Create efficiency and unlock new capabilities with AI.

In medieval Europe, the Talbot was known as a harbinger—one who announces or signals what is to come. Like the Talbot, we help organizations see around corners and prepare for emerging technologies that will reshape their operations.

"West" speaks to the pioneering spirit of American expansion. Just as the western frontier represented both challenge and opportunity, artificial intelligence opens new territories for enterprise innovation. We guide organizations through this technological frontier, helping them stake their claim in the AI landscape while avoiding common pitfalls.

Together, these elements—the foresight of the Talbot and the pioneering spirit of the West—define our approach to AI implementation.

Cognitive Hive AI (CHAI) is a configurable, adaptable, modular swarm architecture for complex use cases, such as are found in the defense, manufacturing, and healthcare industries. 

For many basic business AI needs, a standalone product—from ChatGPT to any of the thousands of AI tools, platforms, and tools available commercially—gets the job done. It's a matter of finding the right tool and plugging it in. But there are many environments in which no product is sufficiently configurable, adaptable, explainable, and agile. For these use cases, it's more helpful to approach AI implementation as the assembling of a capabilities set rather than deploying a tool. 

With Cognitive Hive AI, we build an ensemble of capabilities to match the needs of the client. A CHAI instance can incorporate any number of module types, from large language models to small language models to quantitative engines to various types of machine learning, IoT integrations, and data streams. These collaborate together toward a common goal, whether that goal is finding a cure for cancer, or monitoring gray zone activities by U.S. adversaries. 

Because CHAI is modular, individual modules can be updated, replaced, or fine-tuned without disrupting the entire system. As AI improves and new capabilities come online, the CHAI ensemble can incorporate the latest. It's more akin to an evolving organism than a monolithic product. 

If you'd like to explore what a CHAI implementation might look like for your use case, contact us to discuss. 

Explainability is crucial because AI systems that make opaque decisions create unacceptable risks in business and government operations. When AI makes decisions about loans, medical diagnoses, or defense operations, organizations must understand how those decisions were reached.

Cognitive Hive AI addresses this through its modular architecture:

• Each AI module handles specific tasks with clear, traceable decision paths
• Organizations can examine how individual components contribute to final outputs
• The system maintains audit trails showing exactly why decisions were made
• Human operators can intervene at specific points when needed

Unlike black-box AI systems, CHAI's explainability enables:

• Regulatory compliance in highly regulated industries
• Detection and correction of biased outputs
• Clear accountability for AI-driven decisions
• Trust building with stakeholders and end-users
• Effective human oversight of AI systems

This transparency is particularly vital in high-stakes applications where errors could result in financial losses, legal liability, or harm to individuals. The ability to understand and validate AI decisions becomes a fundamental requirement for responsible deployment.

A modular approach to AI offers several strategic advantages over monolithic, black-box systems:

Enhanced security and control

• Deploy components in air-gapped environments when needed
• Control exactly which modules access sensitive data
• Update individual modules without compromising the whole system
• Maintain stricter governance over AI operations

Faster deployment and updates

• Add new capabilities without disrupting existing operations
• Update specific modules to address emerging threats or needs
• Scale individual components based on demand
• Test new modules in isolation before deployment

Lower operational costs

• Run only the modules you need for specific tasks
• Reduce computational requirements compared to monolithic systems
• Deploy on standard hardware rather than specialized infrastructure
• Scale resources efficiently based on actual usage

Better explainability

• Trace decisions through specific modules
• Understand exactly how the AI reaches conclusions
• Maintain clear audit trails for compliance
• Enable effective human oversight

Reduced vendor dependency

• Avoid lock-in to single AI providers
• Mix capabilities from different vendors
• Maintain control of your AI infrastructure
• Replace components without system-wide changes

This modular approach aligns with the Department of Defense's Modular Open Systems Approach (MOSA), ensuring compatibility with emerging standards while maintaining the flexibility to adapt as technology evolves.

A system of systems approach breaks down AI implementation into specialized modules that work together while maintaining independence. Unlike monolithic AI systems that try to solve every problem, this approach lets organizations coordinate multiple AI capabilities—each optimized for specific tasks—into flexible networks that accomplish what no single system could achieve.

Think of it like a hospital network versus a single medical device. While the medical device's components only work within that unified system (if the device breaks down, its components are useless), each hospital in a network maintains independence while contributing to broader capabilities. The same principle applies to AI deployment: instead of relying on one massive model, we deploy specialized AI modules for different functions—fraud detection, risk analysis, pattern recognition, etc.—that maintain independence while working together. This enables rapid updates, clear accountability, enhanced security, and the ability to add new capabilities without disrupting existing operations.

