Talifun Tokenizer
AI Infrastructure
AI Infrastructure

The performance layer every AI system is missing.

Cut tokenization latency. Lower compute waste. Increase AI throughput without changing your stack.

Speed
19×
Faster than tiktoken
Python o200k benchmark performance for high-volume AI workloads.
Economics
~95%
Gross margin
High-margin IP with zero infrastructure cost to operate.
Integration
Drop-in
Python · Node.js · Rust
No rewrites, no retraining, and no architecture change.
www.talifun.com
The Problem 03/14
The Problem

AI infrastructure has a hidden cost that grows with every request.

Tokenization sits in the critical path of every AI interaction. It is treated as plumbing — but at the scale of modern AI workloads, slow tokenization means idle GPUs, bloated latency, and wasted compute budget.

01

The Bottleneck Is Real

The most widely used tokenizer processes 35–80 MB/s. At 1 billion tokens per day, that is over 3 hours of CPU time your GPU infrastructure spends idle — waiting for data that arrives too slowly.

02

The Cost Multiplies at Scale

Long contexts, agentic loops, and RAG retrieval mean tokenization no longer happens once per request — it happens repeatedly. Every agent loop, every retrieval cycle, every context rebuild adds to the bill.

See appendix for source data and methodology.

www.talifun.com 03/14
The Timing 04/14
The Timing

The tokenization tax is compounding. Not shrinking.

AI workloads have fundamentally changed. The tools processing them have not kept up.

Agents Multiply Tokenization

An AI agent plans, retrieves, calls tools, rebuilds context, and reasons over intermediate results before producing one answer. It may tokenize 4–12× per task. Every loop is a cost.

Context Is Getting Longer

Modern AI systems bring in conversation history, retrieved documents, tool outputs, logs, contracts, and customer records. Context is rebuilt continuously. The volume tokenized per session keeps growing.

Volume Is Enormous and Growing

OpenAI processes 3–7 billion AI requests per day. Google processes 4–12 billion. Every request is a tokenization event. As context windows expand, tokenization's share of infrastructure cost expands with them.

"The teams building at this scale need a tokenizer that was built for it."

www.talifun.com 04/14
The Solution 05/14
The Solution

One swap. Instant savings. Zero rewrites.

Replace your tokenizer with Talifun. Same API. Same BPE vocabulary. Same model compatibility. Up to 19× faster — with no architectural changes required.

Input
Raw Text
↗ Drop-in Replacement
Talifun Tokenizer
Inference
Model
Output
Fast Response
19×

Up to 19× Faster

Consistent throughput gains across Python, Node.js, and Rust. Sub-millisecond p99 latency in every runtime.

→

Drop-In Replacement

pip install · npm install · cargo add. Same API shape. No rewrites. No migration project.

www.talifun.com 05/14
The Product 06/14
The Product

One tokenizer. Every AI stack.

Production-ready BPE tokenization for every team. Plugs directly into existing pipelines without architectural changes.

Python — AI Research & Training
Research & Training

The standard for AI research, training, and data preparation. Drop-in replacement for tiktoken.

pip install talifun
Node.js — AI Apps & Agents
Apps & Agents

The standard for AI applications, agentic loops, and full-stack web development.

npm install talifun
Rust — High-Performance Infrastructure
Inference & Infra

The standard for inference engines, high-throughput pipelines, and low-level infrastructure.

cargo add talifun
www.talifun.com 06/14
Founding Team 07/14
Founding Team

Built to take a technical breakthrough to market.

Systems engineering, product development, and commercial execution.

Taliesin Sisson
Taliesin Sisson
Founder & CEO

Systems architect and entrepreneur. First startup in 1998 — a CMS-driven marketplace with 700 businesses. Decades of experience building high-performance, low-level infrastructure for enterprise scale.

Heather Vivian
Heather Vivian
Co-Founder & Chief Brand Officer

Senior digital designer and AI product builder with over 15 years of experience across SaaS, fintech, gaming, and retail. Leads Talifun's brand identity, visual systems, and go-to-market design. Clients include ITV, Bwin, and East of England Co-op.

Noeleen Sisson
Noeleen Sisson
Co-Founder & Head of Frontend

Frontend developer and creative producer responsible for Talifun's web presence and video communications. Background spans e-commerce entrepreneurship — founding and running an Amazon marketplace business — and operational roles at Ocado and Witch.

www.talifun.com 07/14
Business Model 08/14
Business Model

High-margin IP. No infrastructure cost. Profitable from deal one.

~95%
Gross Margin
Deal 1
Break-Even
Zero
Infra Cost
Path 1 — Default Motion
$3M
Lifetime License

Non-exclusive perpetual right to deploy internally. For AI platforms, inference providers, RAG vendors, and data platforms.

Negotiation band $500k–$12M depending on deployment scope  ·  Annual support & updates 15–20% of license price
Path 2 — Strategic Motion
$50M
Exclusive IP Acquisition

Full IP transfer including source code, derivative rights, and redistribution rights. Buyer captures multi-year value and denies competitors access.

Soft floor $30M  ·  With full rights $60M+
www.talifun.com 08/14
Milestones 09/14
Milestones

The product is complete. This is a sales motion.

