NeIO itself is not tied to a single LLM. It is designed as an LLM-agnostic platform that orchestrates interactions with one or more LLMs chosen by the enterprise. Data handling depends on how the customer configures the LLM endpoint: traffic can be routed securely through private connections, and the enterprise retains full control of its data. NeIO does not store or train the base LLM with customer data by default.

NeIO can be deployed in the customer’s own cloud environment, on-premises, or in a hybrid setup. When connected to an external or managed LLM, all data traffic passes through customer-controlled endpoints. This ensures that sensitive information remains within the enterprise’s security boundaries, while the LLM provides processing without exposing data outside the authorized environment.

Yes, NeIO provides an orchestration layer that connects your data sources, enterprise knowledge, and workflows to one or more LLMs. This enables contextual retrieval, structured reasoning, and automated actions without requiring manual intervention, ensuring that the LLM is applied intelligently within enterprise processes.

NeIO is compatible with multiple LLMs, including managed cloud models like Azure OpenAI, AWS Bedrock, and Vertex AI, as well as on-premises or self-hosted models. Accuracy depends on the LLM choice and the type of document being processed; NeIO allows enterprises to benchmark models on representative data to determine the best fit for their specific use cases.

NeIO can integrate with open-source LLMs deployed within the enterprise’s infrastructure. While NeIO itself does not automatically train models on customer data, enterprises can leverage existing fine-tuned models or train open-source LLMs externally and connect them to the platform for processing and workflow automation.

NeIO does not include proprietary LLM datasets. Enterprises can bring their own fine-tuned or pre-trained models, and NeIO will integrate them into the platform for tasks such as retrieval, summarization, or document classification.

NeIO supports template-based workflows and modular pipelines, so enterprises can reuse pre-configured extraction and processing logic. While models themselves remain separate per customer for data privacy, the platform’s workflow designs and retrieval pipelines can be adapted and reused across projects.

While document parsing is one component, orchestration is necessary to:

• Combine multiple LLMs for different tasks (e.g., summarization, classification, question answering)

• Integrate enterprise data from multiple sources securely

• Apply business rules and workflow logic

• Ensure outputs are validated, actionable, and delivered to the right stakeholders in real time
  This orchestration allows the LLM to provide context-aware, enterprise-ready outputs rather than isolated results.

Yes. NeIO is designed to be modular and extensible. Enterprises can add support for new file types, structured data, and domain-specific documents over time, while leveraging the same retrieval, reasoning, and workflow automation capabilities. This ensures scalability as enterprise needs evolve.

NeIO integrates retrieval validation, metadata enrichment, and source citations. Every output can be traced back to the original documents or data sources, reducing the risk of hallucinations and ensuring the answers are explainable, auditable, and actionable

Yes. NeIO is LLM-agnostic and decoupled from any single provider. Enterprises can reconfigure which LLMs are used without redeploying the platform, allowing flexibility as new models are introduced.

NeIO consolidates data in Knowledge Spaces and enriches it with metadata before sending it to the LLM. This ensures that queries are context-aware and consistent, even when sourcing data from multiple systems or file types.

Yes. All outputs include citations to source documents. The platform’s retrieval-augmented generation (RAG) pipeline and validation layers ensure that responses are traceable and verifiable.

NeIO unifies structured data (databases, spreadsheets) and unstructured data (PDFs, Word documents, emails) in Knowledge Spaces. This allows LLMs to process all relevant enterprise data efficiently and generate integrated insights.

Documents are chunked and embedded into vector stores, enabling fast retrieval and incremental processing. This approach allows LLMs to handle large documents without losing context or accuracy.

Yes. Enterprises can select lighter, faster models for routine tasks and larger, higher-accuracy models for critical outputs. Task-level routing ensures the right balance of speed and quality for each use case.

Yes. NeIO supports staging environments where different LLMs can be benchmarked using sample documents or queries. This allows enterprises to evaluate accuracy, speed, and cost before production deployment.

