A Multi-Agentic Enterprise Solution by Technostacks

Acts as the brain, routing intents to the appropriate specialized agent.

Leverages Role-Based Access Control (RBAC) and RAG (Retrieval-Augmented Generation) to answer queries with citations and references.

Validates responses against fallback rules to eliminate hallucinations and ensure the user’s requirements are met.

Manages long-term state and session history, ensuring agents provide personalized, informed responses across interactions.

Intercepts actions before execution to cross-reference them against regulatory libraries and internal safety policies, ensuring all operations remain compliant.

Monitors system performance and failure logs to dynamically handle human hand-offs, assigning urgent issues to relevant human managers and drafting context-aware communications.

Provides standardized, helpful messaging during failure cases to maintain user trust.

Facilitates real-time, schema-less querying directly into the database to provide instant analytics and operational insights.

Combines keyword and semantic search (OpenSearch) with clickable references that link back to historical message contexts.

Every agent action, from ticket creation to database querying, is logged, ensuring a 100% auditable trail for compliance.

Data access is strictly compartmentalized; an agent querying the MongoDB for a local depot cannot access sensitive regional financial data.

The Evaluator Agent and Guardrails act as a final filter, ensuring all LLM outputs are cross-referenced with internal Knowledge Base truths before reaching the end-user.

AI monitors workforce schedules and attendance in real time to identify staffing shortages, absenteeism, and operational coverage gaps.

AI analyzes asset data and incident reports to detect potential failures and trigger proactive maintenance workflows.

AI consolidates maintenance, uptime, and resource data into real-time operational insights for faster decision-making.

AI generates audit-ready compliance reports by validating operational data against regulatory and internal standards.

AI detects critical system failures, isolates affected assets, and automatically triggers high-priority maintenance alerts.

AI tracks battery, fuel, and power levels, sending proactive alerts before assets reach critical operating thresholds.

Real-time sensor analysis identifies abnormal vibrations to prevent equipment failures and improve operational safety.

AI continuously monitors environmental conditions and operational parameters to optimize asset performance and extend equipment lifespan.

RAG-powered AI instantly retrieves accurate answers from company policies, SOPs, and internal documents with source-backed references.

MCP securely connects to live enterprise systems, enabling real-time operational insights through simple natural language queries.

Visualize which agents (Q&A, Analytics, Maintenance) are most active and their respective accuracy scores.

Real-time monitoring of token consumption versus cached responses (ElastiCache) to show cost savings over time.

An AI-generated metric for each vehicle based on maintenance history and agent-analyzed image reports.

A dashboard view showing where the Evaluator Agent intervened, helping managers see how the Knowledge Base is evolving over time.

Track the “thinking time” of individual agents to optimize model selection (e.g., switching between Nova Lite and larger models).

Automated tracking of AI feature consumption on a per-company basis, enabling precise billing based on token usage and API calls.

AI agents analyze visual evidence to produce immediate damage report drafts, significantly reducing the administrative burden on managers.