PASS 01
Temporal-proximity linking
Token sessions are matched to output events — merged PRs, closed tickets, and resolved cases — inside adaptive attribution windows.
The Token ROI Intelligence Platform
The teams spending the most on tokens are creating the most value. You just can't see it yet.
Arcline instruments the full stack from raw token consumption to business output — building per-user efficiency profiles and systematically propagating high-ROI patterns across your organization.
aarcline.com/dashboard/code-roi
Actual product interface. Figures shown are sample data.
Reference model — illustrative, not customer results
125x
Modeled return on the token line item
top-decile engineer scenario
$40K→$5M
Modeled spend to attributed value
reference SWE profile
3.2x
Modeled organizational lift
pattern-replication scenario
Now onboarding design partners
Instrument your first Token ROI profiles with us.
We're working hands-on with a small group of engineering organizations. If your org has 100+ AI-active engineers and no per-user attribution, we want to talk.
Become a design partner →Correlates token spend with output signals across your stack
GitHubJiraLinearSalesforceZendeskSnowflakeAnthropicOpenAIVertex AI
The problem
Token spend is invisible infrastructure
A typical enterprise with 500+ AI-active employees has zero per-user attribution, no value correlation, and budget pressure aimed squarely at its highest-ROI users. The absence of measurement creates three compounding failure modes.
Failure mode A
The efficiency illusion
Teams that look cost-efficient by spending fewer tokens are often the most expensive when fully-loaded — they accomplish less per dollar of salary. Low token spend isn't efficiency, it's underperformance.
Team A: $8K/yr, 2 features/qtr. Team B: $22K/yr, 9 features/qtr.
Failure mode B
The talent inversion
Token caps and spend interrogations signal to top AI-native engineers that you don't understand the value they create. The people most capable of 10x+ returns leave for organizations that measure and reward it.
High performers self-select out of constrained environments.
Failure mode C
The replication gap
Without a way to extract the patterns of high-ROI users, each one operates as an isolated cell of excellence. You get the cost of their token spend but never the compound interest of systematic learning.
Local knowledge never transfers across the org.
The token ROI thesis
Token spend is not a cost. It is a value multiplier.
Like compute at a data center, individual token spend is the substrate for every output a knowledge worker produces. It does not subtract from ROI — it amplifies it.
The economic model
Token ROI=Marginal Value GeneratedToken Cost + Fully-Loaded Labor Cost
The reference model: a top-decile engineer converting roughly $40K of annual token spend into $5M of attributed marginal value. This is a model, not a customer result. Arcline exists to find these people in your org and test which patterns replicate.
10x token user — reference model
- Weekly token spend
- 200,000 tokens
- Annual token cost
- $41,600
- Fully-loaded salary
- $350,000
- Total annual cost
- $391,600
- Marginal value generated
- $5,200,000
- Token ROI
- 13.3x
The product
Three surfaces, one loop: measure the spend, find the leak, change the policy.
Every image below is a capture of the running application, not a rendering. Figures are sample data.
Executive overview
One number the CFO and VP Engineering can both defend.
Organizational Token ROI sits alongside attributed value, active profiles, and the spend trend. Every figure drills through to the sessions and merges underneath it, so the headline is auditable rather than asserted.
aarcline.com/dashboard
Efficiency audit
Why the money leaked, ranked by what a fix reclaims.
An A–F grade scored on the share of metered spend a remedy would actually recover — never an arbitrary deduction. Each finding expands to its evidence, its remedy, and the dollars it returns, then ships as a governed policy PR against a held-out baseline.
aarcline.com/dashboard/code-roi
Allocation rules
Policy that changes behavior, then proves what the change caused.
Routing and budget rules run as governed experiments with a holdout, so a saving is measured against a counterfactual instead of a hopeful before-and-after. This is the layer free metering tools cannot reach.
aarcline.com/dashboard/rules
The distribution of ROI
Token ROI follows a power law — and it isn't fixed
Most knowledge workers haven't yet developed the workflow patterns that unlock high ROI. The top decile has. Arcline makes that top 10% legible, then moves the whole distribution toward it.
