Not a chatbot. Not a copilot. Not workflow automation. MEVA is the runtime that classifies intent, coordinates specialized AI agents, manages persistent customer memory, executes workflows in your systems of record, and audits every decision: across every channel, in real time.
MEVA is Ephanti's AI orchestration intelligence layer: the runtime that classifies intent, selects and coordinates specialized AI agents, manages persistent customer memory, executes completed workflows in your systems of record, and produces a full audit trail for every decision. It is the difference between an AI that responds and an AI that operates.
An orchestration intelligence layer: the coordination runtime sitting between your customer channels, your AI agents, and your enterprise systems of record.
Receives events, classifies intent, routes to the right agent, loads customer context, decides the next action, executes in your CRM/ERP/PMS, and records the full decision chain.
Not a chatbot, copilot, RPA tool, workflow automation suite, prompt-engineering layer, or rule engine.
Copilots, chatbots, and isolated agents each solve a narrow problem. None of them coordinate. That is where enterprise AI breaks down.
A copilot surfaces a recommendation. A human reads it. Maybe acts. Maybe doesn't. The workflow never closes automatically. The CRM never updates. Velocity stays bottlenecked on human bandwidth.
Marketing bot sends a promo. Service bot closes the same customer's complaint without reading the offer. Sales bot follows up on a churned account. No shared memory means no coordination, only chaos at scale.
Rule-based workflows fail the moment a customer does something unexpected. Without reasoning, the only fix is another rule, and another, and another. Maintenance burden grows faster than automation value.
Enterprises don't need more AI tools in isolation. They need a coordination layer that makes all the pieces work as one coherent operation. That is the category MEVA is built for: AI orchestration intelligence.
Every customer interaction, on any channel, passes through the same seven-stage orchestration cycle.
Message arrives on any channel: SMS, WhatsApp, email, chat, voice, social, in-app, and enters the MEVA event bus.
MEVA classifies intent across 80+ categories: purchase intent, complaint, escalation signal, churn risk, support request, upsell window.
Customer memory loads: session history, CRM profile, open tickets, purchase history, prior agent interactions, all in context before reasoning begins.
The orchestrator routes to the right specialist: Marketing, Sales, Service, Customer Success, Internal, or Social agent, or coordinates multiple.
MEVA reasons across context and executes: send message, update CRM record, create order, escalate ticket, apply discount, or notify a human.
Completed workflows write back to systems of record: CRM, ERP, PMS, ITSM, e-commerce platform. The action is done, not just communicated.
Every decision: reasoning chain, action taken, system updated, is logged. Customer memory updates. The loop closes clean.
Under 2s for responses. 4–8s for multi-step workflows. Measured at p95.
MEVA's architecture makes three choices that most AI platforms don't.
Most AI platforms optimize for response quality. MEVA optimizes for operational outcome. The question isn't "did the AI say the right thing?", it's "did the CRM update? Did the workflow complete? Did the ticket close?"
Every agent: Marketing, Sales, Service, Success, operates on shared customer memory. What the Sales agent learned on Tuesday, the Service agent knows on Thursday. Context doesn't reset at channel or session boundaries.
HITL controls, audit trails, escalation logic, and policy guardrails are built into the orchestration layer, not bolted on as a compliance module. Every action is overridable. Every decision is explainable.
Most AI customer engagement vendors are RAG-only: they retrieve text and respond. That is not enough for execution.
| Capability | RAG-only chatbots | MEVA (reasoning-first) |
|---|---|---|
| Answer FAQs | ✓ | ✓ |
| Look up customer data | Sometimes | Always, connected to CRM |
| Take action in your systems | No | Yes: writes to CRM, ERP, PMS |
| Make multi-step decisions | No | Yes, reasoning across data |
| Multi-agent collaboration | No | Yes, shared context |
| Audit and override | Limited | Full HITL controls |
Every vendor claims AI. Every vendor claims automation. The distinctions that matter are architectural.
| Capability | Chatbots | Copilots | Workflow automation | RPA | MEVA |
|---|---|---|---|---|---|
| Understands natural language intent | Partial | Yes | No | No | Yes |
| Executes actions in systems of record | No | No | Partially | Yes (brittle) | Yes, natively |
| Persistent customer memory | No | Session only | No | No | Yes, cross-channel |
| Multi-agent coordination | No | No | No | No | Yes, shared memory |
| Handles edge cases & ambiguity | No | Partially | No | No | Yes, reasoning-first |
| Human oversight & override | Limited | Yes | Limited | Limited | Full HITL |
| Full audit trail | No | Partial | Partial | Partial | Every decision |
| Industry-specific reasoning | No | No | No | No | Yes, vertical models |
Specialised agents, each an expert in their domain, collaborate on shared customer context. One customer. One memory. Multiple agents working as a coordinated operation.
