Prove AI’s impact.
Across your human and agent workforce.
See what AI costs, what your teams produce with it, and which workflows to automate next.
AI ROI
AI ROI
2.5×
Net capacity value ÷ AI spend
Net capacity value
$1.35M
Estimated · over 12 weeks
What AI produced
1,440 hrs / wk
36 people-equivalents of capacity
What AI cost
$547K
$31.65 per AI hour · 12 weeks
AI returns across departments
| Department | Hours back / wk | Cost / AI hr | Adoption |
|---|---|---|---|
| Engineering | 520 | $38.00 | 84% |
| Customer Support | 340 | $23.00 | 71% |
| Finance | 60 | $38.00 | 43% |
| All 7 departments | 1,440 | $31.65 | 58% |
Where is the next return?
Invoice processing and reconciliation
One of 195 candidate workflows, with $3.6M a year in combined potential capacity value.
Explore automation opportunities →ROI = (capacity value − spend) ÷ spend · hours valued at $110 loaded cost. Estimated capacity value, not realized cash savings. See the full ROI breakdown →
Trusted by AI-forward enterprises
AI Value Measurement Platform
Know where your AI investment pays off.
Connect spend and adoption to the work your company delivers. Start with the question you need to answer.
Spend Intelligence
Account for your AI spend.
Trace license and token costs to the teams and agents using them.
ExploreAI Impact
Measure what AI changes.
Compare adoption, fluency and AI capacity across every department.
ExploreDeveloper Intelligence
See what engineering delivers.
Connect Engineering Output, code quality and delivery to AI spend.
ExploreWorkflow Intelligence
Find what to automate next.
Identify repeated work and track automation against a measured baseline.
ExploreThe company view
Find the teams to learn from. And the ones to support.
Compare adoption, fluency and cost alongside the capacity AI adds, department by department.
Department leaderboard
| Department | AI adoption | vs avg | Fluency / 10 | Capacity / wk | AI spend | Cost / AI hr | Automatable | 12-wk trend |
|---|---|---|---|---|---|---|---|---|
| Engineering310 employees | +26 | 6.8 | 520 hrs | $237,120 | $38.00 | 14% | ||
| Product120 employees | +18 | 6.2 | 140 hrs | $57,120 | $34.00 | 18% | ||
| Customer Support420 employees | +13 | 5.6 | 340 hrs | $93,840 | $23.00 | 36% | ||
| Marketing140 employees | +8 | 5.1 | 120 hrs | $38,880 | $27.00 | 22% | ||
| Sales380 employees | 0 | 4.8 | 240 hrs | $86,400 | $30.00 | 24% | ||
| Finance110 employees | -15 | 4.0 | 60 hrs | $27,360 | $38.00 | 46% | ||
| Other Departments520 employees | -29 | 4.8 | 20 hrs | $6,240 | $26.00 | 20% | ||
| Company total2,000 employees | 58% | 5.6 | 1,440 hrs | $546,960 | $31.65 | 23% |
Adoption counts measured users with at least one AI session per week. Capacity converts observed AI work into human-hour equivalents. Automatable is the share of observed effort. Team level by default.
Developer Intelligence
Measure what engineering delivers with AI.
Connect Engineering Output, code quality and delivery to AI spend. See where coding agents help and where teams need support.
AI Impact · Larridin Router · Agent Effectiveness · Engineering Performance · Ask AI
Explore Developer IntelligenceAI Impact
Engineering Output / $1K
67.6 pts +31% ↑
per $1K of invoiced AI spend
Quality
92 / 100 +4 ↑
AI Quality Score · defect rate 2.1%
Velocity impact
18h −38% ↓
median PR cycle, AI-assisted vs baseline
AI ROI
4.8× +0.6 ↑
$136K estimated net value over four weeks
Token & Spend Insights
All-in cost
$183,847
$122,554 usage + $61,293 fixed licenses
Spend breakdown
Usage by actor
Session split
Human · 73% Agents · 27%
Spend split · variable usage
Human · $33,847
Agents · $88,707
Attribution · selected surfaces
Alerts
Budget nearly exhausted: reporting-pipeline-v3 switched to a cheaper model at 90% of budget.
