Guidepad Solutions for Every Organization

Define a source, a target, and the mapping between them as data, and the pipeline runs immediately — no code written, no build, no redeployment. Extract, transform, and load, schema management, format conversion, and cross-system data movement are all configured on a running system by operators and analysts, not just developers. A new data source or sensor is a configuration task measured in hours, replacing the separate ETL, orchestration, and transformation tools most programs stand up and the engineering effort that goes with them.

Run AI agents as governed services with policy checks, role-based access, human approval where required, and full audit trails. Every agent executes inside an explicit state machine that bounds what it can do, credentials are scoped per step so the agent never holds them, and every run produces a replayable execution record. Models are swappable through a registry, including open-weight, quantized models for constrained hardware.

Connect intake, validation, analysis, approvals, and reporting through reusable workflows, and apply the same operational controls to human and AI participants. Teams choose the paradigm that fits the task: sequential plans for procedures with defined steps, or eventdriven automations for anything that has to respond as it happens.

Every Operation emits events when it runs; listeners subscribe to those events and fire the next Operation the moment something happens — no polling a database, no waiting for a batch. Sensor feeds, system telemetry, and user actions arrive as events that operators hook automations to from a live event feed: pick an event, define what runs next, and it fires every time that event recurs. The event bus rides on Guidepad's federating messaging exchange, so many instances sit on one and an event raised at a remote or disconnected node reaches the hub across a distributed network, with a durable queue and replay keeping the record complete through outages. Automations are organized on canvases — one per team, functional area, or concern.

Guidepad's flagship interface. Canvas View is not a diagram of the system; it is the system. Teams draw services, data flows, workflows, agents, and automations on a live canvas, Guidepad builds and runs what is drawn, and every element displays its real-time state. Operators and analysts extend it without writing code, and organizations arrange canvases by team, functional area, or set of automations, so the enterprise architecture stays current because it is the running system rather than documentation of it. Operational dashboards, model management, and audit views share the same surface.

Connect existing databases and data sources through a common data model and governed, auto-generated APIs while retaining existing systems of record. Data is typed on ingest, synchronized hub-and-spoke across intermittent links, fused and deduplicated by configured workflow, and released only to entitled consumers under role-based and attribute-aware access control. Licensed per hub with unlimited spokes.

Define services, environments, and deployment plans as data and standardize deployment across AWS, Microsoft Azure, Google Cloud, Oracle Cloud Infrastructure (OCI), and on-premises hardware — including government cloud regions and air-gapped networks — on Kubernetes, virtual machines, and bare metal. Third-party capabilities are hosted, versioned, governed, and audited as Services. Where pipeline tooling ends at deployment, Guidepad's policy enforcement and audit continue into runtime operations, identically in every environment.

Deploy AI services and mission workflows on customer-controlled infrastructure, from enterprise cloud down to Raspberry Pi-class hardware. Instances operate offline with local inference and signed-token authorization, apply configured statebased behavior for link loss and degraded conditions, and resynchronize with the hub when connectivity returns — built for DDIL operations.

Manage model versions, deployment, replacement, and rollback as governed operations. Models are swappable through a single model reference — open-weight models served locally on the runtime, including quantized small models for constrained hardware, or commercial models from the major AI labs accessed through governed connections — with no change to the workflows, policies, or audit that surround them. Retrieval carries lineage, and every AI-supported result retains its evidence, model version, and policy version. Model management, operational dashboards, and audit views share one interface.

Define data models, workflows, services, and environments as structured data. Definitions are reused across environments and APIs regenerate as models evolve, eliminating repeated integration work and the contractor lock-in that comes with hand-built glue code. Protected by U.S. Patents 11,907,688 and 12,375,295.