Veldrith · Technology intelligence Sample data
Every application, platform and data center scored across all tiers, with a target state and a ranked roadmap to get there.
“Are we getting enough business value from technology?”
- Only —% of the $—M run cost goes to applications that are both valuable and healthy.
- $—M a year (—%) runs — applications that should be retired.
- — high-value applications are ready for AI today, the fastest route to AI returns.
- Risk to watch: — infrastructure assets are out of vendor support, and $—M of price increases arrive with renewals in the next 12 months.
“Where can I reduce technology spend and improve ROI?”
- $—M a year can come out across applications, licenses, security tools and support calls.
- Quick wins in 90 days: $—M from unused licenses and overlapping security tools, taken at renewal.
- Act before renewals: $—M of vendor price increases are due in the next 12 months.
- Monthly run cost is —% versus a year ago, led by infrastructure and cloud.
“What should my technology organization do next?”
- Retire — applications, — of them Tier 2–4, starting with those nobody has used in months.
- Modernize — applications in priority order; the backlog is on the Modernization tab.
- Move — data-center applications to the cloud in three waves, and keep only — on-premises.
- Replace or upgrade — infrastructure product lines that are already out of support.
- Start AI work on the — high-value, AI-ready applications.
Strategic roadmap
The moves that take the estate to its target state, in Now, Next and Later waves, with an owner and the value of each.
| When | Area | Move | Owner | Value |
|---|
Retire candidates sit in every tier
Applications per tier by recommendation. Most of the retire list is Tier 2–4, systems a Tier 1-only review never looks at.
Where applications run, today and in the target state
Applications by where they are hosted. The Infrastructure & cloud tab shows each decision and the move waves.
Monthly run cost
Last 12 months, $M per month, by cost type.
Run cost by department
$M a year. Hover for apps, debt and incidents.
What to do with each application
Number of applications per recommendation (6R). Hover for cost and savings.
Where the money runs
Annual run cost by hosting model, $M.
The score is one input to the recommendation. A low score at Tier 1–2 means replace rather than retire, and stale or duplicate applications are retired whatever they score, so the retire list (— applications) differs from the Retire band.
How the portfolio scores
Applications per 10-point band of the Application Rationalization Score (0 = retire now, 100 = invest). Colored by band.
Score by department
Average score, applications to retire and AI-ready applications in each department.
| Department | Apps | Avg score | Retire | AI-ready | Cost $M/yr |
|---|
Why each score is what it is
The 30 lowest-scoring applications that still cost the most, with the five parts of the score (each 0–100, higher is better) and what pulls it down.
| Application | Tier | Score | Value | Health | Cost efficiency | Usage | Reliability | What pulls it down | Cost $K/yr |
|---|
Application inventory
Every application across every tier, with its owner, hosting, cost, score and recommendation. Click a column to sort; search matches any text.
Showing — applications.
Savings opportunity by lever
Estimated annual savings, $M. The rules are on Method & data feeds.
Cost by type
Annual run cost, $M.
Cost by hosting model
On-prem apps carry data-center, hardware and staffing cost; SaaS mostly license.
| Hosting | Apps | Cost $M/yr | Infra $M/yr | Avg $K per app |
|---|
Most expensive applications
Annual run cost, $K, top 15. Red bars run on end-of-life stacks.
Highest cost per user
Annual cost divided by monthly active users: expensive apps few people use.
| Application | Users | Cost $K/yr | $ per user | Recommendation |
|---|
Which domains depend on which
Integration links from the domain on the left to the domain it calls along the top. Darker means more links; dense rows are hard to change alone.
Highest blast radius
Applications the most other systems depend on. An outage or change here ripples furthest, so these come first in any migration plan's risk review.
| Application | Domain | Tier | Systems depending on it | It depends on | Tech debt | Change risk |
|---|
Support runway
When vendor support ends for each product line, against today. Red is already past; amber ends within a year. Hover for units, cost and the action.
Renewal calendar
Support renewals in the next 12 months, $K: today's price and the expected increase. Negotiate or plan an exit before these months.