NOW
Today
Product Complete
Production-ready across all runtimes
Benchmarks validated
Website live
3mo
3 Months
First Deal
First license closed
$500k–$3M revenue
6mo
6 Months
Pipeline Built
3–5 enterprise conversations
$1.5M–$9M pipeline
12mo
12 Months
Scale or Exit
Multiple licenses or acquisition
$5M–$50M
18mo
18 Months
Recurring Revenue
Support & maintenance
+$750k–$2M/yr
3 Months
$500k–$3M
First deal closed
6 Months
$1.5M–$9M
Pipeline converted
12 Months
$5M–$50M
Scale or strategic exit
18 Months
+$750k–$2M/yr
Recurring support
www.talifun.com 09/14
Competitive Landscape 10/14
Competitive Landscape

No existing tokenizer was built for production AI scale.

Existing tokenizers were designed for correctness and compatibility — not for long contexts, agentic loops, or high-volume API pipelines.

Tokenizer
Python MB/s
p99 Latency
Node.js
All 3 Runtimes
Best-in-class
Talifun ✦
832
0.34 ms
✓
✓
✓
tiktoken
36
6.87 ms
✓
✓
✗
HF Tokenizers
26
3.44 ms
Partial
Partial
✗
RS-BPE
44
8.59 ms
✗
✗
✗
TokenDagger
34
5.57 ms
✗
✗
✗
Key differentiator: Talifun is the only tokenizer delivering best-in-class throughput AND sub-millisecond latency across all three major AI development runtimes simultaneously.
www.talifun.com 10/14
Value in Production 11/14
Value in Production

Faster tokenization means lower costs and more capacity across every AI workload.

More inference capacity from the same hardware
More QPS headroom. Better p99 SLA compliance. Meaningful latency reduction at every context size.
2.5%–14% lower end-to-end inference latency
Faster data cycles and training throughput
More offline corpus build runs per day. Faster dataset refresh. Less idle GPU time waiting for tokenized input.
+43% more runs/day
More headroom as agents and context keep growing
Lower task latency in agentic RAG. Dramatically more evaluation runs per day.
7%–17% lower agentic RAG latency · +55%–60% more eval runs/day
Use Case
Business Impact
Inference / Chat
2.5%–14% lower latency · more requests per server
Agentic RAG
7%–17% lower task latency · more throughput
Offline Corpus Build
+43% more runs/day · faster model iteration
Evaluation / Regression
+55%–60% more runs/day · faster release cycles
API Gateway Accounting
8%–37% lower control-plane latency

Modelled across production workload scenarios. Full methodology in appendix.

www.talifun.com 11/14
Market Size 12/14
Market Size

The five largest AI platforms represent $15M–$50M+ in reachable near-term revenue.

$15M+
5 Lifetime Licenses at $3M per deal
$50M
1 Exclusive Acquisition — strategic buyer
Company
Requests/Day
Est. Annual Saving
Target License
OpenAI
3B–7B
$0.5M–$38M/yr
$5M–$10M
Google
4B–12B
$0.8M–$67M/yr
$7M–$12M
Anthropic
200M–1.5B
$0.07M–$10M/yr
$3M–$5M
Microsoft
200M–800M
$0.06M–$5.6M/yr
$2M–$4M
Meta AI
300M–1B
$0.1M–$7.9M/yr
$2M–$4M

Annual saving is modelled value capture based on public usage anchors and production workload scenarios. See appendix for full methodology.

www.talifun.com 12/14
Vision & Ask 13/14
Vision & Ask

Build the performance layer for the future of AI.

As AI becomes more context-heavy, more data-intensive, and more agentic, tokenization becomes more important — not less. The product is built. The market is ready. The team is here.

Strategic Acquisition
US$30M – US$60M+
Licensing Partners
US$1.5M – US$5M per deal
Seed Investment
GTM & Sales Capital
www.talifun.com 13/14
14/14

Appendix

www.talifun.com 14/14
Appendix A1 A1
A1 — Market Evaluation
Full Per-Company Evaluation

Modelled annual savings and target license pricing. Source: public usage anchors.

Company
Requests/Day
Primary Use Case
Est. Annual Saving
Target License
OpenAI
3B–7B
Inference, API
$0.5M–$38M/yr
$5M–$10M
Google
4B–12B
Inference, Search AI
$0.8M–$67M/yr
$7M–$12M
Microsoft
200M–800M
Enterprise API
$0.06M–$5.6M/yr
$2M–$4M
Meta AI
300M–1B
Social AI, API
$0.1M–$7.9M/yr
$2M–$4M
AWS
150M–600M
Managed API
$0.05M–$4.2M/yr
$2M–$3.5M
Anthropic
200M–1.5B
API, Agentic
$0.07M–$10M/yr
$3M–$5M
xAI (Grok)
100M–500M
Inference, Agentic
$0.03M–$3.5M/yr
$1.5M–$3M
Perplexity
30M–120M
Search, RAG
$0.01M–$0.8M/yr
$500k–$1.5M
DeepSeek
100M–400M
API, Training
$0.03M–$2.8M/yr
$1M–$2.5M
ByteDance (Doubao)
400M–2B
Inference, Social AI
$0.1M–$14M/yr
$3M–$6M
Baidu (ERNIE)
200M–1B
Inference, Search
$0.06M–$7M/yr
$2M–$4M
Alibaba (Qwen)
300M–1.5B
API, Enterprise AI
$0.09M–$10.5M/yr
$2.5M–$5M
www.talifun.com A1
Appendix A2 A2
A2 — Workload Analysis
Value by Workload Scenario

End-to-end improvement estimates across all 9 production workload types.