Share of AI-attributable value by ROI tier
n = 510 users
~65%
of value from the top 10% of token users
~88%
of value from the top 25% of token users
<1.0x
ROI for the bottom 40% — cost exceeds value
Technical architecture
A continuous pipeline from tokens to intelligence
Four interconnected subsystems. Finance tools stop at the invoice and gateways stop at the request — we sit above both, joining spend to the outcome it produced. Your existing cost feed becomes an input, not a competitor.
TTL
Token Telemetry Layer
Start with zero procurement: a local CLI reads the session logs 36+ agents already write to disk — no API keys, nothing uploaded. Graduate to SDK or gateway ingestion for org-wide rollout, and fold in existing Ramp or Vantage cost feeds rather than replacing them.
VAE
Value Attribution Engine
Correlate token events with output signals from GitHub, Jira, Salesforce, and more using three-pass temporal-proximity attribution.
UEP
User Efficiency Profiler
Build longitudinal per-user profiles across six dimensions, distinguishing genuine high ROI from raw token volume artifacts.
OLS
Organizational Learning System
Extract generalizable patterns from high-ROI users and propagate them as playbooks, nudges, and benchmarks across the org.
Zero workflow change for end users · First Token ROI profiles within 14 days of integration
Value Attribution Engine
Why attribution is hard — and how we do it anyway
Naive token-to-output correlation is confounded and gameable. Arcline treats attribution as a Bayesian inference problem, not a database join.
PASS 02
Counterfactual baselining
Each person is modeled against their own pre-AI baseline and role-matched peers, subtracting seniority and role confounds instead of rewarding them.
PASS 03
Adversarial validation
Token inflation without output lift, output splitting, and session padding reduce confidence rather than increasing the reported ROI.
Confidence-honest by default
Every profile ships with a posterior and confidence interval. A 30x ROI estimate with wide uncertainty is presented as exactly that — never laundered into a leaderboard fact.
estimate ± uncertainty
User efficiency profiler
Six dimensions that separate 10x users from volume artifacts
Each profile is longitudinal and evidence-backed — measuring how efficiently token investment converts into shipped output, not how many tokens were burned.
Token ROI Ratio
top 96 · median 42Marginal value generated per $1 of token spend, rolling 30-day.
Context Utilization
top 91 · median 38Value generated relative to context window usage.
Session Depth Quality
top 88 · median 34Multi-turn iterative refinement vs. single-turn queries.
Latency Compression
top 84 · median 47Speed token usage translates into shipped output signals.
Prompt Pattern Stability
top 93 · median 51Consistency of high-performing prompting strategies over time.
Cross-Domain Leverage
top 79 · median 29Token investment in one domain producing adjacent value.
Top decile Median
High-ROI user signature
Analysis of high-ROI users consistently surfaces a recognizable behavioral signature — the basis of Arcline's replication playbook.
- 1Front-loaded context — 30–40% of the window on problem framing before output
- 2Staged decomposition with intermediate validation checkpoints
- 3Output reuse: session depth 3–5x higher than median
- 4Tool integration density at 4x the median rate
- 5Failure recovery within 1–2 turns — the strongest ROI predictor
Organizational learning system
From individual excellence to organizational capability
Identifying a 10x token user is valuable. Replicating their patterns across 500 engineers is transformational. The OLS is designed for resource expansion — never headcount reduction.
Playbook extraction
Continuously analyze top-decile sessions and encode them as annotated workflow sequences — trigger conditions, decomposition strategy, validation checkpoints, and failure-recovery heuristics.
Peer benchmarking
Real-time, objective, psychologically-safe efficiency percentiles. Every tier includes evidence-backed recommendations drawn from the behavior of the tier above.
Budget reallocation
Automated weekly resource signals: where to invest more, where patterns aren't landing, and where high-ROI value is concentrated in too few people.
Sample weekly resource signal
Reallocation opportunity
Engineering: 3 members in the bottom ROI quartile despite top-20% token spend.
$280K/yr recoverable
Concentration risk
85% of AI-attributable value in Search Platform is generated by 2 individuals.
Succession risk
Budget expansion case
4 Data Science users are token-constrained by team cap; marginal ROI averages 34x.
+$612K unlockable
Security & Compliance
Built for procurement, not just for demos
Arcline sits in the path of your most sensitive signal — token usage tied to people and output. The trust layer is designed in from day one.