Biggest renewal increases
Product lines whose next renewal goes up the most, $K a year.
Infrastructure lifecycle register
Every product line with its support end date, cost and the action. Dates marked "Sample estimate" are made up for this demo; "Vendor published" dates are the vendors' real ones.
| Product line | Category | Units | Support ends | Date basis | Status | Support $K/yr | Renewal increase % | Apps on it | Action |
|---|
Tech debt distribution
Number of applications by tech debt score (0 = clean, 100 = worst). Red is high debt, score — or more.
Debt by technology stack
End-of-life stacks concentrate debt, incidents and support cost.
| Stack | End of life | Apps | Cost $M/yr | Avg debt | Incidents 90d |
|---|
Highest tech debt
The 20 worst applications and what drives each score: code quality, outdated dependencies, open vulnerabilities and documentation gaps (each 0–100, higher is worse).
| Application | Stack | Age (yrs) | Code | Dependencies | Open vulns | Docs | Debt score |
|---|
Business value vs technical health
The TIME model: each dot is an application, sized by run cost. Hover for details.
Recommendations and their value
Estimated savings use the rules on the Method tab.
| Recommendation | Apps | Cost $M/yr | Savings $M/yr |
|---|
Modernization backlog
Applications ranked by modernization priority, which weighs tech debt, business value, incidents and run cost. Start at the top.
| Priority | Application | Stack | Hosting | Tech debt | Business value | Recommendation | Savings $K/yr |
|---|
Stale and redundant applications
No release in over — days and no logins in — days, or fewer than — monthly users; or a second application doing the same job.
| Application | Capability | Users | Days since release | Days since login | Cost $K/yr | Recommendation |
|---|
Incidents per month
Last 12 months, all applications.
Applications causing the most issues
Last 90 days. MTTR is mean time to restore, in hours.
| Application | Tier | Stack | Hosting | Incidents | P1 | MTTR hrs | Availability % |
|---|
Where license money is wasted
Annual cost of reclaimable seats, $K, top 15 products. Hover for the seat breakdown.
Every license, seat by seat
Departed: assigned to people HR shows as gone. Inactive: current employees with no login in 90 days. Unassigned: bought, never given out. Act before each renewal.
| Product | Purchased | Active | Departed | Inactive | Unassigned | Used % | Waste $K/yr | Renewal |
|---|
Spend by security category
$K a year. Red categories have three or more tools for one job. Hover to see them.
Every security tool and what to do with it
Coverage: share of in-scope assets the tool protects. Feature use: share of licensed features turned on. Keep the strongest tool per category; fold the rest in at renewal.
| Category | Tool | Cost $K/yr | Coverage % | Feature use % | Recommendation | Savings $K/yr |
|---|
Which systems generate the calls
Calls in the last 90 days, top 15 causes. Blue are applications, orange infrastructure. Hover for the main issue and linked incidents.
Call drivers traced to IT
Each call reason is linked to the application or infrastructure behind it. Repeat: callers who called again within 7 days.
| System | Type | Channel | Top issue | Calls 90d | Handle min | Repeat % | IT incidents | Cost $K/yr |
|---|
Target hosting mix
Applications by where they run today and in the target state. Retired applications leave the estate.
Data-center exit waves
Applications moving to the cloud, grouped so the simplest move first and the most connected move last.
| Wave | Timing | Apps | Tier 1 | Links to other systems | Infra $M/yr |
|---|
Wave 1: Tier 3–4 applications with few links. Wave 2: Tier 2 and moderately linked. Wave 3: Tier 1 and heavily linked systems, after the platform is proven.
Infrastructure platform decisions
Keep, upgrade, replace or exit for every infrastructure product line, ordered by when the decision is due.
| Product line | Category | Units | Decision | Decide by | Action |
|---|
Scenario: the virtualization renewal
— VMware hosts, renewal due —, with $—M of expected price increase.
| Option | Cost | Risk | Time |
|---|---|---|---|
| Renew as is | High | Low | At renewal |
| Renegotiate and shrink the footprint | Medium | Low | One quarter |
| Move to another hypervisor | Low | Medium | Up to 18 months |
| Move workloads to cloud with the exit waves | Medium | Medium | Up to 24 months |
Recommended: renegotiate a shorter term now, and let the exit waves shrink the footprint before the following renewal.
