AI agents perform well in controlled demos. They break down in live workflows when they lack enterprise context: entities, relationships, rules, dependencies, and operational signals.
Cubyts designs, builds, and operates Context Engines that ground agents in how your business actually works.
For enterprises moving from fragmented or vendor-locked context layers.
Plan migration →Engage Cubyts when AI is moving beyond experiments and needs to operate inside real workflows.
A scoped context-layer architecture
Domain model covering entities, relationships, rules, and signals
Working Context Engine in your environment
Agent-ready interfaces and workflows
Evaluation and quality checks
Ownership and handoff plan for your team
We can assess whether it is ready for agentic AI and define the right Context Engine foundation.
A Context Engine is the substrate your agents reason against. It models the entities that matter to your business, the relationships between them, the rules that govern them, and the operational signals that change them.
Built well, it gives agents a grounded view of your enterprise. Built poorly, or not built at all, agents are guessing.
There is no single right architecture. The right foundation depends on your data landscape, use cases, and how far you intend to take agents into operations.
A Context Engine built directly on selected systems of record. We model entities, relationships, and operational signals from a focused set of sources into a context layer agents can reason against.
A Context Engine built on a unified data substrate consolidated from systems of record. We design the consolidation layer, build connectors, and structure the substrate for cross-system reasoning.
A Context Engine built as a domain-specific ontology over your data foundation. We model business semantics into a queryable substrate that gives agents a durable reasoning surface.
Design, build, and deployment of agents that operate on a Cubyts Context Engine. Includes workflow modeling, orchestration design, evaluation, and operational handoff.
Migration of context layers, ontologies, graphs, or semantic models into a target environment while preserving the reasoning surface agents depend on.
A short-form assessment of data landscape, candidate workflows, and foundation fit. Produces a scoped recommendation, architectural sketch, and delivery plan.
Cubyts engagements are services-led and outcome-defined. We work with your team, model your domain, and deliver a Context Engine in your environment.
Conversations start with the workflow you want to improve and the level of reliability your AI systems need to achieve.
