The questions buyers, architects and admins actually ask.

x101 is an enterprise AI platform built by VisionWaves: a single intelligence layer that sits between your people and all of your existing systems, including ERP, CRM, ITSM, document repositories, databases and custom APIs. It does not replace those systems; it sits across them. Teams ask questions, retrieve knowledge and execute multi-system actions in plain language or voice, and x101 returns sourced answers or completed actions, governed on each request. See the Platform page for the full architecture.

x101 brings agentic orchestration, agentic workflow automation, enterprise knowledge retrieval, deep research, voice agents and a no-code Agent Studio into one platform, all reachable from a single Command Centre. Teams use it across IT, security, CRM, finance, HR and telecom for tasks that span several systems at once. Representative solutions are on the Use Cases page .

Each request flows through six governed stages: Ask, Analyse, Plan, Execute, Verify and Deliver. Intent and identity are resolved first, permission checks run before any data is retrieved, work is executed across systems in parallel, the output is verified against source data and policy before it surfaces, and the full chain is captured in an immutable audit trail. Each stage is explained on the Platform page .

x101 is system-agnostic and model-agnostic. Rather than being tied to one vendor's cloud or one large language model, it unifies all enterprise systems under a single governed layer, with RBAC/ABAC enforced before retrieval, PII masking, output guardrails and a signed, immutable audit trail. You choose the deployment environment and the underlying model, so there is no ecosystem or inference lock-in. The comparison on the home page sets it against ecosystem copilots, enterprise search and workflow platforms.

x101 is the enterprise intelligence layer within the VisionWaves family. Several siblings build on or alongside it: OpsSingularity (Sentinel AI) for IT and security operations is built on x101, while Enterprise Singularity provides the broader operating layer, NetSingularity owns telecom OSS/BSS, Code Singularity delivers complete systems, and DataByte handles data engineering. The shared traits across the family are consistent: governed, any cloud or on-premises, no lock-in, with AI agents at the centre.

x101 is not locked to any single LLM provider. It works with Anthropic Claude, OpenAI GPT, Google Gemini, Mistral and on-premises models, and the model can be swapped through configuration. That means the platform can adopt newer or more cost-effective models as they appear, without re-architecting.

Yes. Because x101 is model-agnostic, it can run against on-premises and open-weight models as well as hosted commercial ones. This is what makes a fully air-gapped deployment possible: the inference can stay entirely inside your environment.

The model is a configuration choice, not a hard dependency. Orchestration, retrieval, governance and connectors all sit above the model layer, so swapping or mixing models does not require rebuilding agents or workflows. The investment stays in the platform, not in any one vendor's model.

Answers are grounded in retrieved source content rather than the model's memory alone, and they are returned with citations to the exact document, page and version. The Verify stage cross-checks output against source data and policy before anything is surfaced, so unsupported claims are caught before they reach the user.

Latency and throughput are determined by the model and inference provider in use, the level of reasoning required, and the complexity of the task. As a general guide, simpler queries typically return in around two seconds, while complex operations that need multiple agents to orchestrate and correlate across systems can take up to a few minutes.

x101 uses agentic retrieval-augmented generation (RAG) across repositories such as SharePoint, Confluence, PDFs and wikis. Retrieval is permission-checked against the requesting user, so people only see what they are entitled to see, and each answer is grounded in its source with page-and-version citations that can be opened and verified.

An orchestrator decomposes a task, dispatches a team of specialist agents to work in parallel across different systems, and then synthesises their results into one answer. Coordinating work this way keeps quality high on large, multi-step jobs that would otherwise exceed a single model's context window.

Yes. The no-code Agent Studio is a visual builder where a business user configures an agent's persona, tools, memory and guardrails, and connects it over native MCP and A2A standards. Agents can be built and shipped without writing code, while developers can extend them through the SDK.

Deep Research runs automated, multi-step research that scans many sources in parallel and returns a cited, formatted deliverable in minutes rather than a list of links. Each line of enquiry runs with its own context, which keeps quality uniform across a large body of material instead of degrading as the work grows.

Re-indexing runs on a schedule that you define. Each connected source can be set to refresh on the cadence that suits how often its content changes, so the knowledge base stays as current as your data requires.

Governance is enforced on each request, not bolted on afterwards. RBAC/ABAC permission checks run before any data is retrieved, PII can be masked in line with policy, input and output guardrails validate safety, and each step is recorded in a signed, immutable audit log. VisionWaves maintains an information security management system aligned to ISO/IEC 27001:2022.

x101 evaluates role-based and attribute-based access control before retrieval, during the Analyse stage, and it acts only within the permissions of the requesting user. A person can never retrieve or act on data they are not entitled to, even through a conversational or voice interface.

The audit trail captures the key metrics for each step of a request, including the tokens used, the cost, the model invoked and the time taken, alongside user-level attributes such as who made the request. Each action is recorded in a signed, immutable log, built for compliance review and for replaying exactly what happened.

Yes. Conversation data is isolated between tenants and stored in per-client repositories and persistence, so one customer's data is never co-mingled with another's. No customer or user data is used to train the underlying models. The Data Processing Agreement sets out the processing terms.

x101 runs on any public cloud (AWS, Azure, GCP), in a private cloud, on-premises, or in a fully air-gapped environment. This makes it suitable for organisations with strict data residency, isolation or sovereignty requirements.

x101 is designed to scale horizontally, and its orchestration model runs specialist agents in parallel. Large, multi-step jobs keep a uniform quality as volume increases, rather than degrading on a single long context.

Because x101 deploys against existing systems, data and governance rather than requiring a rebuild, a pilot can typically start in weeks and then roll out at the organisation's own pace. The exact timeline depends on the number of systems connected and the governance review involved.

x101 runs on any public cloud as well as private environments, deployed on Kubernetes (K8s). This keeps it portable across public cloud, private cloud and on-premises infrastructure without re-architecting.

x101 connects to enterprise applications such as ERP, CRM and ITSM, to SQL and other databases, to document repositories, to email and to SaaS tools, over more than 1,000 connectors and native MCP and A2A standards, plus APIs and SQL. It can read, write and act across these systems within the permissions of the requesting user. Browse the full list on the Connectors page .

MCP (Model Context Protocol) is an open standard for connecting AI models to tools and data sources. A2A (Agent-to-Agent) is an open standard that lets agents discover, talk to and delegate work to each other across vendors. x101 supports both natively, which keeps integrations standards-based and portable rather than proprietary. Both terms are defined in the Glossary .

Yes. Beyond the pre-built connector library, x101 provides an SDK together with native MCP and A2A support for connecting custom, in-house or legacy systems. Coverage is not limited to the systems that ship with the platform.

More than 1,000 connectors are available, spanning enterprise applications, databases, document repositories, communication tools and SaaS services. The current catalogue, grouped by domain and searchable, is on the Connectors page .

x101 ships with a set of platform components, one of which is User Management. It provides role-based access control (RBAC), attribute-based access control (ABAC) and AoA, so administrators can manage users, roles and access policies at scale from within the platform.

Reach out through the Contact page , where the Singapore, USA and India offices are listed by phone and email, and the team typically responds within one business day. Because x101 deploys against existing systems, data and governance, a pilot can usually start in weeks and then roll out at your own pace.