Every data-center application, decided
Where each application goes, with its wave. Sorted by wave, then by run cost.
| Application | Tier | Decision | Wave | Links |
|---|
Most crowded business capabilities
Applications per capability today, top 15. Hover for the proposed core platform.
Target platform for every capability
The core platform is the highest-scoring application not marked for retirement. The rest are consolidated into it or retired.
| Capability | Department | Apps today | Core platform | Its score | Consolidate | Retire |
|---|
Where technology spend goes
Annual external spend by vendor, $M, top 15. Colored by the recommended relationship.
Vendor strategy
Strategic partners carry the largest healthy spend. Consolidate where tools overlap or seats go unused. Renegotiate or exit where prices jump or products fall out of support.
| Vendor | Spend $M/yr | Products | Where | Relationship | Why |
|---|
AI strategy: where to start, department by department
Each department’s main AI use case, sequenced by how ready its applications are. Now: readiness 60+ and at least ten ready, high-value applications. Next: readiness 50+. Later: fix the data and platforms first.
| When | Department | Use case | Ready, high-value apps | Avg readiness | Main blocker |
|---|
AI readiness by department
Average AI readiness score, 0–100. Hover for ready applications and the main use case.
What blocks AI
Applications not yet ready, by their biggest blocker.
Start here: high value, ready now
The 25 most valuable applications that are already AI-ready, with the use case that fits their department.
| Application | Department | Use case | Value | AI ready |
|---|
Valuable but not ready: fix these first
High-value applications held back from AI, and the one thing in the way.
| Application | Department | Blocker | Stack | Value | AI ready | Recommendation |
|---|
Tech debt score
A 0–100 score, higher is worse: 30% code quality (static analysis), 25% outdated dependencies, 25% open security vulnerabilities, 20% documentation and test gaps. High debt starts at —. Technical health is 100 minus debt.
Business value
Driven by criticality tier (Tier 1 is revenue- or operations-critical), active users, and a penalty where another application covers the same capability.
TIME model
Value and health split at —. Invest: high value, healthy. Migrate: high value, unhealthy, so modernize. Tolerate: low value, healthy, so keep cheaply. Eliminate: low value, unhealthy.
Recommendation (6R)
- Retire: stale, a Tier 3–4 duplicate scoring under —, or a score under — at Tier 3–4.
- Replace (SaaS): score under — at Tier 1–2.
- Refactor / Rebuild: score —–— on COBOL, Oracle Forms, PowerBuilder, Delphi, VB6 or Classic ASP.
- Replatform: same score range, on-prem or on a desktop. Refactor: same range, already in cloud.
- Rehost: score —+ but still on-prem. Retain: —–—. Retain & Invest: —+.
- Stale: no release in over — days and no login in —, or fewer than — monthly users.
Application Rationalization Score (ARS)
One 0–100 score per application, higher is better: 30% business value, 25% technical health, 20% cost efficiency (cost per user against the rest of the portfolio), 15% usage (zero when stale) and 10% reliability. Bands: Invest —+, Maintain —+, Modernize —+, Retire below. Each score lists the parts that pull it down, so it can be challenged.
AI readiness
A 0–100 score, higher is better: 25% APIs and integration, 25% data quality, 20% platform (cloud and SaaS score high, end-of-life and desktop tools low), 15% security, 15% documentation. Ready —+, Needs work —+. The blocker is the weakest part.
Infrastructure strategy
Each data-center application follows its recommendation: retire, replace with SaaS, or move to the cloud. Mainframe systems and store, warehouse and distribution-center edge systems stay on-premises. Moves are grouped into waves: Tier 3–4 applications with few links first, then Tier 2, then Tier 1 and heavily linked systems.
