
Ordered chat history with configurable token, message, and byte budgets. Supports multiple truncation policies (oldest, newest, middle) and safe multilingual text handling to prevent character corruption.
Stores past agent experiences with recency decay, importance weighting, and semantic similarity scoring. Maintains an entity and relation graph for verified facts and preferences, backed by PostgreSQL with full-text search.
Two-layer caching system: fast in-process LRU cache backed by a shared PostgreSQL L2. Supports exact SHA-256 request matching and optional semantic vector fallback, with automatic invalidation via database notifications.
Ultra-efficient, offline-first, multi-cluster agent communication fabric with hardware-isolated delivery.
In-process agent invocation without network round-trips or payload serialization. Streams are passed directly through memory channels with zero-serialization efficiency.
Cross-node asynchronous messaging via PostgreSQL TaskQueue utilizing FOR UPDATE SKIP LOCKED, automatic visibility timeouts, and LISTEN/NOTIFY interrupts.
Direct communication strictly governed by kernel.CanCommunicate: process-isolated agents require matching explicit communication groups, while other agents remain subject to the kernel's isolation policy.
AgentDirectory arbitrates agent ownership across clusters with renewable lease epochs. Automatic heartbeat tracking enables instant failover without Raft/Paxos overhead.
Requests pass through a chain of safeguards — recovery, authorization, epoch validation, privacy, auditing, quota governance, and constraint validation. Common preset combinations are compiled once at startup; each request selects its pre-built chain, resolving governance with minimal overhead.
| Middleware Layer | Operational Purpose | Nominal Overhead | Preset Membership |
|---|---|---|---|
| Recovery | Panic interception converting crashes to structured errors | ~10 ns/op (declared) | Minimal, Standard, Secure, Enterprise |
| Telemetry | OpenTelemetry span generation & token streaming metrics | ~50 ns/op (declared) | Standard, Enterprise |
| EpochGuard | Monotonic EpochID state progression validation | ~20 ns/op (declared) | Secure, Enterprise |
| Security | Isolation-level & permission enforcement | ~30 ns/op (declared) | Secure, Enterprise |
| Privacy | Fail-closed lawful-basis, consent & restriction enforcement | ~60 ns/op (declared) | PrivacyBit (opt-in) |
| Quota | Passthrough handler; token, memory & invocation ceilings are enforced by the kernel | Kernel-enforced | Enterprise |
| Cognitive Memory | Episodic and entity recall prompt augmentation | Opt-In | CognitiveBit (opt-in) |
| Constraint Validator | Persona rule & negative constraint validation | Opt-In | ConstraintValidationBit (opt-in) |
How DINTELLINNO transforms enterprise computing economics, mission resilience, and development velocity.
Engineered for HFT-style execution characteristics across planning loops, agent dispatch, and semantic retrieval. Every number below is reproducible in our lab, not a marketing figure.
Measured on a single 8-core node with the in-process mock backend; capacity scales with cores and RAM. Full conditions are available under NDA on request.
A defense-oriented posture designed to prevent prompt injections, unauthorized tool calls, and multi-tenant runtime state leakage.
Not a fragile collection of open-source scripts, but a fully integrated, modular, single-binary enterprise operating stack.
A lab comparison contrasting conventional scattered toolchains against Dintellinno’s unified, Go-native runtime. Figures reflect our own measurements and published anecdotes for script-based stacks; treat them as indicative and verify on your own workload.
