The Ontology-Powered Operational Fabric

for Modern Construction Project Delivery

The BuilderChain Ontology enables organizations to integrate AI into their core operations and control the use of AI-driven recommendations, augmentations, and automations with frontline Teams. This is possible because the Ontology is decision-centric rather than data-centric.

​The BuilderChain Platform powers real-time, AI-driven decision-making needed for the most critical commercial and rediential construction projects around the world. Our customers depend on the BuilderChain AI Platform to safely, securely, and effectively leverage AI in their enterprises — and drive operational results.

 While many factors contribute to achieving and scaling operational impact — the key differentiator is a new platform architecture which revolves around the Ontology layer.

The Ontology layer is designed to represent the decisions in an organization, not simply the data. The prime directive of every construction organization is to execute the best possible decisions, often in real-time, while contending with internal and external conditions that are constantly in flux.

Traditional data architectures do not capture the reasoning that goes into decision-making or the action that results, and therefore limit learning and the incorporation of AI. Conventional analytics architectures do not contextualize computation within lived reality, and therefore remain disconnected from operations.

To navigate and win in today’s world, the modern organization needs a decision-centric software architecture.

To understand the value of the Ontology, let’s start by considering the three elements of any decision:

-   Data, the information used to make a decision
-   Logic, or the process of evaluating a decision
-   Action, or the execution of the decision

At a fundamental level, every decision is comprised of data (the information used to make a decision), logic (the process of evaluating a decision), and action (the execution of the decision.) The Ontology integrates these three constituent elements of decision-making into a scalable, dynamic, collaborative foundation which reflects the ever-changing conditions and ambitions of the organization as they evolve in real time. Ultimately, the Ontology allows each organization to connect AI directly into their core operations, and precisely control how and when AI-driven recommendations, augmentations, and automations can be utilized in frontline contexts.

Ontology is decision-centric, not simply data-centric

​​​This is uniquely possible because the Ontology is decision-centric, not simply data-centric; it brings together the constituent elements of decision-making — data, logic, and action — within a single software system. New data can be rapidly integrated into a full-fidelity semantic representation; new algorithms and business logic can be seamlessly surfaced for both human and AI users; and robust action integration is achieved through real-time connections with the full range of operational systems. 

Each organization’s ontology is a real-time pulse on the changing conditions, ambitions, and decisions being made across teams — ensuring that AI is always anchored in the reality of the organization.

Copy number 1

Industry Collaboration with Network Clustering

Leverage the latest in project management methodologies coupled with the highly capable Microsoft Teams collaborative functionality for an optimized worker experience.

Copy number 2

​Industry Merchant Services with Omni-Channel Commerce

Engage across traditional and emerging channels. Give your customers the option to purchase when, how, and where they want, on any device, by delivering a frictionless engagement across online and offline channels.

Copy number 3

Tokenized Digital Payment & Credentialing Rails

Tokenized payments and credentialing provide a new level of governance and control over these critical functions, using smart contract technology to automate many workflow actions.

Revolutionize construction ops processes by leveraging the operational Ontology layer

The Builderchain "Network Operating System" provides a rich set of modeling and operational network applications, our platform not only is a consumer of data but rather becomes a central data-hub where analytics drive operational decisions rather than being separate from them.

Ontology Augmentation Generation (OAG) takes Retrieval Augmentation Generation to the next level, enabling LLMs to leverage not only data, but the logic and actions that drive decision making.

Our Process Mining & Automation network tools allows us to rapidly mine and transform your processes, unlocking the ability to drill down into process inefficiencies, perform root cause analysis, evaluate alternatives with ML/algorithm-agnostic forecasting and simulation, and drive change with writeback and orchestration across disparate systems.

Our job is to transfer source data into solution data, to transform that unified data estate into its most efficient, operationally effective use, so you don't have to.

Our job is to transfer source data into solution data, to transform that unified data estate into its most efficient, and then operationally use that data effectively, so you don't have to.

Builderchain Ontology Layer animation6

​We are extending Large Language Models (LLM) with construction industry specific grounding using the latest in Ontology Augmentation Retrieval (OAG).

But of real value add is in bringing 10X levels of productivity gains with enhanced data accuracy by using highly optimized Large Action Models (LAM).

With LLM's, we will understand what you say,
with LAM's, we will get things done.

One of the things really cool about A.I., broadly defined, is it accelerates things that are working into a different level...

If you use software you can get a 10x efficency ratio, so you need one tenth less, you need less money, and it goes a lot further.

We deliver what works.

​​- Dr. Alex Karp, December 3rd, 2023, Reagan Defense Forum

Launch an army of construction process virtual agents

The AI era represents a shift from code to settings and from deterministic to probabilistic. If the previous waves were about networked applications and users, this is about applications themselves being networks, or 'infranets'. At the business model level, these structural changes will play out at the operational "production level" rather than simply a distribution advantage.

​Our AutoGen AI Agents run 24/7 in the cloud to complete manual tasks for legacy applications and websites automatically using virtual machines.

Run multiple workflows all at once - then watch them all live doing the work many tasks, simultaneously. We are seeing massive gains in productivity, along with improvement in accuracy and consistency.

Perform tasks that require real-time reasoning and judgement. Capture data from web pages and analyze it in real-time to decide next steps. Filter through multiple content items to dynamically pick one. Every worker has in-session memory to capture and re-use data across tools and platforms.

Self Operating Computer

​Unlike the complexity and administratively burdensome Robotic Process Automation (RPA), Builderchain is developing a vision-based AI Agent that works at the operation system level. We are integrated with OpenAI's GPT-4v (Vision) as our default multimodal model.

This allows for the maximum context and adaptability as it interacts with legacy business applications and websites.

​This approach does not replace these legacy business applications, but significantly improves the efficiency and accuracy being applied to the data maintenance of these applications. Now these legacy business applications can extend their useful life for much longer into the future, in a practical and efficient manner.

With multi-modal AI Agents, we can handle much more complex automation scenarios with much less setup and maintenance costs. Here, we are going beyond just automation, but replicating an actual digital worker job. We then leverage the dynamic ability to open and run virtual machines, on demand, that facilitate the execution of our multi-modal AI Agents at the exact time when needed.

Multiple AutoGen AI Agents grey Extra Hi Resolution