Application and platform strategy
Applications are grouped by business capability. The core platform for each is the highest-scoring application not marked to retire; the rest are consolidated into it or retired.
Vendor strategy
External spend combines application licenses, SaaS seats, security tools and infrastructure support. Renegotiate or exit: a price increase of $0.25M or more at renewal. Strategic partner: the six largest vendors otherwise. Consolidate: 30% or more of spend on overlapping tools or unused seats.
Executive advisor and personas
The advisor ranks the next moves across every lever by annual dollar impact and names an owner. The CEO, CFO and CIO views answer each executive's question from the same numbers. In the product, an AI advisor answers follow-up questions in plain English and drafts the board memo.
Savings assumptions
Retire saves —% of run cost; Replace saves —%; Replatform and Rehost save —% of infrastructure and —% of labor; Refactor saves —% of labor and —% of infrastructure. One-time migration cost is not netted off yet.
Modernization priority
35% tech debt, 25% business value, 20% incident load (P1s weigh more), 20% run cost. Applications to retain score zero.
Infrastructure lifecycle
Status compares each product line's end of vendor support with the as-of date. Extended support is the extra yearly cost of running past that date (extended updates, third-party or premium break-fix support). Renewal increase is the expected uplift on support contracts renewing in the next 12 months, from quotes or the vendor's announced pricing.
License reclamation
Reclaimable seats are unassigned (bought, never given out), departed (assigned to someone HR has terminated) and inactive (no login in 90 days). Savings are those seats at list price per seat, taken at the next renewal or true-up.
Security consolidation
Per category, the tool with the best coverage and feature use is kept. Every other paid tool is folded into it, saving —% of its cost after migration effort. Tools already included in an existing suite are counted as free capacity, not savings.
Avoidable support calls
Each call reason is linked to an application or piece of infrastructure. Annual cost is calls × handle time × loaded cost per minute. The avoidable share per system (fixing the root cause, self-service, proactive alerts) is assumed to be —% realized.
Going live: where each number comes from
This page runs on generated sample data. In production each source below becomes a live query, so the dashboard refreshes as the systems change.
Inventory, owners, tiers
ServiceNow CMDB / APM module, LeanIX, Ardoq, or Jira Assets.
License and SaaS cost
Contracts and spend from Coupa, SAP Ariba or the ERP's accounts payable; SaaS discovery from Flexera, Zylo or Productiv.
Infrastructure cost
AWS Cost and Usage Report, Azure Cost Management, GCP billing export, plus on-prem chargeback from VMware or the data-center model.
Labor cost
Team allocation from Workday or the HR system, and engineering time from Jira or ServiceNow worklogs.
Dependencies
Service maps from Datadog or Dynatrace, API gateway and integration logs (MuleSoft, Kong), CMDB relationships.
Usage and staleness
Sign-in logs from Okta or Entra ID; release history from GitHub, GitLab or Azure DevOps.
Incidents and MTTR
ServiceNow ITSM, PagerDuty or Jira Service Management.
Tech debt
SonarQube for code quality, Snyk or Dependabot for dependencies and vulnerabilities, endoflife.date for runtime support status.
Infrastructure lifecycle
Asset discovery (ServiceNow Discovery, vCenter, network scans, the software collector) for versions and units; vendor lifecycle feeds and endoflife.date for support end dates; support contracts and renewal quotes from procurement for cost and price changes.
License seats
Admin APIs of each SaaS product (ServiceNow, Atlassian, Salesforce, Microsoft Graph, Zoom, Slack, Adobe), joined to the HR termination feed from Workday and sign-in data from Okta or Entra ID.
Security tools
Contracts from procurement, plus each tool's own API for coverage (assets enrolled versus the CMDB) and features turned on.
Call center
Contact-center platforms (Genesys, NICE, Five9, Amazon Connect) and case systems (Salesforce Service Cloud, Zendesk, ServiceNow). Call reasons are linked to systems through case categories, incident links and AI reading of call transcripts.