How it Works

Why EnterpriseWeb

Smarter, Faster, Better Intelligence

1Knowledge-first

Don't guess when you know

Ontologies and databases provide inferences and facts with deterministic accuracy, performance and efficiency that probabilistic language models can’t beat

EnterpriseWeb is a domain-driven AI orchestrator that supports hybrid reasoning across ontologies, knowledge graphs, relational databases, systems, devices and language models for a richer intelligence layer

2Workflow-first

Don't try, do

Deterministic decision and workflow logic supports transparent, auditable and governed actions, instead of unreliable and unpredictable agentic AI

EnterpriseWeb enables the declarative modeling of intent-based processes as a set of loosely-coupled tasks that can be dynamically configured and composed into complex workflows

3CPU-first

Brains not brawn

Software optimization beats brute force compute by dramatically reducing language model and token use to get more with less

The Semantic AI Harness efficiently orchestrates language models, as well as classic AI/ML/RL algorithms and good old-fashioned analytics for better and more efficient reasoning

4ROI first

Control costs, optimize value

EnterpriseWeb’s domain-driven AI orchestrator enables intelligent routing and precise Context-as-a-Service to ensure maximum price–performance

EnterpriseWeb puts the ROI in GenAI to reduce OpEx, improve customer retention, and deliver new value-added services

Rewriting Tokenomics

Less is More!

EnterpriseWeb’s Semantic AI Harness is accurate, auditable and governed, as well as ultra-low token, low-latency and resource and energy efficient. It averages only 3 tokens per interaction, 12-15 total (input and output) in a sub-second multi-turn transaction.

AgentsRegistered Clients
Intent
Context-as-a-Service
Semantic AI HarnessEnrich-Refine-Validate
Context-as-a-Service
Inference
Language ModelsModeled endpoints
  • The Semantic AI Harness acts as a deterministic backend for AI; mediating the relationships between language models and agents while enforcing access controls, permissions and contracts
  • EnterpriseWeb leverages domain knowledge, typed objects and active state to rewrite agent prompts and provide highly precise Context-as-a-Service to the language model
  • The Semantic AI Harness iteratively enriches and refines language model inferences against the Knowledge Plane until they can be deterministically validated
  • By shifting processing away from the language model, EnterpriseWeb reduces each “turn” with the LLM to the exchange of only a handful of tokens rather than overloading context windows
  • 3Tokens per Turn, Average
  • 12-15Tokens per multi-turn transaction
  • < 1Second per multi-turn transaction

Hybrid‑Reasoning and Parallel Processing

Task with Complex Logic
Language Models
Knowledge Plane
SQL, GQL, SPARQL
Connected Systems
Optimized Decisions / Actions

The best way to optimize and control AI is to orchestrate it.

EnterpriseWeb treats language models as composable inference services (local or cloud-based endpoints) registered in a catalog and invoked by workflows. The loose coupling naturally supports intelligent model routing and allows coordination with internal services and external data sources, AI/ML runtimes, and analytics engines.

Rather than depending on language models as the blackbox “intelligence layer” for everything, the Semantic AI Harness supports a “The Best Tools for the Job” approach (e.g., quality, latency, cost).

The Semantic AI Harness can flexibly execute searches, queries, inferences and constraint satisfaction problems across its Knowledge Plane, as well as orchestrate virtualized data sources (e.g., SQL, GQL, SPARQL) and other connected systems. The runtime fans out operations for parallel processing and correlates results for hyper-efficient situational awareness.

Orchestrating Complex Interactions

EnterpriseWeb’s serverless runtime is event-driven, asynchronous, concurrent and massively parallel. It supports the declarative modeling of tasks as complex events with multiple operations, queries and inferences and embedded service calls, which can be executed sequentially or in parallel as per policies for highly dynamic and deterministic actions with logs and traces linked to records.

The Semantic AI Harness:

  • Dynamically assembles relevant context (typed objects, data and workflows)
  • Semantically rewrites and routes prompts to language model
  • Composes and configures tasks into complex, multi-step and long-running workflows
  • Federates SQL, GQL and SPARQL queries (zero-copy integration)
  • Orchestrates distributed solution endpoints, virtualized data sources, federated components, and physical devices
  • Enforces constraints (e.g., ontological axioms, business rules, workflow logic, governance policies and transaction guarantees)
  • Automatically generates vector embedding for natural language processing

Deep Tech Deep Dive

For Systems Engineers

Eliminate Developer Toil and Unleash Productivity

Enabling enterprise-grade contextual automation in a distributed system is wicked hard. EnterpriseWeb abstracts complexity so developers can focus on the business.

Read the deep dive For Knowledge Engineers

Realizing Semantic Web Services

While inspired by the seminal 2001, Scientific American article, “The Semantic Web”, EnterpriseWeb took another path to realize the elusive vision of knowledge-driven interoperability and automation.

Read the deep dive