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Prompt as per usual
Use your LLM of choice. TRS integrates directly into your company's AI stack.
Stop wasting
tokens.
.↓
TRS
Token Reduction System
Reduce AI costs by 85%
Model classification routes each request to the right level of intelligence before tokens are spent.
Classification routes simple tasks to lower-cost models and reserves premier models for complex work.
- Classifies task complexity, risk, and context requirements.
- Routes routine work to lower-cost models and reserves premier models for hard calls.
- Keeps routine prompts off premier models unless the classification score calls for them.
Model classification
We assign the request to the cheapest model class that can satisfy the task reliably.
Token optimization
Reduce spend by matching each request with the lowest-cost model class that can do the work.
What is AI overkill costing you?
Your workload and provider mix determine the final savings. The estimate excludes implementation costs.
Estimated savings with TRS
Current spend
$50,000
With TRS
$10,000
Annual savings
$480,000
Current
$50,000
With TRS
$10,000
Executive dashboard
Control AI spend without retraining every employee.
Asking every employee to understand model pricing, capabilities, and routing is an impossible training task. TRS makes the decision automatically while the executive dashboard gives leaders one clear view of usage, savings, and policy performance.
- No new workflow for employees to learn.
- Company-wide visibility into AI spend and savings.
- Central policies that keep every team on the right model.
Teams are wasting millions of dollars on AI.
TRS optimizes your company's tokens without sacrificing productivity and performance. It reads the request, chooses the right model lane, and keeps simple work out of premier-model paths.
01
Keep high-intelligence calls for work that actually needs them.
02
Move simple requests through cheaper routes without changing the product surface.
03
Give operators a clear policy layer for cost, latency, and quality tradeoffs.
Book a free token audit today.
We review your model usage and request mix, then show where lower-cost routes can handle the work.
Usage map
See which providers and models account for your current spend.
Savings plan
Get a savings range and the routing changes behind it.
Sample token audit
Your AI usage at a glance
Sample 01
01
Usage map
Where current model spend is going
Premier models68%
Standard models22%
Fast models10%
02
Savings plan
Illustrative 80% cost reduction
Current spend
$50,000
With TRS routing
$10,000
Potential monthly savings
80% reduction
$40,000
Give your AI budget more room.
Zumah integrates pre-inference compression and attention-state reuse into your LLM stack, reducing the token footprint and computational overhead of eligible requests.
Pre-inference context compression
Zumah compresses eligible context before model execution, reducing the input sequence length and the computational load of prompt processing.
Attention-state reuse
Eligible requests reuse intermediate attention states computed for shared input prefixes, reducing redundant prefill operations and the inference costs associated with repeated context.
Request optimization
With Zumah
Your input
Less to process
About us
co-founder
Jeremy focuses on analytical problem solving, applied AI, and data-heavy systems. As a Machine Learning and Data Science Intern at NT Concepts, he brings deep technical depth to architecture, modeling, and early product decisions.
co-founder
Sam focuses on the creative side of product: interface feel, user flow, and polished execution. As a Software Engineering Intern at Capital One, he turns ideas into web and mobile products that feel clean and usable.