01
THE DEFAULT IS WRONG
Too many requests go to frontier models.
Routine work rarely needs frontier capability—yet the most expensive model is everyone’s default. Our benchmark measures the gap.
AI engineering economics
Stop paying
frontier prices for routine work.
Models and harnesses, benchmarked on your own merged PRs. Every task routed to the cheapest profile that passes.
Cost per merged PR—not tokens, vendor slides or vibes.
SEE WHAT WE BELIEVE
Claude Code→OpenAI Codex→Cursor→GitHub Copilot→Custom agents→Prompt infrastructure→
Claude Code→OpenAI Codex→Cursor→GitHub Copilot→Custom agents→Prompt infrastructure→
01 WHAT TEEV AI BELIEVES
Six convictions.
Zero vendor dogma.
Each one is testable—proved or disproved on your own code, not a provider’s benchmark slide.
02
ROUTING
Routing today is immature.
One default model is not routing. Neither are keyword rules, gut feel, or a vendor shuffling its own SKUs.
PROVIDER-NEUTRAL OR IT ISN’T ROUTING
03
BENCHMARKS
Public benchmarks are compromised—and benchmaxed.
Models train on the tests the market watches. Your merged PRs have never been published.
YOUR CODE IS THE ONLY BENCHMARK THAT COUNTS
→ THIS IS WHAT TEEV BENCH BUILDS
04
EXECUTION
Harnesses are underrated.
The same model through two harnesses can differ 2× in cost per task—context re-feeding is usually why.
MODEL + HARNESS + CONFIGURATION
05
ECONOMICS
Token cost is not a useful metric.
Cheap tokens read more, retry more and think in circles. What matters is cost per successful task.
MEASURE OUTCOMES, NOT INPUTS
06
WHERE WE HELP YOU GET TO
The destination: models and harnesses become fungible—and invisible.
Developers describe the work; a routing layer picks the rest—and every part can be swapped as the evidence changes.
A MEASURED DESTINATION, NOT A PLATFORM PITCH
THE OPERATING PRINCIPLE
Every claim above becomes a test. Every test runs on your code. Every recommendation has to survive the evidence.
02 SPEND SCENARIO
What could
better usage buy back?
Directional, and yours to adjust. The real answer depends on your task mix, context and workflow.
↳ No email. No lead form. Just a number.
$
$5k$500k+
Estimated optimisation opportunity
ASSUMPTION · 25% AVOIDABLE SPEND
Our typical scenario—not a promise. Change the assumption above.
Potential annual capacity unlocked
$225,000
Model spend only. Developer time recovered from retries, rework and context churn is not included—and may be larger.
03 HOW WE WORK
From invoice to
measurable routing.
Finance, platform and engineering—usage evidence turned into routing decisions developers actually follow.
A defensible view of spend by team, tool, model and repository.
- Invoice and usage reconciliation
- Cost-per-outcome baselines
- High-variance workflow mapping
Separate productive usage from repeated context, idle loops and weak routing.
- Session-level pattern analysis
- Prompt and context review
- Model-fit assessment
Better defaults inside the developer workflow—not a policy document.
- Model routing and budget rules
- Repository context architecture
- Team-specific playbooks
Track that savings persist and delivery quality holds.
- Before-and-after measurement
- Executive and team scorecards
- Quarterly optimisation loop
04 BENCHMARK
Your merged PRs are already a benchmark. Teev makes it runnable.
Every merged pull request is a completed exam: a real task, solved by your engineers, validated by your reviewers and your CI. Teev converts that history into a private, executable benchmark—then runs every model and harness against it.
01
SCAN
Find the benchmark-grade work.
PR metadata alone shows how many benchmark-grade tasks your history holds—before Teev requests a line of code.
02
EXTRACT
Reconstruct the moment before the fix.
The repository, rebuilt exactly as it stood before the change. The human fix is sealed away as the answer key.
03
VALIDATE
Prove the task can grade itself.
A hidden test from the real change must fail before the fix and pass after it—or the task is discarded.
04
EXECUTE
Run every profile against the same work.
Every model-harness profile attempts the same tasks in identical, disposable environments. Teev records cost per task—not per token.
05
REPORT
Make every comparison reproducible.
Every result traces to benchmark, profile and run. When a new model ships, the same tasks rerun—the comparison is exact.
THE STANDARD
What survives is graded the way your team already grades work: does the test pass?
Run Teev on your history ↗THE QUESTION WE ANSWER
Which AI coding usage is creating leverage, and which is merely creating tokens?
01Cost per merged PR
02Spend by work type
03Agent rework rate
04Time-to-accepted change
05 WAYS TO WORK TOGETHER
Start with the bill.
Finish with a system.
Every engagement is led by practitioners who understand both engineering workflows and the economics behind model usage.
01
2 WEEKS · FIXED SCOPE
Spend diagnostic
Find the drivers, quantify the avoidable share, leave with a 90-day plan.
↗02
6–10 WEEKS · HANDS-ON
Proof sprint
Redesign routing, context and team habits—then prove it in production with Teev.
↗03
QUARTERLY · ADVISORY
AI engineering economics
Ongoing counsel for a fast-changing model and tooling portfolio.
↗A useful first conversation
Bring us the invoice.
We’ll bring the questions.
Thirty minutes: we pressure-test your spend and tell you where we’d look first. No deck. No obligation.