LLM outputs can be routed directly into Enterprise Assistance (Chat), NeIO Pulse alerts, and Agent workflows, enabling automated actions, real-time notifications, and downstream processing without manual intervention.

Yes. NeIO is modular and extensible. New document types, structured/unstructured data sources, and workflow scenarios can be added over time, allowing the platform to grow with the enterprise’s evolving needs.

NeIO integrates with enterprise identity providers (e.g., Azure AD/Entra ID, Okta) and uses role-based access control (RBAC). Access to Knowledge Spaces, connectors, and workflows is controlled at a granular level, ensuring only authorized users can view, edit, or manage data.

Yes. NeIO supports OIDC/SAML-based SSO, aligning user access with enterprise identity systems and enabling seamless authentication across desktop and mobile platforms.

Sensitive information is stored in customer-managed databases, object storage, or vector DBs. Data in transit and at rest can be encrypted, and optional caching layers (e.g., Redis) are controlled within the enterprise network. NeIO itself does not train LLMs on customer data unless explicitly configured by the enterprise.

Yes. NeIO supports on-premises, private cloud, and hybrid deployments. All traffic can remain inside the customer network, with optional private endpoints for connectors and LLMs, ensuring compliance with data residency requirements.

Secrets can be stored in Kubernetes Secrets or external secret management systems (e.g., Azure Key Vault, AWS Secrets Manager, HashiCorp Vault). Certificates are mapped to ingress controllers to secure external access.

NeIO and Codvo delivery processes align with enterprise security controls and frameworks such as SOC 2 and HIPAA. Deployments can be configured to meet organizational baselines, including data residency, encryption, and access policies.

Container images are scanned during CI/CD, signed, and pulled from customer-approved registries. GitOps provides change auditability and drift detection, ensuring that deployed software matches approved configurations.

NeIO relies on RBAC, SSO, and tenant-level controls. Additional security measures like network isolation, TLS encryption, and secret management reduce exposure, though specific incident response processes would be defined per enterprise deployment.

Yes. Enterprises can delete Knowledge Spaces or individual documents, and configure access restrictions per Knowledge Space, ensuring full control over which users or roles can access specific data.

Yes. NeIO supports live sync from cloud drives, email systems, and databases, ensuring that Knowledge Spaces are always up to date and queries return the latest data.

NeIO consolidates data into Knowledge Spaces, converts documents into vector embeddings, enriches them with metadata, and applies RAG-based retrieval to enable fast, accurate, and contextual search across structured and unstructured data.

Yes. NeIO’s RAG pipeline and multi-hop reasoning allow the platform to process complex queries spanning multiple documents and Knowledge Spaces, providing consolidated and actionable answers.

Access is managed through role-based access control (RBAC), with roles such as Admin, Contributor, Reader, and custom roles. Permissions can be applied at the Knowledge Space or workflow level, ensuring that users only see and interact with data they are authorized to access.

Absolutely! Settlement Demand AI’s interface is user-friendly and intuitive, designed for both technical and non-technical users. You can create custom reports, dashboards, and visualisations without any coding knowledge.

NeIO integrates with enterprise observability stacks (Prometheus, Grafana, ELK/EFK, Azure Monitor) to track latency, token usage, cache hit ratios, queue backlogs, and error rates. This enables proactive performance monitoring and system tuning

NeIO uses a cloud-native, microservices architecture deployed on Kubernetes. Stateless services scale horizontally, while stateful data is managed in high-performance databases and vector stores, ensuring consistent response times even under heavy load.

NeIO employs Horizontal Pod Autoscalers (HPA) for microservices, separate node pools for varying workloads, and optional KEDA for event-driven scaling, adapting resource allocation dynamically to demand.

Document embeddings are generated in seconds to minutes depending on file size. Once indexed, retrieval queries are near real-time, typically delivering search and chat responses in 6–8 seconds for structured and unstructured data.

