Cookie Preferences

    We use cookies to enhance your browsing experience, analyze site traffic, and personalize content. By clicking "Accept All", you consent to our use of cookies. Learn more

    Built on Enterprise Neuro-Symbolic Intelligence

    The First Neuro-Symbolic Virtual Assistant

    The EVO Virtual Assistant is built on a fundamentally new foundation — neuro-symbolic intelligence — combining neural pattern recognition with symbolic reasoning and adaptive control. The result is a conversational experience that is dramatically more accurate, more grounded and more reliable than traditional RAG and LLM-only chatbots.

    Powered by the AsterMind EVO Platform, the EVO Virtual Assistant doesn't merely retrieve documents and pass them to a language model. It maintains persistent state, reasons over context, detects drift and selectively coordinates LLMs only when they add value — reducing LLM API calls by 4-5x while improving answer quality. It performs strongly with or without an LLM, avoiding the brittleness, hallucinations and runaway costs of LLM-only RAG architectures.

    Available in self-hosted and SaaS deployments, and in single-tenant or multi-tenant configurations.

    Powered by EVO Platform Technology

    99% faster AI execution
    90% lower infrastructure
    Adaptive intelligence
    Simulation-driven decisions
    Air-gapped deployment
    Digital clone technology
    Learn why organisations choose EVO →
    Neuro-Symbolic Advantage

    Beyond Traditional RAG

    Traditional RAG chatbots are a thin veneer over a language model. The EVO Virtual Assistant is built on a fundamentally different foundation — neuro-symbolic intelligence — which delivers a measurably superior experience across every dimension that matters.

    Dimension
    Traditional RAG Chatbot
    EVO Virtual Assistant
    Architecture
    Stateless retrieve-then-generate pipeline wrapped around an LLM
    Neuro-symbolic core with persistent state, symbolic reasoning and adaptive control
    Answer Quality
    Fluent but often hallucinated; confidence cannot be trusted
    Grounded, explainable answers with explicit uncertainty and the ability to defer
    LLM Dependency
    Hard dependency — every turn calls an expensive generative model
    Optional LLM coordination, invoked only when it adds value (4-5x fewer calls)
    Context & Memory
    Bounded context window; loses state across turns and sessions
    Persistent operational memory; reasons over time and prior interactions
    Adaptation
    Static — requires re-indexing and re-prompting when content changes
    Self-adapting — detects drift in knowledge and usage patterns continuously
    Cost & Latency
    Unpredictable token costs; latency tied to LLM round-trips
    Bounded, predictable cost and latency; selective LLM invocation
    Deployment
    Cloud-bound; depends on hosted LLM APIs
    Cloud, on-premise or fully air-gapped — works with or without an LLM
    User Experience
    Generic, inconsistent answers; users learn not to trust the bot
    Context-aware, consistent and trustworthy — users get answers that hold up

    A Better Experience, Measurably

    Users get answers that are accurate, grounded and consistent. Operations teams get predictable cost and latency. Security and compliance teams get explainability, auditability and air-gapped deployment. That is what neuro-symbolic intelligence delivers — and what traditional RAG cannot.

    Why Neuro-Symbolic Beats Traditional RAG

    Traditional RAG chatbots are stateless retrieval pipelines bolted onto an LLM — fragile, expensive and prone to hallucination. The EVO Virtual Assistant is a living conversational system that combines neural pattern recognition with symbolic reasoning and adaptive control — delivering measurably better answers, lower cost and a fundamentally better user experience.

    Neuro-symbolic reasoning combines neural pattern recognition with symbolic logic for grounded, explainable answers

    Persistent state and context across conversations — no more amnesia between turns or sessions

    Operates with or without an LLM, eliminating dependency on generative models and their hallucinations

    Selectively coordinates LLMs only when they add value, reducing API calls by 4-5x

    Adaptive control loops detect drift and continuously improve response quality without retraining

    Scales from single organizations to multi-tenant SaaS platforms — cloud, on-premise or air-gapped

    What the EVO Virtual Assistant Is Not

    Not a thin LLM wrapper or prompt-engineered chatbot
    Not a stateless retrieve-then-generate pipeline
    Not dependent on hallucination-prone generative models
    Not a static FAQ bot or scripted assistant

    The EVO Virtual Assistant is designed for adaptive, explainable, and resilient conversational intelligence.

    EVO Virtual Assistant Advantages

    Discover how the EVO Virtual Assistant solves business problems that traditional conversational AI cannot address.

