Toki — Token Efficiency Layer

Toki — Cut LLM Token Use by 30%

A drop-in preprocessing layer that compresses prompts before they reach the model, reducing API costs and improving throughput — no model changes required.

Request Pilot See Demo
Stage
Working MVP
30%
Token reduction (typical)
Drop-in preprocessing — integrates via SDK or CLI

Quick Demo

Copy/paste into your pipeline to see immediate token savings.

Before: tokens = 1000

After Toki: tokens = 700 (30% reduction)
Integrates as: 
pip install toki-sdk from toki import compress, decompress c = compress(prompt)

send c to model (or inject into pipeline)

output = model.infer(c) final = decompress(output)

How It Helps

Lower Costs
Less token usage on APIs -> lower bills.
Faster Inference
Smaller payloads = lower latency.
Edge Ready
Makes LLMs practical on mobile and edge devices.
No Model Changes
Works as a preprocessing layer — compatible with any tokenizer.

Pilot Options

  • Proof-of-value pilot (2–4 weeks)
  • Integration support & SLA
  • Enterprise licensing or revenue share

Contact

Connect for pilots, partnerships, or technical documentation.

Name
Scott

Company
Digital Mineral Solutions LLC

Email
scott@digitalmineralsolutions.com

LinkedIn
/in/outsidegems

Trust & Compliance

Designed for secure deployments. We can provide NDA/proof-of-concept on request.

Next Steps

  1. Save this page to your phone or print the sign text
  2. Use the one-liner: “I cut token use 30% — ask me how”
  3. Offer to run a 2-week pilot for paid PoV