Token spend is the new cloud bill, and most teams cannot attribute it to a feature, a customer, or a decision. We instrument your AI workloads, model the unit economics, and re-engineer the expensive paths so cost per outcome falls without quality falling with it.
Adoption pillars covered
Discover
Primary focus
Build
Supporting
Enable
Light touch
Govern
Primary focus
Delivery models supported
Project-based
Best fit
Embedded engineering
Available
Workshop-based
Available
Step 01 — see it
The first thing a tokenomics engagement produces is a picture like this one: every call tagged, every dollar attributed, and the handful of code paths quietly consuming the majority of the bill ranked in order.
Where the spend actually lands
A typical attribution once instrumentation is in place. Before it, most teams can only see one number: the invoice.
Retrieval-augmented chat
largest share
41%
Background summarization
batch eligible
27%
Agent tool loops
high variance
19%
Classification & routing
cheap, high volume
13%
Shares shown are illustrative of the pattern we usually find, not a specific client result. Your first deliverable is this same chart built from your own traces.
The calls worth looking at first
Oversized context on every chat turn
The full document set is re-sent each turn instead of the passages that changed, so cost scales with conversation length rather than with the question.
Frontier model on classification work
A high-volume, low-difficulty task is routed to the most expensive model because that is what the prototype used and nobody revisited it.
Agent retry loops without a budget
Failed tool calls retry until they succeed or the request times out, so a small number of sessions produce an outsized share of the spend.
Synchronous summarization
Work that nobody is waiting on runs at interactive priority, forfeiting the discount available to batched and off-peak processing.
Evaluation runs against production models
Test suites exercise the same expensive tier as live traffic, which is defensible for release gates and wasteful for everything else.
Step 02 — change it
Each lever is applied on its own and measured on its own, so you can see what it bought and stop when the remaining work stops being worth it. Nothing ships until the evaluation suite says quality held.
Baseline
100%
Model tiering
−24%
Context reduction
−18%
Caching
−15%
Batching & scheduling
−8%
After
~35%
Model tiering
Route each task to the cheapest model that still passes its evaluation, with automatic escalation when confidence is low.
Effort
Low
Guarded by
Per-task eval suite
Context reduction
Send the passages that matter instead of the whole corpus, and stop re-sending context the model has already been given.
Effort
Medium
Guarded by
Answer quality benchmark
Caching
Exact-match and semantic caches in front of the model, plus provider-side prompt caching for stable system context.
Effort
Low
Guarded by
Staleness checks
Batching & scheduling
Move latency-insensitive work off the interactive path so it can run batched, off-peak, and at a lower rate.
Effort
Medium
Guarded by
SLA regression tests
Reductions shown are an illustrative composite, not a guarantee. Your actual numbers come out of the baseline, and every lever is measured against your own evaluations before it ships.
Step 03 — keep it
Savings decay the moment someone ships a new feature against the default model. Routing makes the cheap path the default path, and the evaluation gate makes it safe to leave that way.
Step 1
Cache lookup
Exact and semantic matches return without ever reaching a model.
Step 2
Task classification
The request is scored for difficulty, sensitivity, and latency tolerance.
Step 3
Tier selection
It is routed to the cheapest tier that passes that task type in evaluation.
Step 4
Escalate or return
Low-confidence answers escalate a tier; everything else returns and is logged.
Traffic mix after routing
The frontier model stays available for the work that genuinely needs it. It just stops being the default for work that does not.
effectively free
22%
lowest cost per call
44%
moderate
26%
highest cost per call
8%
What keeps it honest
Every tier change gated by evals
Required
Escalation path always available
Automatic
Quality tracked per task type
Continuous
Provider swap without a rewrite
No lock-in
Where this sits
This service lives mostly at the two ends of the adoption journey: finding out where AI spend actually goes, and putting the controls in place so it stays defensible as usage grows. The engineering work in between is real, but it exists to serve those two.
