TokenTrim™ · Inference-compression engine

A tenth of the cost. No measurable accuracy loss.

Most of a conversation’s tokens are repeated context, instructions, history, retrieved docs, tool scaffolding. TokenTrim distills and compresses that footprint before it reaches the model, cutting tokens roughly 10× per interaction. Works with our models, or your own.

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Inference compression 98,938 → 9,465 tokens / interaction

Tokens per interactionlivebenchmark

Before · raw context98,938

After · TokenTrim9,894

Compression level90%

drag to explore, tune per flow, model-agnostic

~10×

fewer tokens

−90%

cost per interaction

Tokens / interaction · before

Tokens / interaction · after

Fewer tokens

Added latency budget

What TokenTrim compresses

Repeated instructionsConversation historyRetrieved docs · RAGTool scaffoldingSystem promptsKV cache

Inference compression

Trim the footprint, not the meaning.

TokenTrim sits in front of inference, model-agnostic. It distills repeated context, applies prompt and KV compression, validates against the uncompressed baseline, then serves the model a fraction of the tokens. The signal stays; the bulk goes.

1Distill context 2Compress · prompt + KV 3Validate baseline 4Serve the model

Distill repeated context into a compact representation.

01 · Context distillation

Instructions, conversation history, retrieved documents and tool scaffolding repeat on nearly every interaction. TokenTrim collapses that redundant footprint, prompt and KV compression working together, so the model sees the signal, not the bulk. Pre-inference, in-line, before a single token reaches the model.

1.1Context distillationrepeated instructions & history collapsed losslessly

1.2Prompt & KV compressionretrieved docs and tool scaffolding compacted

1.3Pre-inference, in-lineruns before the call reaches the model

Tokens · before vs. afterpre-inference

TokenTrim 98,938 → 9,465 tokens · ~10× compression

Cheaper inference. Same answers.

02 · Accuracy preserved

Compression only helps if the output holds. Every TokenTrim profile is validated against the uncompressed baseline on resolution, faithfulness and policy adherence, gated, measured and reversible. In benchmarking, the compressed path showed no measurable accuracy loss.

A/B

gated vs. baseline

measurable accuracy loss

Accuracy vs. tokensbaseline-gated

Resolution & faithfulness held within noise · any regression rolls back

Cost-per-resolution trends down as you scale.

03 · Cost per resolution

Token cost is the bulk of inference cost. Without compression, the bill climbs with volume; with TokenTrim it stays flat, about a tenth of the spend at the same quality. Lower cost-per-resolution on every turn, and a faster prefill into the bargain.

~10×

lower token spend

↓ 90%

cost / resolution

Cost over volume$ / interaction

without TokenTrimwith TokenTrim

Works with our models, or your own.

04 · Frontier-model-agnostic

TokenTrim sits in front of inference, not inside any one model. Point it at a hosted frontier model or your on-prem deployment, the compression layer is the same. No retraining, no lock-in. Drop it in front of GPT-5.x, Gemini, Claude or on-prem Llama with one config.

4.1Provider-neutralhosted frontier models or on-prem, one interface

4.2No retrainingdrops in front of existing inference

4.3Bring your own modelyour weights, your VPC, your region

Compression profilemodel-agnostic

Mode

PromptPrompt + KV

Target model

G5GPT-5.x▾

Deployment

On-prem · your VPC▾

Active profile

tokentrim : ratio 10.4× · baseline-gated

validated No measurable accuracy loss vs. the uncompressed baseline.

Latency

Fewer tokens, faster turns.

A smaller footprint is a faster one. Less to encode and decode means lower time-to-first-token and a tighter cost-per-resolution, comfortably inside a sub-500ms voice budget even after compression overhead.

Latency impact · per turnlivems

Baseline prefill~610

Compress step+92

Trimmed prefill~228

Net per turn~320

net positive smaller context nets out faster · inside a sub-500ms voice budget

Frontier-model-agnostic

Works with any model.

TokenTrim compresses the context, not the model. Run it in front of the frontier models you already use, or your own.

GPT-5.x Gemini Claude on-prem Llama your model

What it adds up to

The math of a tenth of the cost.

~0×

Fewer tokens per interaction

From roughly 98,938 to 9,465 tokens on a benchmarked interaction, about a tenth of the footprint reaching the model.

Lower inference spend

Token cost is the bulk of inference cost. Cut the tokens and the bill falls with them, with no measurable accuracy loss.

<0ms

Latency budget intact

Compression overhead nets out positive: smaller context means faster prefill and a tighter cost-per-resolution.

“We dropped inference spend by roughly 90% at the same answer quality. At our volume, TokenTrim paid for itself in the first month, and the cost-per-resolution keeps falling as we scale.”

VP, Platform Engineering

Enterprise AI platform · billions of tokens / month

Validated against the uncompressed baseline before rollout, no measurable accuracy loss, and the compression overhead nets out inside a sub-500ms voice budget.

~10×

fewer tokens / interaction

−90%

inference spend

Models & infrastructure

Drops in front of the stack you already run.

FAQ

TokenTrim™, answered.

Most of a conversation’s tokens are repeated context, system instructions, conversation history, retrieved documents and tool scaffolding that recur on nearly every interaction. TokenTrim distills and compresses that redundant footprint (prompt and KV compression together) into a compact representation before it reaches the model. In benchmarking this took an interaction from roughly 98,938 to 9,465 tokens, about a tenth, with no measurable accuracy loss, because the meaning is preserved while the bulk is removed.

Yes. TokenTrim is frontier-model-agnostic, it sits in front of inference rather than inside any one model. It runs with GPT-5.x, Gemini and Claude, and with on-prem Llama or your own deployment. There’s no retraining and no lock-in; the compression layer is the same regardless of which model serves the interaction.

Every compression profile is A/B-tested against the uncompressed baseline on resolution, faithfulness and policy adherence. Profiles are gated, measured and reversible: a profile only ships if it holds quality, and any regression rolls back automatically. The compressed path is continuously compared to the raw path, so accuracy stays within noise.

It usually helps. A smaller context is faster to encode and decode, which lowers time-to-first-token and tightens cost-per-resolution. Even after the compression step’s own overhead, the net result stays comfortably inside a sub-500ms voice budget on every turn of the interaction.

Yes. Because it’s provider-neutral and drops in front of existing inference, TokenTrim can run against models in our cloud, in your private VPC, on-prem, or pinned to a specific region for data residency, using your own weights and tools.