    Undetected Process & Policy Drift

    The Problem

    Organizations gradually drift away from documented policies, SOPs, and security baselines—the primary root cause of audit findings, cybersecurity incidents, and cost overruns.

    Why Traditional Chatbots Fail

    • No persistent baseline of actual behavior
    • No continuous feedback loop
    • Reactive rather than preventative

    How We Solve It

    • Maintains living operational baselines
    • Continuously detects divergence
    • Initiates corrective actions automatically
    • Escalates only when material thresholds are breached

    Fewer audit findings, fewer incidents, and materially lower remediation costs.

    Cross-Department Execution Failures

    The Problem

    Most execution failures occur at departmental boundaries—sales commitments operations can't deliver, legal delays blocking revenue, and finance approvals arriving too late.

    Why Traditional Chatbots Fail

    • No shared organizational state
    • Loss of context over time
    • No modeling of second-order effects

    How We Solve It

    • Maintains organizational memory across systems
    • Tracks cause–effect chains between functions
    • Predicts downstream impacts before commitments are finalized

    Fewer failed deals, fewer fire drills, and improved margin protection.

    Decision Risk from Confident Wrong Answers

    The Problem

    Executives increasingly rely on AI-generated outputs for budgeting, forecasting, and strategy. Hallucinated or overconfident answers introduce real decision risk.

    Why Traditional Chatbots Fail

    • Optimize for fluency rather than correctness
    • Cannot express uncertainty clearly
    • No accountability or governance mechanism

    How We Solve It

    • Explicit uncertainty awareness
    • Ability to refuse or defer answers
    • Validation and decision-governance mechanisms built in

    Reduced strategic error and increased executive trust in AI-supported decisions.

    Manual Exception Handling

    The Problem

    Exceptions consume disproportionate operational effort—invoice mismatches, contract anomalies, and approval bottlenecks that drain resources.

    Why Traditional Chatbots Fail

    • No root-cause resolution
    • No learning loop
    • Human workload remains unchanged

    How We Solve It

    • Identifies recurring exception patterns
    • Recommends systemic process fixes
    • Reduces exception volume over time

    Lower operating costs and faster cycle times.

    Deployment Variants

    Choose the deployment model that fits your infrastructure and compliance requirements

    Most Popular

    EVO Virtual Assistant Server (SaaS)

    A fully managed deployment of the EVO Virtual Assistant operated by AsterMind.

    Typical Use

    • Organizations that want enterprise chatbot capabilities without DevOps overhead

    Key Characteristics

    • Full Neuro-Symbolic Engine
    • Document ingestion and training
    • Local or private-cloud deployment
    • Pluggable LLM support (local or commercial APIs)
    • Secure API key management
    • Local caching to reduce latency and LLM costs
    • Zero infrastructure management
    • Automatic updates and backups
    • High availability and redundancy
    • SOC 2–compliant hosting

    EVO Virtual Assistant Server – Multi-Tenant (SaaS)

    A fully managed, multi-tenant EVO Virtual Assistant platform hosted by AsterMind.

    Typical Use

    • SaaS companies embedding chatbots into their products
    • Enterprises offering chatbot services internally or externally

    Key Characteristics

    • All the features of the EVO Virtual Assistant Server
    • Instant tenant provisioning
    • Per-tenant document training and access control
    • Strong tenant isolation with cryptographic separation
    • Shared system-wide knowledge with tenant-specific overlays
    • Role-based permissions (System Admin, Tenant Admin, Tenant User)
    • Unlimited tenants without per-tenant infrastructure duplication
    • Elastic auto-scaling
    • White-label support
    • Usage analytics for billing and reporting
    • 24/7 monitoring and operational management

    EVO Virtual Assistant Server (Self-Hosted)

    A complete chatbot backend deployed on customer-controlled infrastructure.

    Typical Use

    • On-premise environments
    • Regulated or compliance-sensitive deployments
    • Custom LLM or local model configurations

    Key Characteristics

    • Full Neuro-Symbolic Engine
    • Document ingestion and training
    • Local or private-cloud deployment
    • Pluggable LLM support (local or commercial APIs)
    • Secure API key management
    • Local caching to reduce latency and LLM costs

    EVO Virtual Assistant Server – Multi-Tenant (Self-Hosted)

    An enterprise-grade, multi-tenant chatbot platform operated on customer infrastructure.

