maisa and sapient

HALP, standing for Human-Augmented LLM Processing.

Enterprise AI failures are not just about faulty algorithms; they're a symptom of deeper organizational issues, including siloed data, lack of a clear strategy, and a skills gap. By combining the strengths of Maisa.ai, Sapient Inc., and Equitus.us KGNN on the IBM Power11 platform, enterprises can address these readiness challenges and accelerate successful AI adoption.

Here's a breakdown of how this integrated ecosystem can solve key enterprise AI adoption challenges:

1. The Challenge: Data Fragmentation and Lack of Context

Organizations often have a wealth of data scattered across different systems (databases, emails, documents), making it unusable for AI.² This "data swamp" is the most significant barrier to AI adoption.

The Solution: Equitus KGNN provides the foundational solution.³

• Automated Data Unification: KGNN automatically ingests disparate data sources and connects them into a unified knowledge graph.⁴ This process eliminates the need for manual, time-consuming ETL (extract, transform, load) pipelines.⁵

• Semantic Contextualization: Instead of just linking data, KGNN enriches it with semantic meaning, establishing relationships and context.⁶ This turns raw data into a usable, AI-ready intelligence layer.⁷

• On-Premises Security: Because KGNN is optimized for IBM Power11, data remains on-premises, addressing critical security and compliance concerns for regulated industries.

2. The Challenge: Lack of Actionable Insights and Reasoning

Even with clean data, traditional AI models can struggle with complex, multi-step reasoning.⁸ They may provide an answer but lack the ability to show their work or adapt to new, unforeseen situations.

The Solution: Sapient Inc.'s HRM provides the reasoning engine.⁹

• Human-like Reasoning: Sapient's Hierarchical Reasoning Model (HRM) uses a brain-inspired architecture with high-level and low-level modules to "think" at different levels of abstraction.¹⁰ This allows it to perform deep, sequential reasoning on the knowledge graph built by Equitus.

• Efficiency and Traceability: Unlike large language models that often rely on "chain-of-thought" prompting, HRM is a lightweight, efficient model that can provide traceable and auditable "chains of truth."¹¹ This is critical for high-stakes decisions where explainability is non-negotiable.

• Tackling Complex Problems: By leveraging the contextual data from KGNN, the HRM can solve complex problems that are beyond the scope of traditional AI, such as rare disease diagnostics in healthcare or complex fraud detection in finance.

3. The Challenge: Talent Gap and Difficulty in Deployment

Many organizations lack the specialized AI and data science talent required to build, deploy, and manage complex AI systems.¹² This leads to a high rate of pilot failures.

The Solution: Maisa.ai provides the "citizen developer" platform.¹³

• Empowering Business Users: Maisa.ai's platform allows non-technical business users to create and deploy AI-powered "digital workers" using natural language.¹⁴ This democratizes AI creation and bridges the skills gap, enabling employees to automate their own workflows and business processes.

• Reliable and Auditable Agents: Maisa's agents are not just simple automations; they are powered by an advanced reasoning engine that makes their actions reliable, transparent, and fully auditable.¹⁵ This addresses a major concern in enterprise AI: a lack of trust and control.

• Seamless Integration: These digital workers can be created and deployed to leverage the knowledge graph and reasoning capabilities of the Equitus KGNN and Sapient HRM, all without needing to write a single line of code.

4. The Platform: IBM Power11

The entire ecosystem runs on IBM Power11, providing the essential infrastructure.

• AI-Optimized Hardware: Power11 is a purpose-built platform for AI inference.¹⁶ Its on-chip AI acceleration and planned Spyre accelerator chip are designed to handle the computationally intensive, real-time workloads of KGNN and HRM with greater efficiency than traditional servers.¹⁷

• Security and Resilience: Power11 provides industry-leading security features and high availability, with an estimated 99.9999% uptime.¹⁸ This ensures that mission-critical AI applications are secure and reliable, avoiding the risks associated with cloud-based AI.

• Lower TCO (Total Cost of Ownership): By eliminating the need for expensive GPUs and cloud-based services, Power11 helps organizations reduce both capital and operational costs, making enterprise AI more economically viable.¹⁹

By bringing together these three partners on a single, integrated platform, organizations can move from a piecemeal, experimental approach to a cohesive, secure, and highly efficient AI strategy, ultimately overcoming the core challenges of enterprise AI adoption. This is an excellent question that gets to the heart of how different AI platforms can be combined to create a more powerful and user-friendly solution. The collaboration between Maisa Studio and Equitus.us's Knowledge Graph Neural Network (KGNN) on IBM Power11 systems would create a unique and highly beneficial ecosystem for enterprise users.

