AI impact intelligence
Where can AI transform our business?
As AI moves from hype to impact, the answers leaders need sit under the surface of how it's actually being used.
The impact gap
AI adoption without visibility is a liability.
Seats are deployed, tokens are billed, slides get made. What no one has is grounded evidence of where AI is actually changing the business, whether the investment is paying off, and how exposed the firm really is.
Opportunity
You can't see where AI is quietly transforming the work.
The highest-value patterns aren't in a deck. They're in what people repeatedly reach for AI to do, where adoption is concentrated, and where it's absent. Without that view, the transformation roadmap is someone's intuition.
Value
Tokens and seats aren't the metric. Insight is.
Usage reports tell you how much, not whether it mattered. Surveys and self-reporting tell you what people remember to say. Leadership needs evidence of value that isn't filtered through the layer selling the programme.
Appetite
Nobody has a current answer to “how exposed are we?”
AI risk is scattered across endpoints, providers, and ad-hoc spreadsheets. By the time you've triangulated it, the answer is already stale. Boards and regulators don't accept stale.
Introducing ARIS
Organisational context. Conversation context.
One source of truth for AI impact.
ARIS captures how AI is actually used inside your firm and grounds it against a live understanding of your organisation. Evidence, not anecdotes. Answers to where AI is transforming work, whether you're getting value, and how exposed you are right now.
How ARIS works
From AI usage data to evidence-grounded answers.
Three layers that turn every AI session into a transformation and risk signal. No surveys. No self-reporting. No data science team required.
Step 1
Discover. Capture every AI interaction.
Full session telemetry across the surfaces your people actually use. Prompts, responses, tool calls, and context. The foundation that everything downstream is grounded in.
What we capture per session:
Prompts and responses
Full conversation, tool calls, and returned context.
MCPs and skills invoked
Which connectors and skills the agent reached for.
CLI permission mode
Default, accept-edits, yolo. Flagged when unsafe.
Environment variables
Keys, endpoints, proxy bypasses, personal tokens.
Identity and surface
User, team, tool (Claude Code, Codex, Cursor, n8n).
Step 2
Assess. Structure the risk posture.
Every session is classified and scored against your policies so the firm's AI risk posture lives in one place and is always current. Boards, regulators, and oncall all read the same answer.
Scoring uses GRASP: five dimensions that turn vague risk intuition into a structured, comparable profile for every agent and session.
Step 3
Understand. Ask anything, get evidence.
Natural-language intelligence linking AI usage to human capital transformation. Every answer traces back to sessions: grounded, auditable, and unfiltered by the layer that sold you the programme.
Usage intelligence, adoption mapping, and risk queries live in the same surface so leaders stop asking three different tools three different versions of the same question.
How we compare
Built for where AI is actually running.
Governance platforms audit policies. DLP tools watch the browser. Observability stacks need instrumentation. ARIS captures session-level evidence from the agentic AI where real work happens, and runs entirely in your environment.
| Provider | Source-truth discovery | CLI agents | Workflow coverage | Risk scoring | Session querying | Adoption intelligence | Policy-breach detection | On-prem deployment |
|---|---|---|---|---|---|---|---|---|
| ARIS | ||||||||
| Nudge Security | ||||||||
| Harmonic Security | ||||||||
| Datadog | ||||||||
| Dynatrace | ||||||||
| Kong AI Gateway | ||||||||
| LiteLLM | ||||||||
| Fluency |
Comparison reflects publicly documented capabilities as of April 2026. Scroll sideways on narrow screens.
Your data. Your environment.
Self-hosted by design.
No data leaves your organisation. ARIS runs in your environment, analyses locally, and is built for the enterprise from day one.
Deployment
Fully self-hosted via Docker or Kubernetes inside your MDM.
Data privacy
No cloud services, no telemetry to Ryora. Your data never leaves your environment.
Compliance
SOC 2 Type II in progress. Built for regulated enterprises from day one.
Access control
SSO and RBAC enabled. Read-only access modes available for auditors.
The pilot
4 weeks. Two themes. Four outcomes.
A structured engagement that leaves you with grounded answers to how AI is transforming your work, and a current, queryable view of how exposed you are.
Transformation
Usage intelligence
Ask anything about AI usage in plain English. Grounded, evidence-linked answers. No surveys. No self-reporting.
Transformation
Adoption mapping
See where AI usage is concentrated, where it's absent, and how adoption patterns vary across the org, validated against your org chart.
Risk
Session visibility & risk scoring
Every session captured, classified, and scored across five AI surfaces at full or compliance-bounded depth.
Risk
Structured risk posture
All risk data in one place so “how exposed are we?” always has a current answer.