This modular approach also aligns with the Department of Defense's Modular Open Systems Approach (MOSA), which emphasizes defined interfaces and component replaceability. At Talbot West, we implement this through cognitive hive AI (CHAI), which coordinates independent AI modules like specialized bees in a colony—each maintaining autonomy while contributing to emergent and advanced collective capabilities.

When we look at the impact AI can have on an organization, we tend to think about two distinct areas:

1. Efficiency: Reduce costs and increase throughput by using AI to do things quickly that humans were doing slowly. 
2. Emergent capabilities: Unlock new capabilities that were not possible until now.  

Let's look at both with some examples in each area.

Operational efficiency

AI dramatically improves existing processes across departments:

• HR: AI screening tools evaluate thousands of resumes in hours, not weeks. Natural language processing analyzes job descriptions to suggest optimal candidate matches. Chatbots handle routine employee questions about benefits and policies.
• Sales: AI analyzes customer interaction data to predict which leads are most likely to convert. It automates follow-up sequences and suggests the best times to contact prospects. Sales teams focus on building relationships rather than administrative tasks.
• Marketing: AI-powered tools generate and test hundreds of ad variations simultaneously. They analyze customer behavior patterns to deliver personalized content at scale. Marketing teams shift from repetitive content creation to strategic campaign planning.
• Finance: AI automation handles routine bookkeeping and reconciliation tasks with greater accuracy than manual processing. It flags unusual patterns for fraud detection and provides real-time insight into cash flow trends.

Emergent capabilities

AI enables entirely new organizational capabilities:

• Predictive maintenance: AI analyzes sensor data from equipment to forecast failures before they occur, preventing costly downtime. Manufacturing plants using AI-driven maintenance report 20-30% reductions in repair costs.
• Customer insight: AI processes mountains of customer feedback across channels to identify emerging issues and opportunities. Companies gain deep understanding of customer needs and market opportunities that would be impossible to discover manually.
• Supply chain optimization: AI simulates thousands of possible scenarios to identify optimal inventory levels and distribution routes. Organizations reduce costs while improving delivery speed and reliability.
• Product development: AI accelerates R&D by analyzing huge datasets to identify promising directions for innovation. It can test thousands of potential designs virtually before physical prototyping begins.

The key is implementing AI strategically, focusing on areas that deliver clear business value rather than adopting technology for its own sake. Most organizations start with efficiency gains in core processes, then expand into more transformative applications as they build AI capabilities.

Want to explore how AI could transform your operations? Let's discuss your specific challenges and opportunities.

When we engage with a client—whether it's the Department of Defense, a municipality, a software company, or an equipment manufacturer—we start with a deep discovery of that client's business processes and chokepoints. We look for where artificial intelligence solutions can make the biggest impact, and we look for which solutions are needed.

If a client's most pressing needs can be solved with an off-the-shelf product, we'll recommend that product. The more complex the use case, however, the more likely it is that a CHAI architecture is needed to address it.  

First and foremost, we have a duty to our clients to do what's in their best interest. The majority of organizations can benefit hugely from integrating AI into their operations. If that's not the case for you, we'll tell you so. 

Assuming you're a good candidate for some sort of AI solution, our first step is a deep dive to understand your operations. We'll look at where you have inefficiencies that could be streamlined with AI, as well as where AI could unlock advanced capabilities to give you an edge. We'll then hone in on what seems like the most low-hanging fruit.

1. Feasibility study: a feasibility study (sometimes called a productivity assessment) is a report that we put together after extensive research and engagement with your stakeholders. It details what the proposed AI solution is, what its ROI is expected to be, and what the roadmap is for implementing it. It's your complete blueprint for the implementation. 
2. Pilot project: after getting stakeholder buy-in for the feasibility study's recommendations, the next stage is a pilot project or proof of concept. This is a small-scale, low-risk demonstration that the proposed solution will work as described. It allows us to work out all the nuances and get the implementation working optimally before rolling it out on a widespread scale. 
3. Full deployment: after demonstrating effectiveness in its limited-scale proof of concept, we roll the AI solution out to active use. We can assist with ongoing maintenance and monitoring, or turn it over to you with detailed training and instructions, depending on your internal resources.

Talbot West also provides a wealth of other AI-related services, including AI education and training, AI workshops, AI governance solutions, and more. 

See our solutions page for the full scope of what we offer.