Here’s a breakdown of how this could work and the value it would provide:

Understanding the Core Components

• Maisa Studio: A "model-agnostic self-serve platform" for deploying "digital workers." The key here is its focus on natural language training and its "agentic process automation" approach. This means business users, not just data scientists, can define and deploy AI-powered workflows. Maisa's "Chain of Work" and "Knowledge Processing Unit" architecture make these digital workers auditable and reliable, addressing the "trust" issue often associated with black-box AI models.

• Equitus.us KGNN: A "Knowledge Graph Neural Network." This is a platform that automatically connects, correlates, and unifies vast amounts of disparate, unstructured data into a structured knowledge graph. Its core strength lies in turning fragmented data into "explainable intelligence" and providing a context-rich, AI-ready data foundation. It also runs natively on IBM Power11 systems, leveraging their built-in AI acceleration (Matrix Math Accelerator).

• IBM Power11: The underlying infrastructure. It provides the high-performance, secure, and reliable platform for both Maisa Studio and Equitus KGNN to run on-premise, leveraging its on-chip AI acceleration to handle demanding AI workloads without a dependency on GPUs or external cloud services.

The Synergistic Collaboration

The Maisa/Equitus/IBM Power11 partnership is a classic example of "the whole is greater than the sum of its parts." They work together to solve a major challenge for enterprises: the gap between AI model development and business-user adoption.

Here's how they could work together:

1. KGNN Creates an AI-Ready Foundation: Maisa's digital workers need data to be effective. Equitus KGNN would serve as the "data pre-processor" and "knowledge engine" for Maisa. It would automatically ingest fragmented data from various enterprise sources (documents, databases, emails, etc.) and create a unified, structured knowledge graph. This is a critical step because Maisa's digital workers can then query this clean, organized data source, rather than trying to make sense of siloed, raw data.

2. Maisa's "Digital Workers" Leverage the Knowledge Graph: A business user could use Maisa Studio's natural language interface to create a digital worker with a prompt like: "Create a weekly report on all customer support tickets related to product 'X' that have been open for more than 48 hours and identify the top 3 recurring technical issues."

   • Without KGNN: The digital worker would have to figure out how to access different data sources (e.g., a CRM, an email system, a bug tracker), understand the relationships between them, and then perform the analysis. This is a complex, fragile process.

   • With KGNN: The Maisa digital worker simply queries the Equitus KGNN. The knowledge graph has already unified and connected all the data. The Maisa agent can execute a powerful query like: "Find all nodes representing a 'support ticket' for 'Product X' with a 'status' of 'open' and an 'open_duration' > 48 hours. Group them by the 'technical issue' node and rank them."

3. IBM Power11 Provides the Optimal Infrastructure: Both Maisa and Equitus benefit from the underlying IBM Power11 system.

   • Maisa: Its digital workers, which are "code-driven" and "traceable," require a reliable, high-performance platform to run on. The Power11's architecture ensures consistent performance and minimizes latency, which is crucial for real-time automation.

   • Equitus KGNN: Knowledge graphs are computationally intensive. The Power11's Matrix Math Accelerator (MMA) can significantly speed up the graph creation and traversal process, allowing for real-time insights and a more efficient data pipeline.

Improving AI Projects for IBM Power 11 Users

This collaboration addresses several key challenges for Power11 clients looking to adopt and scale AI:

• Bridging the Gap between Data and Action: Equitus KGNN provides the "AI-ready data" that has historically been a major bottleneck. Maisa Studio then allows business users to take immediate action on that data by automating processes.

• Empowering Business Users: Instead of AI projects being siloed within a specialized data science team, Maisa's natural language interface allows business professionals to build and manage their own digital workers. This accelerates the deployment of AI from months to days.

• Ensuring Trust and Transparency: Maisa's "Chain of Work" ensures that every decision made by a digital worker is auditable and explainable. This is critical for regulated industries and for building confidence in AI systems. The combination with Equitus KGNN adds a second layer of explainability by showing how the data was connected to produce the insight.

• Optimizing Infrastructure Value: IBM Power11 clients get a clear, tangible return on their investment. They can use the same server to run both their mission-critical business applications and a powerful, integrated AI stack for data preparation and process automation, all while maintaining data security and sovereignty.

• Creating a Feedback Loop for Continuous Improvement: As Maisa's digital workers operate, they generate data about their performance and "learn" from user feedback. This new, clean data can be fed back into the Equitus KGNN, enriching the knowledge graph and improving the accuracy and effectiveness of future automation.