Hexigma solves these 3 core AI implementation challenges. Our platform uses our library of hybrid models for Physics Informed Neueral Networks and LLMs and ensures that these 3 problems are solved in production:

1. Agent Accountability Problem Current enterprises deploying AI systems face a critical governance and trust crisis. AI decisions are often opaque "black boxes" where business stakeholders cannot understand how conclusions were reached or verify the reasoning behind automated actions. This lack of transparency creates multiple business risks: regulatory compliance failures in highly regulated industries, inability to audit AI-driven decisions during disputes or investigations, and executive reluctance to deploy AI for high-stakes business processes. When AI systems make errors or unexpected decisions, organizations cannot trace the root cause, learn from failures, or demonstrate due diligence to stakeholders. The result is AI implementations that remain confined to low-risk, tactical applications rather than strategic business functions where accountability is paramount.
2. Agent Context Problem AI systems consistently underperform in enterprise environments because they lack the contextual awareness that human decision-makers take for granted. Each AI interaction starts from zero context, requiring users to manually provide background information, business rules, historical precedent, and situational nuances that should already be understood. This creates a productivity paradox where teams spend more time preparing AI systems than they save from AI assistance. Critical business context – such as customer history, regulatory constraints, seasonal patterns, organizational priorities, and interdepartmental dependencies – remains fragmented across systems and tribal knowledge. Without this context, AI systems make technically correct but business-inappropriate recommendations, miss important edge cases, and fail to adapt to changing business conditions, ultimately delivering generic solutions to unique enterprise challenges.
3. Agent Coordination Problem Modern business processes require multiple AI agents to work together, but current AI systems operate in isolation, creating coordination chaos. When organizations deploy multiple AI tools across different departments or workflows, these systems cannot communicate context, share insights, or coordinate actions. This leads to duplicated effort, conflicting recommendations, and missed opportunities for compound value creation. For example, an AI system analyzing customer sentiment cannot inform the AI system managing inventory levels, even when customer feedback directly impacts demand forecasting. Teams manually bridge these coordination gaps, creating bottlenecks that negate AI efficiency gains. As organizations scale their AI deployments, the coordination complexity grows exponentially, resulting in fragmented AI implementations that work against each other rather than amplifying collective intelligence across the enterprise.