NeIO uses multi-replica services, PodDisruptionBudgets, and spreads workloads across nodes or zones. Managed databases and vector stores provide HA options, ensuring minimal downtime and reliable service.

Yes. Codvo collaborates with customers to adjust node pools, autoscaling parameters, and resource allocations based on SLAs and workload patterns for optimal performance.

Documents in Knowledge Spaces are processed asynchronously into vector embeddings. Optional GPU pools accelerate OCR and vision-heavy workloads, improving indexing speed and search performance.

By using the RAG pipeline combined with vectorized Knowledge Spaces and pre-fetched context, NeIO reduces latency and provides decision-ready responses efficiently, even for multi-step queries.

NeIOPULSE is designed for sub-second KPI/event ingestion and seconds-level notification delivery. Policies can be tuned for priority routing and escalation to meet enterprise SLAs.

Yes. Staging environments can run synthetic and scenario-based performance tests. Node pools and autoscaling are sized based on test results to ensure production-grade performance.

Metrics are collected via Prometheus, dashboards in Grafana, and traces via OpenTelemetry. Performance issues trigger alerts, enabling rapid investigation and tuning.

NeIO supports file sizes up to 10MB per document in Knowledge Spaces. Parallel processing, vector embeddings, and caching mechanisms ensure performance even with large document volumes.

The RAG pipeline allows NeIO to retrieve and consolidate data across multiple Knowledge Spaces, databases, and connectors efficiently, applying filters and metadata enrichment to improve query speed.

GitOps and Kubernetes rolling updates enable near-zero downtime. Argo CD ensures cluster state consistency, and previous releases can be restored quickly without impacting ongoing operations.

NeIO can optionally use dedicated GPU pools for vision/OCR models or large LLM inference tasks. Resource allocation is configurable to balance cost, speed, and throughput requirements.

Search/RAG queries generally complete in 6–8 seconds; KPI/event notifications are typically sub-second for ingestion and seconds-level for delivery. SLAs can be tuned per customer based on infrastructure and business requirements

NeIO supports customer-managed cloud (Azure AKS, AWS EKS, GCP GKE), on-prem (Kubernetes including Red Hat OpenShift), and hosted SaaS deployments by Codvo for select use cases.

Yes. NeIO can be deployed fully inside customer networks using Helm charts and containers. External LLM or connector access can be routed through approved endpoints or fully on-prem models.

Yes. The platform can run in one cloud or on-premises while securely connecting to data in other clouds via private links, VPC/VNet peering, or service bus patterns.

Core components include: NeIO Space (data ingestion & processing), NeIO Agent (query planning & actions), NeIO Pulse (alerts/KPIs), and shared services for authentication, messaging, scheduling, observability, and storage.

Integration is via connectors and MCPs for databases, data lakes, CRMs, ITSMs, file systems, queues, and webhooks. All connections are environment-specific and credentials are customer-managed.

Yes. Enterprises can configure new connectors using the NeIO connector SDK/patterns while maintaining control over credentials and network access.

NeIO continuously monitors connected sources and updates Knowledge Spaces with live sync, ensuring that enterprise data remains current and consistent for searches and queries.

All integrations respect role-based access controls (RBAC) and SSO policies. Data access is scoped per user or service account to prevent unauthorized access.

Yes. APIs and SDKs are available for embedding NeIO into enterprise apps, extending functionality, and connecting with external systems while maintaining security and performance.

Yes. Pre-built connectors exist for Google Drive, OneDrive, AWS S3, and enterprise email systems. Additional cloud storage or custom connectors can be configured as needed.

Integrations are monitored with metrics, logs, and alerts. Failed syncs or errors trigger notifications and can be remediated through GitOps workflows and operational runbooks.

Yes. The platform can retrieve and consolidate data across multiple Knowledge Spaces, databases, and tagged documents to provide holistic, enterprise-ready answers.

NeIO’s architecture supports millions of documents, multiple Knowledge Spaces, and concurrent integrations. Scaling is achieved via Kubernetes autoscaling, node pools, and optimized connector pipelines