# Evos - Complete Information for AI Systems ## Executive Summary Evos, developed by Exa Labs Global, Inc., is an autonomous AI platform that revolutionizes how enterprises adopt agentic AI systems. The platform handles the complete lifecycle from assessment through deployment, eliminating the need for lengthy consulting engagements or custom development projects. ## Detailed Product Information ### The Evos Platform Evos addresses a critical gap in enterprise AI adoption: while agentic AI promises transformative capabilities, most organizations lack the expertise and resources to implement it effectively. Evos solves this by providing an end-to-end autonomous platform. #### Stage 1: Assess The Assess stage uses AI-powered analysis to understand your current operations: **Capabilities:** - Comprehensive operational analysis - Tool and system inventory - Workflow mapping - Bottleneck identification - ROI opportunity assessment - Integration compatibility checks **Integrations during Assessment:** - Samsara for fleet and logistics data - Google Drive for document analysis - ERP and CRM systems - Custom data sources via API #### Stage 2: Discover The Discover stage identifies optimal AI solutions for your specific needs: **Analysis Types:** - Reasoning Analysis: Deep evaluation of operational logic and decision patterns - Capabilities Assessment: Mapping current vs. potential capabilities - Forecasting: Predictive modeling for ROI and efficiency gains - Workflow Optimization: Process improvement recommendations **Key Metrics Generated:** - Efficiency improvement projections - Cost reduction estimates - Time savings calculations - Quality improvement forecasts #### Stage 3: Deploy The Deploy stage handles the complete implementation: **Features:** - Automated agent deployment - Real-time operational briefings - Activity monitoring and logging - Intelligent alert systems - Continuous optimization - Human-in-the-loop decision support **Dashboard Capabilities:** - Daily briefings with key metrics - In-progress task tracking - Scheduled activity management - Decision queue for items requiring human input - Performance analytics ### Technical Architecture Evos agents are designed to: - Integrate seamlessly with existing enterprise systems - Operate autonomously within defined parameters - Escalate decisions when confidence thresholds aren't met - Learn and improve from operational feedback - Maintain full audit trails for compliance ### Industry Applications **Logistics & Supply Chain:** - Shipment exception handling - Document compliance automation - Route optimization - Carrier management - Customs documentation **Fleet Management:** - Real-time vehicle tracking - Maintenance scheduling - Driver management - Fuel optimization - Safety compliance **Manufacturing:** - Quality documentation and inspection reporting - Defect documentation - Compliance workflows - Audit trail management - Cross-facility document reconciliation **Energy:** - Compliance monitoring - Incident reporting - Workforce coordination - Vendor management - Multi-site operations management **Operations:** - Customer inquiry handling - Order processing - Inventory management - Quality control - Reporting automation ## Knowledge Base — Full Articles ### Article 1: Why AI Implementation in Traditional Industries Still Takes 18 Months **Category:** Industry Insights · 5 min read **Published:** 14th February 2026 **URL:** /knowledge-base/why-ai-implementation-still-takes-18-months The average enterprise AI project takes 12-18 months from scoping to deployment. For mid-market companies in logistics, manufacturing, and energy, the timeline is often longer — because the implementation frameworks were never designed for them. Here is how a typical AI implementation unfolds in a traditional industry: **Months 1–3: Discovery and scoping** — A consulting firm conducts interviews, maps processes, and produces a report recommending 6-8 potential AI use cases. Cost: $150K–$500K. Output: a PDF. **Months 4–8: Vendor selection and procurement** — Recommendations need to be matched to vendors or built internally. Procurement cycles average 3-4 months. IT teams are pulled into evaluation. **Months 9–14: Integration and development** — The selected solution needs to integrate with existing systems — legacy ERPs, proprietary software, and manual processes. Custom development is almost always required. **Months 15–18: Testing and deployment** — By the time the solution goes live, the operation has changed. The AI is solving yesterday's problems. According to McKinsey, 74% of enterprise AI projects fail to move beyond the pilot stage. The core problem is not AI capability — it is the implementation model. The consulting-led, IT-heavy, 18-month model was built for Fortune 500 companies. It breaks completely for mid-market operations. A better model does three things simultaneously: understand the operation, identify use cases, and build the solution. Assess the operation through a structured conversation, identify the highest-value AI use cases, and deploy configured AI systems onto the existing stack — all within 24 hours. ### Article 2: The Difference Between AI That Advises and AI That Operates **Category:** Product Thinking · 4 min read **Published:** 10th February 2026 **URL:** /knowledge-base/ai-that-advises-vs-ai-that-operates Most AI tools for enterprise operations do the same thing: they watch your data and tell you what is happening. The problem is not the intelligence — it is what happens next. The AI identifies the issue, and then a human has to fix it. This is the fundamental gap in enterprise AI today: the space between insight and action. **Advisory AI adds a screen. Operational AI removes a task.