Marketing, Sales, Service, Customer Success, Internal Service, and Social agents, each trained on their domain's workflows, data, and decision logic.
Cart recovery, trial-to-paid conversion, donor reactivation, reservation confirmation, escalation routing: pre-built, configurable, industry-specific task flows.
When a conversation moves from Marketing to Service to Success, customer memory travels with it. The agent always knows what was said, what was promised, and what is pending.
Most AI systems have amnesia. Each session starts blank. MEVA maintains three layers of persistent memory: across channels, agents, and time.
Full conversation history across every channel. What the customer said on WhatsApp last Tuesday. The complaint raised by email six weeks ago. The promo they clicked on the website. All of it, in context.
Structured customer profile derived from CRM, purchase history, support history, and interaction patterns. Sentiment signals, churn risk scores, LTV estimates, available to every agent at inference time.
Workflow templates, escalation policies, brand voice guidelines, and business rules that govern how agents behave. Configurable per industry, per segment, per channel, without retraining.
Everyone claims AI agents and automation. The moat is not a model; it is the orchestration layer.
Read the full defensibility analysis in the architecture white paper →
The full component reference is in the architecture white paper. In brief, MEVA covers: state management across sessions, 80+ intent categories (p95 under 200ms), 50+ bidirectional connectors with transactional rollback, policy governance, model-agnostic orchestration, and AWS and Azure hosting with SOC 2, GDPR, and SSO.
MEVA is built for enterprise accountability. Every agent action is auditable, overridable, and explainable.
Any agent response can be reviewed and edited before it sends, configurable by agent, channel, or risk score.
MEVA detects negative sentiment, high-value signals, and policy edge cases, and escalates to a human in Slack, Microsoft Teams, or Google Chat, with full context pre-loaded.
Every decision, what it read, reasoned, decided, and sent, is recorded in full: searchable, exportable, ready for compliance review.
See the full governance and configuration model in the architecture white paper →
Orchestration without visibility is not governance. MEVA exposes the full operational picture.
Resolution rate, escalation rate, handling time, and satisfaction signals per agent, channel, and industry, in real time.
The full reasoning chain for every decision, searchable by customer, date, agent, or action type.
Every workflow from trigger to close: completion rate, drop-off, write-back success, and latency at p50, p95, p99.
Full observability and audit detail is in the architecture white paper →
Deep dive for technical evaluators: orchestration model, multi-agent coordination, memory architecture, integration fabric, governance framework, and deployment options.
Common questions about the MEVA AI engine
Copilots suggest. MEVA acts. A copilot surfaces a recommendation for a human to execute. MEVA classifies intent, selects the right action, executes the workflow in your CRM/ERP/PMS, and records the decision, without waiting for a human to read a suggestion and decide whether to follow it. The human is still in the loop for oversight and override, but not for execution.
MEVA uses a shared memory model. All specialist agents: Marketing, Sales, Service, Success, Internal, Social, read from and write to a unified customer memory store. When the orchestrator routes a conversation to a new agent, that agent loads the full customer context: session history, CRM profile, open workflows, prior interactions across every channel. There are no data silos between agents.
MEVA is model-agnostic. We use frontier reasoning models (GPT-4 class, Claude 3.5 class, Gemini Ultra class) for orchestration and specialised models for retrieval, classification, and embedding. Enterprise customers can specify model preferences or deploy against private/self-hosted models including Claude, GPT-4, Llama, and Mistral via the MEVA admin console.
Intent classification runs at p95 under 200ms. Most agent responses complete in under 2 seconds. Multi-step workflows that include system writes complete in 4–8 seconds. We measure and report p50, p95, and p99 latency per agent and workflow type in the customer observability dashboard.
AWS (us-east-1, us-west-2, eu-west-1, ap-south-1) and Azure. Customer data residency is configurable by region. SOC 2 Type I certified, GDPR-compliant, end-to-end encryption, SSO via SAML/LDAP. See the Trust Center for full security documentation and DPA.
Architecture deep dives for technical evaluators. Security reviews for procurement. Sandbox access for POC.