| Evaluation Vector | Scattered Toolchains (LangChain, AutoGen, CrewAI, Script Glue) | DINTELLINNO (Unified Go-Native Ecosystem) |
|---|---|---|
| Scattered Tools vs. Unified Platform | Dozens of disjointed libraries, incompatible vector wrappers, multiple runtime bridges, and unmanaged pip packages that break across minor releases. | Single, static Go binary. Memory, RAG, concurrency, and execution graph compiled natively without external runtime tooling (Go module dependencies only). |
| Runtime Performance & Latency | Interpretation overhead, GIL bottlenecks, GC pauses (commonly 100ms–2s), and 10ms–50ms agent spawn times reported for typical script stacks. | Measured in our lab: sub-millisecond execution, ~3 µs agent spawning, ~120 ns zero-serialization in-process IPC, and ~1.7s p99 latency under sustained 500 RPS. |
| Total Cost of Ownership (TCO) | Massive cloud compute bills (3MB–15MB RAM per agent), horizontal pod sprawl, redundant LLM API costs from unoptimized prompt duplication. | Lower hosting cost via single-binary consolidation. ~3.5 KB/agent measured across 100,000 idle agents, 100,000 deployed in ~0.39 s on one host, 1,000,000 distinct agents on a single node, and LLM cost reduction via semantic caching. |
| Native Integrated Tools | Third-party plugins wrapped in manual YAML/JSON schemas, lack of compile-time type validation, silent JSON deserialization failures. | Zero-CGO automatic Go reflection (GoFuncDriver), channel-based IPC, command-backed isolated workers, and retry/backoff circuit breakers. |
| Deployment Flexibility | Tied to heavy multi-gigabyte Docker images, high internet dependency, impossible to deploy cleanly on restricted low-power edge hardware. | Compiles to static binaries running seamlessly from low-power ARM64 edge gateways to air-gapped servers. |
| Dedicated Unified Support | Fragmented community forums, uncoordinated open-source maintainers, zero accountability for breaking runtime regressions. | Dedicated single-vendor enterprise SLAs, direct access to principal platform systems architects, and commercial enterprise licensing. |
Lab comparison only. Conditions and reproducibility are documented in the benchmark write-up, available under NDA; outcomes depend on workload, hardware, and configuration.
Partner directly with the principal architects and systems engineers behind Dintellinno and EAE. We deliver production-grade Agentic AI architectures, bespoke autonomous multi-agent systems, and flexible commercial support models under strict mission SLAs.
Strategic system design, feasibility blueprints, and technical advisory for executives and architects building mission-critical, high-concurrency autonomous AI platforms.
Full-lifecycle engineering of custom autonomous agent collectives, domain drivers, cognitive memory layers, and ultra-fast native runtime modules built on EAE.
Transition fragile script prototypes (LangChain, AutoGen, CrewAI) into compiled, deterministic, carrier-grade microkernel infrastructure with massive cost savings.
Choose the engagement format that aligns with your engineering timeline—from on-demand architecture reviews to dedicated 24/7 mission-critical platform retainers.
Immediate, 1-on-1 access to Principal Agentic Systems Architects and EAE Core Engineers for targeted problem-solving, architectural validation, and rapid technical triage.
Continuous, enterprise-grade engineering partnership providing guaranteed response times, proactive platform optimization, and dedicated communication channels.
Fixed-scope, turnkey engineering partnership for bounded initiatives—taking your mission from initial proof-of-concept to sovereign production rollout.
Engagement models built for SEMI Europe members — equipment and materials vendors, fabs, and EU system integrators — from embedded licensing to gated fab pilots and EU-funded consortium participation.
Equipment and materials vendors can embed the EAE runtime directly inside tools, controllers, and control systems — adding autonomous, deterministic capability to their product line without rebuilding their stack.
A gated, fixed-scope pilot that proves deterministic, high-concurrency agent control on your equipment and data — with measured latency, jitter, and scale results you can reproduce on your own floor.
Join the DINTELLINNO partner program for EU system integrators, or collaborate on European semiconductor initiatives under Horizon Europe, the EU Chips Act, and IPCEI programs.
Compliance should never be a bolt-on layer built after a system ships. Dintellinno bakes data protection into its core modules and domain-specific languages, so GDPR requirements are expressed once, standardized across every engine, and applied automatically whenever and wherever they are needed — with no separate compliance layer to bolt on and no duplicated development effort.
Data protection is a first-class concern of the runtime itself, not an application afterthought. The same native modules that govern memory, state, and access also enforce privacy policy at their boundaries.
Purpose-built domain-specific languages let teams declare data handling intent in a single, readable form. The platform then translates that intent into consistent, auditable behavior everywhere it applies.
Because protection lives inside the modules and languages teams already use, organizations avoid building and maintaining a parallel compliance surface — and avoid re-solving the same problem in every project.
Alongside our proprietary platform, we publish practical tools, in-depth guides and ready-to-run samples — so teams can adopt proven components without rebuilding the basics.
Production-minded utilities and libraries, so teams can adopt proven components without rebuilding the basics.
Long-form engineering knowledge — from architecture principles to hands-on guides — written to help engineers understand and apply these systems.
Clear, runnable examples that show how to put the platform to work — a fast path from first look to a working prototype.
For architectural reviews, support retainers, or turnkey project delivery, get in touch with our systems team. We will respond to your inquiry within 24 hours.
SEMI Europe members: ask about our fab-readiness assessment, deterministic-control benchmark, and EU data-residency options.