Enterprise-ready capabilities for governed AI adoption.
Integrate with your enterprise identity provider to enforce group and role-based security rules. Control exactly who can access which AI models, tools, and knowledge bases.
Seamlessly integrate with OpenAI, Anthropic, AWS Bedrock, and more. Switch between models, compare outputs, and route requests to the optimal provider — all from one chat interface.
Enforce enterprise data security standards with customizable AI guardrails. Define what data can be shared, set PII detection rules, and tailor privacy policies to your compliance needs.
Use our SDK to embed Jarvis Chat directly into your existing applications, portals, and internal tools. Give every team a governed AI experience without leaving the products they already use.
Leverage intelligent automation through AI-driven agents. Automate routine tasks, orchestrate complex workflows, and free your teams for strategic work.
Centralize organizational knowledge and make it instantly accessible through AI. Upload documents, connect data sources, and get accurate answers grounded in your enterprise data.
Watch Jarvis Chat tackle real-world enterprise challenges.
Enterprise knowledge retrieval with governed AI
Natural language queries across your databases
Intelligent document processing automation
AI-powered business insights and reporting
See how Jarvis Chat can transform your team's AI experience with governance built in.
Jarvis Chat is a governed multi-model workspace that runs inside your own cloud account, so teams get a single AI interface without the data-boundary problem that comes with public chat tools. These 6 rows cover what security review asks first.
| Question | Answer |
|---|---|
| Where does it run? | Your Kubernetes cluster on 1 of 3 clouds — Amazon EKS, Azure AKS, or Google GKE. No prompt leaves your environment. |
| Which models? | Multiple providers behind 1 interface, switchable per conversation rather than fixed at deployment time. |
| Who can use what? | Azure EntraID sign-in with RBAC, plus ACL rules scoping individual models and sources to named users or teams. |
| What can it read? | Uploaded documents and connected internal sources, so answers are grounded in your content, not public web data. |
| Can it be embedded? | Yes — the interface can be deployed into an existing internal portal or application rather than as a separate destination. |
| What gets logged? | Every prompt, response, model selection, and citation, retained in your environment for audit and SIEM ingestion. |
A governed workspace is defined by where it runs and what it is allowed to reach. These rows fix both, and name the standards behind identity, transport, and tool access so each claim can be checked at source.
| Specification | Value |
|---|---|
| Delivery model | Licensed, customer-hosted software — 0 shared control plane |
| Runtimes | 3 managed Kubernetes services — Amazon EKS, Azure AKS, Google GKE |
| Model providers | Multiple behind 1 interface, switchable per conversation |
| Managed runtime | Amazon Bedrock among the supported model backends |
| Caller identity | Azure EntraID via OpenID Connect Core 1.0; SAML 2.0 supported |
| Authorization | OAuth 2.0 (RFC 6749) with Bearer usage per RFC 6750 |
| Token format | JSON Web Token (RFC 7519) |
| Access granularity | 1 ACL entry per model and per knowledge source |
| Knowledge sources | 12 supported source types, retrieval bound to caller identity |
| Tool access | Model Context Protocol (MCP) over JSON-RPC 2.0 |
| Embedding options | 3 — JavaScript SDK, Web Components, and iFrame |
| Guardrails | 100+ prebuilt policies across 4 control layers |
| Transport security | TLS 1.2 minimum, TLS 1.3 preferred (RFC 8446) |
| Audit record | Every prompt, response, model choice, and citation, retained in-account |
| Procurement | 2 marketplaces, 3 standard tiers, USD 18,000–60,000 |
| Customer rating | 5 out of 5 from 1 verified AWS Marketplace review |
A 1-minute introduction to the governed workspace: how a team reaches multiple models through 1 interface, and where the access policy sits between the user and the model.
A first workspace is normally live for a pilot team well before any organisation-wide announcement. These 5 steps are the sequence ASCENDING follows on customer deployments, and each is independently reversible.
Procure through a marketplace. Subscribe on AWS Marketplace or Azure Marketplace so the licence draws down existing cloud commitment.
Deploy to your cluster. Install into an existing EKS, AKS, or GKE namespace in the region where the source data already lives.
Connect identity. Bind Azure EntraID and map existing security groups onto roles and per-model entitlements.
Attach knowledge. Upload the first document set or connect an internal source, then confirm citations resolve to the right records.
Set guardrails, then invite users. Enable PII redaction and topic policy, review the first week of audit logs, and widen access from there.
Two design decisions separate a governed workspace from a wrapper around one model API: which providers a team can reach, and where the interface actually appears for users.
These are the questions that decide whether a governed workspace replaces the public AI tools employees are already using.
The difference is where the data goes. This workspace is licensed software deployed into your own cluster, so prompts, responses, and uploaded documents never leave your account.
Everything else follows from that: identity-bound access, per-team model entitlements, and an audit log you own rather than one you request from a vendor.
Yes. ACL entries scope models individually, so a research team and a support team can be entitled to different providers under the same deployment.
Model choice therefore becomes a policy decision rather than a procurement decision repeated per team.
The enterprise knowledgebase lets teams upload documents and connect internal sources, and answers cite the records they came from rather than generic web content.
Retrieval is identity-bound, so a user only ever gets answers from sources they are already permitted to read.
Yes. The workspace can connect to Jarvis Registry to discover and invoke governed agents and MCP tools, which is how a conversation turns into an action.
Neither product requires the other — they are independent, and either can be deployed first.
The workspace is one half of the platform. These pages cover the governance layer behind it and the detailed question set.
ASCENDING is an AWS Advanced Tier Services Partner with the AWS Generative AI Competency, and builds Jarvis as licensed software that runs inside your own cloud account. The sources below cover the model runtime and the control frameworks referenced above.