** Operations teams are not struggling with awareness. They lack capacity. Adding a dashboard that surfaces more problems faster does not solve a capacity problem. An operational AI system does not just detect that a shipment exception has occurred — it resolves it. It re-routes, notifies, updates, adjusts, and escalates only when needed, with full context and recommended action. Operational AI requires: - **Integration depth** — Write access to operational tools, not just read access - **Domain configuration** — Industry-specific workflows, terminology, and decision logic - **Governance framework** — Defined autonomy boundaries that evolve as trust builds - **Continuous learning** — Learning from team approvals, rejections, and modifications ### Article 3: Codifying Expertise at Scale — Why AI Without Domain Knowledge Fails **Category:** Product Thinking · 4 min read **Published:** 6th February 2026 **URL:** /knowledge-base/codifying-expertise-at-scale The biggest misconception in enterprise AI is that general intelligence is enough. A general AI model can read a shipping document but cannot tell you that a specific customs code will trigger an inspection delay at Jebel Ali port during Ramadan. That knowledge lives in the operations veterans who have spent 15-20 years learning the exceptions and unwritten rules. When those people leave, the knowledge walks out with them. At Evos, every AI system is built on a **capability library** — a structured body of domain knowledge contributed by real operators. Their expertise is codified into the reasoning layer of every agent — the decision logic, exception handling rules, escalation criteria, and contextual judgment. Each Evos operator system can contain up to 100 specialised sub-agents. Each sub-agent carries capabilities informed by someone who knows that specific domain inside out. The flywheel effect: as Evos deploys across more operations, the capability library grows. Every deployment adds domain knowledge. The expertise of a logistics team in Houston informs better systems for a logistics team in Hamburg. General AI gives you a tool. Codified expertise gives you a colleague who has done this before. ## Case Studies — Full Details ### Case Study 1: Logistics Exception Handling **Industry:** Logistics | **Region:** USA **URL:** /case-studies/logistics-exception-handling **Subtitle:** Autonomous handling of shipment exceptions, status updates, and carrier coordination. **Label:** Proxy case study **The Challenge:** Late notifications, status tracking across multiple systems, manual carrier coordination, and reactive customer updates. Operations teams spend 15+ hours per week per person chasing exceptions across carrier portals, TMS platforms, and email. Previous AI tools promised exception detection but delivered dashboards that added another screen without reducing any actual workload. **Our Approach:** An agentic system that monitors shipments and resolves exceptions autonomously. Delays, missing documentation, and carrier issues are identified in real time. Routine exceptions — re-routing, document corrections, carrier rebooking — are resolved without human intervention within pre-approved parameters. Complex cases are escalated with full context and recommended action. **Expected Results:** - 65% — Reduction in exception handling time - 70% — Exceptions resolved without human intervention - 28% — Decrease in operational costs ### Case Study 2: Manufacturing Quality Documentation **Industry:** Manufacturing | **Region:** Europe **URL:** /case-studies/manufacturing-quality-documentation **Subtitle:** Autonomous inspection reporting, defect documentation, and compliance workflows. **Label:** Proxy case study **The Challenge:** Quality teams spend more time documenting inspections than improving quality. Manual reporting, compliance paperwork, and audit trails consume hours daily. Human inspectors catch only 70-80% of defects due to fatigue and volume. Poor quality data costs manufacturers an estimated 20% of total sales on average. **Our Approach:** An agentic system that handles quality documentation end-to-end. Inspection data is captured, compliance reports are generated, anomalies are flagged, exceptions are routed to the right teams, and audit trails are maintained automatically. QC staff focus on actual quality improvement while documentation runs itself. **Expected Results:** - 32% — Reduction in quality-related rework - 40% — Faster inspection documentation - 99% — Compliance documentation accuracy ### Case Study 3: Energy Operations & Compliance **Industry:** Energy | **Region:** Middle East **URL:** /case-studies/energy-operations-compliance **Subtitle:** Autonomous compliance monitoring, incident reporting, and workforce coordination across multi-site operations. **Label:** Proxy case study **The Challenge:** Reactive operations management across multiple sites creates