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.
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 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
TRIP comprises four interconnected subsystems. We sit above Claude, GPT, and Gemini as the observability and attribution layer — the same motion as Datadog to infrastructure.
TTL
Token Telemetry Layer
Capture token consumption at user, session, prompt, and model level from every connected LLM provider — via SDK wrapper, gateway proxy, or provider export.
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.
Where this goes
Today, people spend tokens. Increasingly, agents do.
An agent workforce with no ROI attribution is a budget with no owner. The attribution engine we build today becomes the training signal for the allocation engine we ship next.
01
Measure
Content-free token telemetry across every provider, surface, person, and agent.
02
Attribute
Posterior ROI profiles connected to durable outcomes and explicit uncertainty.
03
Allocate
Governed budgets route toward the highest-posterior-ROI work, human or agent.
This is one company, sequenced: instrument the spend, infer the outcome, then let a Thompson-sampling allocation engine learn where the next token has the highest expected value.
Pricing
Priced the way we measure: aligned to value, not volume
Per-seat pricing rewards low token spend. Per-token pricing punishes ambition. Arcline charges a per-seat base — with a value share only on demonstrated ROI improvement above your baseline.
Instrument
$18per seat / month
Unified measurement across every AI surface. See where tokens actually go before deciding anything.
- Token Event Graph across all providers
- API, IDE agent, and chat surface ingestion
- Per-user, per-team, per-workflow spend views
- Normalized cross-provider cost accounting
- Weekly spend digest
Optimize
Most adopted$42per seat / month
Full ROI attribution and efficiency profiling. Know which tokens create value — and which are noise.
- Everything in Instrument
- Value Attribution Engine with outcome linking
- Token ROI profiles across six dimensions
- Individual efficiency percentiles with consent controls
- High-ROI playbook extraction
- Anti-gaming safeguards
Compound
Custombase + value share
The full organizational learning system. Pricing aligned to demonstrated ROI improvement above your baseline.
- Everything in Optimize
- Organizational Learning System
- Weekly budget reallocation signals
- 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.