The roadmap
Week 1
Deploy & configure
- MDM agent deployment
- SSO/IDM integration
- Inference endpoint connection
- Baseline configuration
Week 2
Capture & calibrate
- Session telemetry across 5 surfaces
- Risk scoring calibration
- Adoption data collection
- First data review with IT
Week 3
Analyse & validate
- Usage intelligence queries live
- Adoption heatmap build
- Daily user engagement begins
- Evidence trails validated
Week 4
Prove & present
- All outcomes validated
- Leader sign-off on accuracy
- Executive findings presentation
- Go/no-go recommendation
Stop guessing about AI impact
In 4 weeks, have grounded answers to the questions your board keeps asking.
Where AI is transforming the work. Whether the investment is paying off. How exposed the firm really is. Evidence, not anecdotes.
Book a Pilot Assessment4-week pilot · Ryora available daily · Executive-ready findings
Frequently asked questions
Positioning
Isn't this just AI observability?
No. Observability platforms like Datadog and Dynatrace are built for developers instrumenting their own LLM applications. They need code changes, they focus on latency, cost, and quality signals, and they deliver dashboards for engineers.
ARIS captures session-truth from the AI tools your whole organisation is already using: CLI agents, IDEs, workflow engines, browser chat. No application instrumentation. The output is leader-level intelligence on adoption, value, and risk, not a latency graph.
Isn't this just DLP or an AI policy gateway?
Tools like Harmonic, Kong, and LiteLLM enforce rules at the browser or the API gateway. ARIS isn't a replacement. It makes those enforcement tools actually effective.
We hand them session-level context (who, what tool, what risk tier, which MCPs, what data) that turns a blunt prompt filter into a precise policy decision. ARIS observes and scores. You enforce using the tools you already have, with context they have never had before.
How is this different from native Claude or OpenAI admin dashboards?
Vendor dashboards show you vendor-shaped metrics: seats, tokens, chats per team. Useful, but each one lives in its own pane and each one speaks its own language.
ARIS is a single pane across every AI surface (vendor-hosted chat, CLI agents, IDEs, workflow engines) with a consistent data model, a shared risk framework, and actual insights. Where is transformation concentrated. Which agents are running with weak safeguards. Where usage has stalled. Evidence and patterns, not a token count.
Deployment and coverage
How much changes in our environment?
End-to-end in week 1. ARIS deploys via Docker or Kubernetes inside your MDM. SSO and IDM hookup. A small host daemon on CLI agent hosts captures session telemetry. Browser chat surfaces use a compliance-bounded capture adapter configured via MDM. Inference endpoint hookup for the analysis layer. That is the whole footprint.
Our AI tool isn't on the coverage list. Now what?
Full-telemetry coverage today includes Claude Code, Codex, Gemini CLI, Cursor, Kimi Code, and n8n. Compliance-bounded coverage includes ChatGPT and Claude Desktop.
New integrations ship roughly weekly: new capture adapters, new workflow engines, new IDEs. If the surface you care about isn't there yet, we scope it into your pilot.
Data and governance
Is this a surveillance tool? What about GDPR and works councils?
ARIS is a governance and intelligence system, not a performance-monitoring tool. Default views are aggregated to team and function. Individual-level evidence exists (it has to, for risk investigations and compliance workflows) but access is RBAC-gated, audit-logged, and purpose-bound.
For EU pilots we include DPO and works-council briefing templates and deploy with data-minimisation defaults. Worker representatives review scope before capture starts.
What about PII, secrets, or credentials captured inside prompts?
Everything stays inside your environment. ARIS is self-hosted and sends no telemetry externally. At capture time you define redaction rules per surface and per team.
Environment variables and tokens are flagged automatically. Captured session data inherits your existing classification, retention, and access controls. Nothing about session handling is invented; it plugs into how the rest of your estate already manages sensitive data.
Framework
What is GRASP?
GRASP is a shared language for structuring AI agent risk. Five dimensions that turn vague risk intuition into a comparable, evidence-based profile for every agent and every session.
Governance, Reach, Agency, Safeguards, and Potential damage. ARIS scores every captured session against GRASP so the firm's risk posture is current, queryable, and comparable across agents, teams, and time.
Pilot outcomes and commercials
What are the pilot outcomes after 4 weeks?
Four validated outcomes. Adoption mapping showing where AI is concentrated and where it is absent. Usage intelligence you can query in natural language. Session-level risk scoring across every captured surface. A structured risk posture that always has a current answer.
Plus a board-ready findings briefing with grounded, evidence-linked answers to the three questions leadership keeps asking. Where is AI transforming the work. Is the investment paying off. How exposed are we.
What happens if Ryora doesn't exist in 24 months?
ARIS is self-hosted by design. The platform runs on your Docker or Kubernetes, against your Neo4j and Postgres, with your data inside your environment. No vendor runtime dependency.
Enterprise contracts include source escrow. The system survives the vendor.