Use Case
Improvement
Business Impact
Inference / Chat
2.5%–14.1% lower latency
Better p99 SLA · more requests per server
Online Training Input
+16.8% more runs/day
Less idle GPU · faster model iteration
Offline Corpus Build
+42.6% more runs/day
Faster dataset refresh · shorter build cycle
RAG Ingest / Indexing
+5.8% more runs/day
Faster knowledge base refresh
Online RAG Query-Time
4.8%–12.6% lower latency
Lower end-to-end retrieval latency
Agentic RAG Orchestration
6.8%–16.9% lower latency
Compounding gains across loops · more throughput
API Gateway Token Accounting
7.9%–37.4% lower latency
Lower control-plane overhead
Moderation / Classification Sidecar
4.1%–4.4% lower latency
Safety checks add less total latency
Evaluation / Regression
54.7%–59.5% more runs/day
Faster release cycles · broader test coverage
www.talifun.com A2
Appendix A3 A3
A3 — Benchmark Detail
Full Benchmark Numbers — o200k

Throughput and p99 latency across all runtimes. Source: o200k benchmark suite.

Python
Talifun
832 MB/s
0.34 ms
RS-BPE
44 MB/s
8.59 ms
tiktoken
36 MB/s
6.87 ms
TokenDagger
34 MB/s
5.57 ms
HF Tokenizers
26 MB/s
3.44 ms
Node.js
Talifun
928 MB/s
0.40 ms
AI Tokenizer
98 MB/s
3.39 ms
tiktoken
82 MB/s
4.91 ms
GPT Tokenizer
24 MB/s
2.72 ms
HF Tokenizers
5 MB/s
38.35 ms
Rust
Talifun
943 MB/s
0.23 ms
RS-BPE OpenAI
100 MB/s
1.29 ms
tiktoken-rs
80 MB/s
1.33 ms
HF Tokenizers
38 MB/s
4.69 ms
Splintr
13 MB/s
1.34 ms
~19× Python speedup
vs tiktoken · 832 MB/s · 0.34 ms p99
~9.5× Node.js speedup
vs tiktoken · 928 MB/s · 0.40 ms p99
~9.5× Rust speedup
vs tiktoken-rs · 943 MB/s · 0.23 ms p99
www.talifun.com A3
Appendix A4 A4
A4 — Pricing Logic
Business Value Framework

How Talifun license pricing is anchored to direct, measurable economic value.

Value Driver 1
Direct Infrastructure Savings

Faster tokenization directly reduces CPU time, freeing GPU resources and lowering compute cost. At scale, this represents measurable recovery of previously idle capacity.

Value Driver 2
Product Headroom

Reduced p99 latency means larger prompts, deeper retrieval, and stricter safety checks — all without blowing latency budgets. More revenue capacity from the same hardware.

Value Driver 3
Faster Iteration Speed

+43% more offline corpus runs/day and +55–60% more eval runs/day means faster model iteration, shorter training cycles, and compressed time-to-production for new model versions.

Value Driver 4
Avoided Internal Build Cost

A serious in-house tokenizer effort requires 4–8 strong systems engineers over 9–18 months. Fully loaded replacement cost band: $2M–$8M before achieving performance parity.

Lifetime License
$3M
Band: $500k–$12M
Exclusive Acquisition
$50M
Soft floor $30M · Full rights $60M+

Exclusive acquisition is priced to reflect multi-year value capture AND strategic denial of access to competitors — a durable competitive moat, not just a tooling upgrade.

www.talifun.com A4
Appendix A5 A5
A5 — Pipeline Diagrams
Where Tokenization Sits in the Stack

Tokenization's share of total latency across three core production architectures.

Inference Pipeline — 2.5%–14% tokenization share at 8k–1M tokens
Client
Request
↑ 2.5%–14% share
Tokenization
Talifun: sub-1%
~70–85% share
Model Forward Pass
~5–10%
Detokenize
Output
Response
Offline Training Pipeline — +42.6% more runs/day improvement
Source
Raw Text Corpus
↑ Dominant bottleneck
Tokenization
30–55% of wall time
~20–30%
Buffer / Shuffle
~20–40%
GPU Training Step
Output
Checkpoint
Agentic RAG Pipeline — 6.8%–16.9% latency reduction (compounds per loop)
Input
Task / Query
↑ Repeated 4–12×
Tokenize Context
~15–25%
Vector Search
~50–70%
LLM Reasoning Step
↑ Each loop
Re-tokenize
Output
Final Answer
www.talifun.com A5