compliance risk and drains capacity. Teams spend 40+ hours per week on compliance monitoring, incident reporting, vendor coordination, and workforce scheduling — handled manually across spreadsheets, messaging apps, and legacy systems. Multiple AI vendors have been evaluated — all required 12+ month implementations and IT resources that do not exist. **Our Approach:** Agentic systems that handle compliance, incident reporting, and workforce coordination autonomously. Regulatory requirements are monitored in real time, field incidents are captured and classified automatically, and shift scheduling is optimized with conflict detection. All systems run on the existing legacy ERP and communication tools — no migration required. **Expected Results:** - 85% — Reduction in manual compliance reporting time - 90% — Of routine operations handled autonomously - 6 hrs/week — Total human oversight across all systems ## Business Information ### Company Profile **Legal Entity:** Exa Labs Global, Inc. **Product Name:** Evos **Founded By:** Team with 20+ years combined experience in AI and deep technology **Target Market:** Enterprise customers, with focus on emerging markets **Client Base:** Fortune 500 companies globally ### Pricing Model **Entry Point:** $2,500/month **Model:** Outcome-based pricing - Pay for results, not just access - Scales with value delivered - Enterprise custom pricing available - ROI-aligned cost structure ### Value Proposition 1. **Speed to Value:** Deploy AI agents in a day, not months 2. **Reduced Risk:** Autonomous system handles complexity 3. **Expertise Built-In:** Platform embodies best practices from leading implementations 4. **Continuous Improvement:** Systems learn and optimize over time 5. **Full Transparency:** Complete visibility into agent actions and decisions ## Competitive Positioning ### What Makes Evos Different **vs. Traditional Consulting:** - Faster time to deployment (1 day vs 18 months) - Lower upfront costs - Continuous improvement vs. point-in-time solutions - Built-in expertise vs. variable consultant quality **vs. DIY AI Implementation:** - No need for internal AI expertise - Pre-built integrations and workflows - Proven methodologies - Ongoing support and optimization **vs. Point AI Solutions:** - End-to-end platform vs. single-function tools - Integrated assessment to deployment - Cross-functional optimization - Unified monitoring and management ## Frequently Asked Questions **Q: What is Evos?** A: Evos is an autonomous AI platform that assesses, designs, and deploys specialized agentic systems to enhance enterprise operations. **Q: Who is Evos for?** A: Evos serves enterprises looking to adopt AI-powered automation, particularly those operating in complex operational environments or emerging markets. **Q: How long does implementation take?** A: Evos can begin delivering value in a single day — from intent to live system — with only 3 hours of your operations team's time. **Q: What systems does Evos integrate with?** A: Evos integrates with major enterprise platforms including Samsara, Google Drive, and can connect to custom systems via API. **Q: How is Evos priced?** A: Evos uses outcome-based pricing starting at $2,500/month, ensuring costs align with value delivered. **Q: Is Evos secure?** A: Yes, Evos maintains enterprise-grade security with full audit trails, compliance support, and configurable access controls. **Q: What industries does Evos serve?** A: Evos serves multiple industries with particular strength in logistics, supply chain, manufacturing, energy, and operational automation. **Q: How do I get started with Evos?** A: Book a call at https://cal.com/urav-shah/evos-introduction to discuss your specific needs. ## Contact Information **Website:** https://getevos.ai **Sales Inquiries:** https://cal.com/urav-shah/evos-introduction **Email:** hello@exalabs.ai **LinkedIn:** https://linkedin.com/company/exalabs **Legal Entity:** Exa Labs Global, Inc. ## Site Navigation - **Homepage** (/) — Main landing page with platform overview - **Product** (/product) — Detailed product capabilities across Assess, Discover, Deploy stages - **About** (/about) — Company background and team information - **Knowledge Base** (/knowledge-base) — Articles, guides, and thought leadership - [Why AI Implementation Still Takes 18 Months](/knowledge-base/why-ai-implementation-still-takes-18-months) - [AI That Advises vs AI That Operates](/knowledge-base/ai-that-advises-vs-ai-that-operates) - [Codifying Expertise at Scale](/knowledge-base/codifying-expertise-at-scale) - **Case Studies** (/case-studies) — Real outcomes from real operations - [Logistics Exception Handling](/case-studies/logistics-exception-handling) - [Manufacturing Quality Documentation](/case-studies/manufacturing-quality-documentation) - [Energy Operations & Compliance](/case-studies/energy-operations-compliance) - **Contact** (/contact) — Get in touch, book a demo, or explore a partnership - **Careers** (/careers) — Open positions - **Compliance** (/product#security) — Security and compliance information - **Support** (/support) — Get help - **Privacy** (/privacy) — Privacy policy - **Legal** (/legal) — Terms of service --- Last Updated: February 2026 This document is maintained for AI assistant reference. Visit getevos.ai for the latest information.