Discover
Uncover real value
Most engagements start here. We baseline what you are spending, attribute it to features and customers, and price each unit of work so the expensive paths become obvious.
Full spend attribution model
Cost per request and per outcome
Ranked waste and hotspot inventory
Savings case with effort estimates
Build
Take ideas to production
We implement the changes the baseline justifies — routing, caching, context reduction, and batching — each one gated by evaluations so quality is never traded away quietly.
Model routing and fallback logic
Caching layers and context budgets
Evaluation harness per workflow
Cost telemetry in your pipeline
Enable
AI competency for every team
We leave your engineers able to read the dashboards and run a cost review, but deep team enablement is the scope of our AI competency work rather than this engagement.
Cost review runbook
Dashboard walkthrough
Handover session
Govern
Scale without losing control
Savings evaporate without controls. We install the budgets, quotas, alerting, and forecasting that keep spend predictable as adoption grows across more teams.
Budgets and enforced quotas
Anomaly detection and alerting
Per-team chargeback reporting
Spend forecast for scale
How we deliver it
A tokenomics engagement is usually scoped and time-boxed, because the baseline tells you quickly whether there is enough waste to justify going further. From there, teams either take the work in-house or keep us close while adoption grows.
Project-based
A scoped outcome, shipped
A fixed-scope review and optimization sprint: instrument, baseline, ship the highest-value changes, install the controls, hand over.
Engagement shape
Typical length
4–8 weeks
Starts with
Spend baseline
Ends with
Controls handover
What it includes
Instrumentation and attribution across your AI workloads
Ranked savings plan with measured effort and risk
The top optimizations implemented and proven by evals
Budgets, alerts, and dashboards left running in your stack
Embedded engineering
Senior engineers inside your team
A senior engineer inside your team owning AI unit economics as an ongoing concern while your product and usage keep changing.
Engagement shape
Typical length
3–12 months
Renewal
Monthly
Priorities
Your roadmap
What it includes
Cost reviewed alongside every new AI feature, not after launch
Routing and caching tuned continuously as models change
Forecasting and budget planning with your finance partners
Patterns transferred to your engineers through pairing and review
Workshop-based
Hands-on teaching on your stack
A hands-on cohort that teaches your engineers to instrument, attribute, and defend AI spend on your own workloads.
Engagement shape
Typical length
1–2 weeks
Format
Cohort blocks
Run on
Your workloads
What it includes
Token accounting and attribution taught on your real traces
Labs on routing, caching, and context budgeting
How to gate a cost change behind an evaluation
A cost review ritual your team runs after we leave
Most teams start scoped, then decide
A baseline is cheap relative to what it usually finds. Teams commonly run the project first, then either take the practice in-house through a workshop or keep embedded support while AI adoption spreads to more teams.
What changes
The goal is not the lowest possible bill. It is knowing what each AI outcome costs, being able to justify it, and having the controls to keep that true as more of the organization starts using AI.
Visibility
Every dollar attributed
Token spend rolls up by feature, team, customer, and model, so cost conversations start from evidence instead of guesswork.
Efficiency
Lower cost per outcome
Routing, caching, and context work attack the expensive paths directly rather than throttling usage across the board.
Quality
Savings that hold up
Every optimization is gated behind an evaluation suite, so a cheaper system is demonstrably not a worse one.
Control
Predictable at scale
Budgets, quotas, alerts, and forecasts mean the next wave of adoption arrives inside a plan instead of on an invoice.
Signals we help your team move
We make the invisible platform work visible enough to prioritize, fund, and improve.
Bring us the bill nobody can explain
Send us the workloads and the invoice that prompted the question. We will show you where the money is going, what it buys, and what it would take to change the shape of it.
Spend baseline
Instrument your AI workloads and find out what each feature and customer actually costs to serve.
Optimization sprint
Ship the highest-value routing, caching, and context changes with quality held by evaluations.
Ongoing cost ownership
Keep a senior engineer close while AI adoption spreads and the economics keep shifting.