    Typical Use

    • SaaS providers
    • Agencies serving multiple clients
    • Enterprises with multiple business units

    Key Characteristics

    • Strong tenant isolation with cryptographic separation
    • Shared system-wide knowledge with tenant-specific overlays
    • Per-tenant document training and access control
    • Role-based permissions (System Admin, Tenant Admin, Tenant User)
    • Unlimited tenants without per-tenant infrastructure duplication

    Where It Fits in the System

    The EVO Virtual Assistant sits at the conversational interface layer, acting as the primary interaction point between humans and organizational knowledge systems.

    Typically Deployed:

    As a customer support interface
    As an internal knowledge assistant
    Embedded within SaaS platforms
    Integrated into complex enterprise applications
    ROI Quantification

    Measurable Return on Investment

    ROI is calculated using existing enterprise data—no proprietary metrics required. Value is anchored to costs executives already price on the balance sheet.

    Anchored to Known Baseline Costs

    ROI is calculated using existing, well-understood data from finance, audit, and operations reporting.

    • Cost of audit findings
    • Cost per cybersecurity incident
    • Downtime and outage costs
    • Exception handling labor costs

    Measured by Delta Reduction

    Focus on measurable change, not absolute performance claims.

    • Reduction in incident frequency
    • Reduction in exception volume
    • Improvement in time-to-detection
    • Faster resolution times

    Proven Enterprise Model

    This ROI logic mirrors how major enterprise platforms were adopted.

    • SAP, ServiceNow, Splunk, UiPath
    • Justified on reduction of known pain
    • No vendor-specific metrics required
    • System-level impact measurement

    Department-Level ROI Mapping

    No AsterMind-internal telemetry is required to justify value. Each department measures ROI using their existing metrics.

    Cyber

    Baseline:

    Mean time to detect

    Lever:

    Drift and anomaly detection

    Operations

    Baseline:

    Exception volume

    Lever:

    Root-cause elimination

    Finance

    Baseline:

    Close cycle time

    Lever:

    Workflow stabilization

    Legal

    Baseline:

    Contract delays

    Lever:

    Cross-system awareness

    Executive

    Baseline:

    Decision error risk

    Lever:

    Decision governance

    Example: Policy Drift Reduction

    If policy drift incidents fall from 12 per year to 3 per year, ROI is immediately calculable using known remediation costs.

    12

    Incidents Before

    3

    Incidents After

    75%

    Reduction

    Executive Perspective

    AsterMind is evaluated on outcomes executives already price. These costs already exist on the balance sheet— AsterMind's value is the measurable reduction of them.

    Fewer Failures

    Reduced incidents and outages

    Fewer Exceptions

    Lower exception handling volume

    Fewer Escalations

    Automated resolution paths

    Better Decisions

    Governance-backed AI outputs

    Supporting Products

    Extend and deploy the EVO Virtual Assistant with these companion products

    Licensed Frontend SDK

    EVO Virtual Assistant Client

    The EVO Virtual Assistant Client is a licensed frontend SDK that connects applications to any EVO Virtual Assistant Server. It provides resilient, high-quality conversational experiences even under degraded network or server conditions.

    When connectivity is lost, the client continues to provide meaningful, knowledge-based responses instead of failing silently or returning errors.

    What It Does

    • Connects web applications to EVO Virtual Assistant Servers
    • Maintains session continuity across reloads
    • Streams responses in real time
    • Caches documents locally for performance and resilience
    • Automatically falls back to local intelligence when offline

    Key Characteristics

    • Online and offline operation
    • Automatic failover to local mode
    • Local RAG engine for offline responses
    • Document and response caching
    • Streaming output for improved UX

    What It Is Not

    • Not a standalone chatbot
    • Not usable without an EVO Virtual Assistant license
    • Not a UI-only widget

    Relationship to Other Products

    Requires a licensed EVO Virtual Assistant ServerServes as the runtime interface between users and the backendCan be extended with the Agentic Add-on
    Action & Automation Layer

    EVO Virtual Assistant Client – Agentic Add-on

    The Agentic Add-on extends the EVO Virtual Assistant Client with the ability to take actions inside applications, not just answer questions.

    What It Does

    • Translates natural language into application actions
    • Navigates interfaces and workflows conversationally
    • Executes high-confidence actions automatically
    • Requests confirmation for ambiguous actions
    • Routes low-confidence intent back to knowledge responses

    Key Characteristics

    • Confidence-aware action execution
    • Visual previews before execution
    • Human-in-the-loop safeguards
    • Extensible custom action framework

    What It Is Not

    • Not an unsupervised automation engine
    • Not a brittle RPA ruleset
    • Not allowed to execute low-confidence actions blindly
    Free Deployment Accelerator

    EVO Virtual Assistant Template

    The EVO Virtual Assistant Template is a free, production-ready UI layer designed to accelerate deployment of EVO Virtual Assistant experiences. It is provided as a supporting asset, not a standalone product.

