Shrink Input Tokens 3×
Keep the Facts,
Not the Bloat.
Keep the Facts,
Not the Bloat.
Telegrapher.ai compresses the input text, not the neural weights - your models stay unchanged while your documents, memory, and prompts get 55 % smaller.
TELEGRAPHER MESSAGE
TELEGRAPHER: BORN FROM LLM-SCALE PAIN
MISSION: KILL TOKEN-ECONOMY LIMITS → TEAMS BUILD AI DEEPER ∧ CHEAPER ∧ SMARTER
MISSION: KILL TOKEN-ECONOMY LIMITS → TEAMS BUILD AI DEEPER ∧ CHEAPER ∧ SMARTER
AI-MODELS: TEXT-VOLUME↑ ⇒ TOKEN-COST↑
EACH-TOKEN MATTERS (API-CALLS ∨ VECTOR-DB ∨ CONTEXT-WINDOW)
LEGACY-COMPRESSION: DROPS CRITICAL-INFO → COST↔QUALITY TRADEOFF
TELEGRAPHER: DEEP-COMPRESSION ∧ NO-MEANINGFUL-LOSS → NO TRADEOFF
EACH-TOKEN MATTERS (API-CALLS ∨ VECTOR-DB ∨ CONTEXT-WINDOW)
LEGACY-COMPRESSION: DROPS CRITICAL-INFO → COST↔QUALITY TRADEOFF
TELEGRAPHER: DEEP-COMPRESSION ∧ NO-MEANINGFUL-LOSS → NO TRADEOFF
Token Costs Are a Growing Challenge
As AI models process ever‑larger volumes of text, token costs quickly pile up. Every token counts—whether you’re paying for API calls, vector‑DB storage, or context‑window real estate. Standard compression tools slash costs by cutting corners on meaning, forcing teams to choose between price and quality.
Telegrapher ends that trade‑off, delivering deep compression with no meaningful information loss.
As AI models process ever‑larger volumes of text, token costs quickly pile up. Every token counts—whether you’re paying for API calls, vector‑DB storage, or context‑window real estate. Standard compression tools slash costs by cutting corners on meaning, forcing teams to choose between price and quality.
Telegrapher ends that trade‑off, delivering deep compression with no meaningful information loss.
The Telegrapher Advantage
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Up to 70 % fewer tokens
Smaller context windows, lighter storage bills, faster inference.
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High‑fidelity retention
Internal audits find < 1 substantive fact lost per 1 000 tokens processed.
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Model‑agnostic integration
Plug‑and‑play with any LLM, RAG stack, or vector database—no retraining required.
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LongBench
Dataset
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61 %
Token Reduction
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2.6 ×
Compression Ratio
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98 %
Factual Retention*
CONVERSION EXAMPLE
HIST: 19TH-C TELEGRAPHY ; COST/WORD HIGH → OPERATORS COMPRESS MSGS
METHODS: DROP REDUNDANT WORDS ; REDUCE SENTENCES→MEANING-DENSE PHRASES ; USE SHORTHAND + SYMBOLS
LEGACY: STYLE→JOURNALISM(“TELEGRAPHIC”) ∧ SCIENTIFIC NOTATION ∧ COMPUTATIONAL LINGUISTICS
GOAL: MAX CLARITY ∧ MIN LENGTH
MODERN: TE VARIANTS POWER AI PROMPT-OPTIMISATION
METHODS: DROP REDUNDANT WORDS ; REDUCE SENTENCES→MEANING-DENSE PHRASES ; USE SHORTHAND + SYMBOLS
LEGACY: STYLE→JOURNALISM(“TELEGRAPHIC”) ∧ SCIENTIFIC NOTATION ∧ COMPUTATIONAL LINGUISTICS
GOAL: MAX CLARITY ∧ MIN LENGTH
MODERN: TE VARIANTS POWER AI PROMPT-OPTIMISATION
Telegraph English traces its historical roots back to the concise, abbreviated communication style developed in the era of telegraphy in the 19th century, when sending messages was costly and charged by the word or character. This necessity spurred operators to eliminate redundant words, reduce sentences to minimal, meaning-rich phrases, and employ shorthand and symbolic notation to efficiently convey essential information. Over time, this practice influenced various fields like journalism ("telegraphic style"), scientific notation, and even modern computational linguistics, evolving into a compact, structured language aimed at maximizing clarity while minimizing length, ultimately inspiring contemporary variants tailored to computational contexts such as AI prompt optimization.
how telegraph english works
Seamless Integration, Powerful Results.
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Compress & Transform – `/compress` rewrites raw text into Telegraph English, shrinking tokens by ≈ 65 % while keeping the facts. - Smart Chunk & Embed – the same pass groups TE lines into semantic chunks and returns ready-made embeddings.
- Cheaper Retrieval – at query time the wrapper fetches compressed chunks, so only 35 % of the original tokens hit GPT/Claude, cutting latency by up to 55 %.
- Fidelity on Demand – each chunk carries an ID map; decode back to full wording whenever you need audit-grade originals.
- Metered & Secure – start free with 2 M TE-tokens/month, encrypt data end-to-end, auto-purge after 30 days; SOC 2 in progress.
- Live in a Day – batch API & wrappers let most teams A/B test teRAGs before lunch.
USE CASES
Ideal Applications for Telegraph English
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RAG Systems
Store 2‑3× more context under the same token budget, boosting retrieval quality. -
Vector Databases
Lower embedding and storage costs, maintain search precision. -
API Cost Optimisation
Cut spend on high‑volume vendor endpoints without sacrificing capability. -
Long‑Context Applications
it more content into fixed windows for richer reasoning.
what our users say
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Early‑access perks:
Early‑access perks:
- Priority onboarding support
- Influence on feature roadmap
- Preferred pricing at launch.