Security-first architecture
Built from day one for a SOC 2 Type II audit. Compliance roadmap available on request.
Encryption everywhere
TLS 1.3 in transit, AES-256 at rest. Token telemetry is content-free — we never store prompt or completion text.
Enterprise identity roadmap
SAML 2.0 / OIDC SSO, SCIM provisioning, and team-level role controls are on the enterprise roadmap.
Privacy by architecture
Per-user telemetry is designed for pseudonymization and configurable retention. EU data residency is on the enterprise roadmap.
Procurement roadmap
DPA, SCC, and transparent subprocessor documentation are planned for enterprise readiness.
Deployment isolation roadmap
Dedicated infrastructure and VPC peering are planned options for regulated enterprise deployments.
The three phases
Visibility. Fleet autonomy. Outcome distillation.
Each phase produces the asset the next one requires. Visibility yields the attributed-outcome record, autonomy allocates against it, and distillation turns verified wins into post-training data that makes models natively efficient at the task.
01
Visibility
Now — live in product
Token efficiency and the marginal return on every token spent.
Meter every token, attribute it to a shipped, rejected, or unresolved outcome, and grade the setup with a fix list priced in dollars. This phase answers: what did the last dollar of intelligence actually buy?
Produces
The attributed-outcome record
02
Fleet autonomy
Next — governed rollout
Autonomous management, allocation, and optimization of the agent fleet.
The allocation engine graduates from advising to acting: routing tasks across models and agents, tuning budgets, and retiring failing configurations — every change shipped as a governed experiment against a held-out baseline.
Produces
The intervention ledger
03
Outcome distillation
Then — the compounding asset
Positive outcomes become post-training data that makes models natively efficient at the task.
Every attributed win — the prompt, context, tool calls, and verified business outcome — is classified into curated post-training corpora. Used to tune open-source models or a company's own foundation models, base weights become specialists that complete the task in a fraction of the tokens.
Produces
Outcome-verified training corpora
Phase three is where the economics invert: verified outcomes tune open-source or company-specific foundation models, the capability moves into the weights, and the cost of the task falls permanently.
Pricing
Metering is free. You pay for the loop.
Never charge for what a well-funded incumbent gives away. Cost visibility is free forever — we charge for outcome attribution and governed allocation, with a value share only on improvement above your baseline.
Meter
$0free, unlimited seats
Cost visibility is a commodity — free tools give it away, so we do too. One command, no procurement, no API keys. You get a real waste fix list on day one.
- Local CLI ingestion — no API keys, nothing uploaded
- Setup efficiency grade with actionable waste fixes
- Shadow AI discovery: untracked keys and personal-card spend
- Per-user, per-team, per-workflow spend views
- MCP server: usage insight inside your coding agent
- Ingests Ramp, Vantage, and gateway cost feeds
Attribute
Most adopted$42per seat / month
Where we start and nobody else does: link every token to the outcome it produced in your system of record.
- Everything in Meter
- Value Attribution Engine with outcome linking
- Bayesian attribution with confidence intervals
- Token ROI profiles across six dimensions
- High-ROI playbook extraction
- Anti-gaming safeguards
Allocate
Custombase + value share
The closed loop. Governed allocation experiments that change policy and prove the improvement they caused.
- Everything in Attribute
- Allocation Engine with policy-as-code
- Governed experiments against a held-out baseline
- Per-agent token budgets and scoped autonomy
- Executive brief and portfolio ROI reporting
- Value share applied only above agreed threshold
- Dedicated deployment engineering
Value share is calculated only on ROI improvement above a jointly agreed baseline — verified by the same attribution engine you use every day. If we don't move the number, we don't share in it.
North star metric
Increase your organizational Token ROI by 2x within 12 months.
The single metric Arcline optimizes for is total marginal value generated per dollar of token spend across every integrated process. See it in the live demo dashboard.
The Fair Measurement Standard
Attribution without trust is surveillance. Every deployment starts here.
- You see your data first. Every individual sees their profile before any manager does.
- Content-free by design. We measure metadata — never prompts or completions.
- Expansion, not extraction. Signals default to where the company should invest more.
- Confidence-honest. Low-confidence attribution is labeled, never laundered into a leaderboard.