    What It Does

    • Provides a polished, accessible chat interface
    • Enables rapid deployment with minimal configuration
    • Supports both React-based and script-tag integration
    • Adapts visually to customer branding

    Key Characteristics

    • Pre-built chat UI components
    • Mobile responsive and accessible (WCAG 2.1 AA)
    • Theming and dark-mode support
    • Works with any frontend stack

    What It Is Not

    • Not a licensed product by itself
    • Not a backend or intelligence layer
    • Not required if customers build their own UI

    Relationship to Other Products

    Requires the EVO Virtual Assistant ClientRequires a valid EVO Virtual Assistant licenseIntended to accelerate adoption, not replace custom UI

    Product Relationships

    How the EVO Virtual Assistant ecosystem fits together

    EVO Virtual Assistant

    Core conversational intelligence

    EVO VA Client

    Licensed runtime interface

    Agentic Add-on

    Action and automation layer

    Chatbot Template

    Free deployment accelerator

    Pricing

    Flexible monthly plans for the EVO Virtual Assistant. Start small and scale to enterprise volumes — every tier includes the complete EVO Platform.

    Entry tier

    Starter

    $350 /mo

    25,000 messages / mo

    ~7,000 sessions

    Small teams, initial deployments, single-product pilots

    • Neuro-symbolic AI technology
    • Unlimited documents
    • Embeddable widget (REST / SSE / WebSocket)
    • Encryption at rest and in transit
    • 99.9% uptime SLA, automatic updates, secure hosting
    • BYOLLM — OpenAI, Anthropic, AWS Bedrock, Ollama
    • Translation API — 18 languages with glossary enforcement
    • 1 tenant and 1 user
    • Email support
    • Priority support
    Signup Now
    Most Popular

    Most popular

    Professional

    $1,499 /mo

    ~175,000 messages / mo

    50,000 sessions

    Growing organizations with moderate usage

    • Neuro-symbolic AI technology
    • Unlimited documents
    • Embeddable widget (REST / SSE / WebSocket)
    • Encryption at rest and in transit
    • 99.9% uptime SLA, automatic updates, secure hosting
    • BYOLLM — OpenAI, Anthropic, AWS Bedrock, Ollama
    • Translation API — 18 languages with glossary enforcement
    • 1 tenant, unlimited users
    • Email support
    • Priority support
    Signup Now

    Mid-market

    Business

    $3,499 /mo

    ~525,000 messages / mo

    150,000 sessions

    Mid-market companies with high-volume needs

    • Neuro-symbolic AI technology
    • Unlimited documents
    • Embeddable widget (REST / SSE / WebSocket)
    • Encryption at rest and in transit
    • 99.9% uptime SLA, automatic updates, secure hosting
    • BYOLLM — OpenAI, Anthropic, AWS Bedrock, Ollama
    • Translation API — 18 languages with glossary enforcement
    • Max 3 tenants, unlimited users
    • Email support
    • Priority support
    Signup Now

    Enterprise scale

    Enterprise

    Contact Us

    More then 1M messages / mo

    250,000 sessions / mo

    Large organizations with enterprise-scale demands

    • Neuro-symbolic AI technology
    • Unlimited documents
    • Embeddable widget (REST / SSE / WebSocket)
    • Encryption at rest and in transit
    • 99.9% uptime SLA, automatic updates, secure hosting
    • BYOLLM — OpenAI, Anthropic, AWS Bedrock, Ollama
    • Translation API — 18 languages with glossary enforcement
    • Unlimited tenants and users
    • Email support
    • Priority support
    Contact Sales

    Session and message definitions

    • Session: A single continuous user conversation. A new session starts when the user is idle >30 minutes or explicitly starts a new conversation.
    • Message: A single user turn OR a single AI response within a session.
    • Both are metered monthly; unused allowance does not roll over.

    Overage rates

    CommitmentOverage rate
    Month-to-month$0.03 per session
    Annual prepay$0.02 per session

    Experience the Neuro-Symbolic Difference

    See why neuro-symbolic intelligence delivers a fundamentally better conversational experience than traditional RAG