Spend Intelligence
Know where every AI dollar goes.
Token usage, seat licenses and cloud model costs in one spend view. Trace every dollar to a team, a tool or an agent before the next budget review.
Built for CFOs, finance and operations teams.
Explore Spend IntelligenceA customer perspective
“If you don’t know what people are actually using, you don’t know what to buy next.”
Larry Hill · Gainsight
Gainsight used Larridin to understand AI tool adoption and inform its first enterprise LLM purchase.
For your role
Answer the question on your desk.
Heads of AI & CIOs
Where is our AI rollout working?
Compare adoption and capacity across functions. Decide where to expand and where to help.
See AI ImpactCTOs & Engineering Leaders
What are we delivering with AI?
Review Engineering Output, quality and agent performance alongside cost.
See Developer IntelligenceCFOs & Finance Teams
What is our AI spend paying for?
Reconcile costs across vendors. See which teams and agents use the budget.
See Spend IntelligenceFrom the video hub
How leaders put AI to work.
Common questions
Before you get started.
More detail on measurement, data and rollout.
Talk to our teamWhat does Larridin measure?
Larridin is an AI Value Measurement Platform. It connects AI usage and spend to the work of people and agents across your business. Use it to understand adoption and fluency, compare engineering performance, and find opportunities to automate repeated work.
Explore the platform to choose your starting point.
How is AI capacity different from savings?
AI capacity estimates the human-equivalent work AI contributes. It is not automatically time saved, cash returned or a reduction in headcount. The value depends on the baseline, data coverage and how teams use that capacity.
Workflow measurement separates potential opportunities from completed automations. Compare completed work with the captured baseline before reporting realized benefits.
What data needs to be connected?
The sources depend on what you want to measure. Spend Intelligence brings together billing and usage data. Developer Intelligence connects engineering activity, code changes and supported agent sessions. Workflow Intelligence uses captured work activity to identify repeated processes.
Review supported tools, access and coverage with the Larridin team. The Developer Intelligence setup guide explains the engineering setup.
How does Larridin handle privacy and access?
Larridin supports role-based access and enterprise authentication. Choose the scope of measurement and review data handling, retention and permissions with your administrator and security team.
The Trust Center provides security information for your review. Access and collection depend on the products and connections you enable.
How do we start a rollout?
Start with a team and a decision you need to make, such as an AI tool renewal or an engineering rollout. Confirm the relevant data sources, establish coverage and review the baseline before expanding to more departments.
Book a demo to discuss the setup for your environment. The rollout schedule depends on your systems and access requirements.
Where does Developer Intelligence fit?
Developer Intelligence is the engineering part of Larridin. It connects Engineering Output, quality and delivery with AI spend, agent effectiveness and readiness. AI Impact provides the company view across departments.
Explore Developer Intelligence or read the engineering measurement methodology.
Are the dashboard numbers customer results?
No. Dashboards marked “Example” use illustrative data. The company view represents 2,000 employees and 12 weeks of spend. Adoption counts weekly active AI users, and fluency is scored out of 10. All departments are included in the company totals.
AI capacity uses a 40-hour week per people-equivalent. Cost per AI hour divides spend by human-equivalent AI hours over the same period. Workflow examples estimate annual capacity value at $31 per hour over 52 weeks. The opportunity pool includes workflows in pilot and already automated; their figures should not be added together.
The engineering preview uses the separate four-week example on our Developer Intelligence pages. Customer interviews are attributed and linked to their published sources.
You said AI would change your business.
Now prove it’s working.
Walk through Larridin with our team. Bring the question you need to answer.
Book a Demo