Technical Visionaries

Stop Paying AI to Reread the Same Instructions

Most AI companies repeat long safety and policy instructions inside every system prompt. SASKI replaces that repeated text with a short middleware decision, cutting prompt size, reducing token waste, and lowering AI operating costs.

SASKI Token ROI Calculator

Compute Monthly Input Cost Reduction
Monthly API Requests 1,000,000
Current Governance Prompt Size 482 tokens
SASKI Compressed Size 68 tokens
Input Token Cost (per 1M tokens)
Tokens Saved / Request
414
Tokens Saved / Month
414M
Monthly Dollar Savings
$1,035

(Use these sample numbers in our calculator above)

Company Type AI Requests / Mo Old Prompt Size With SASKI Model Cost Plain English Takeaway
Growing Startup Customer support AI 100,000 380 repeated tokens 45 tokens $0.15 / 1M input tokens Stops the company from paying the AI to reread the same long instructions on every chat.
Scaling Platform HR, education, or workplace AI 2,500,000 472 repeated tokens 65 tokens $2.50 / 1M input tokens Cuts thousands of unnecessary tokens from every request once traffic becomes expensive.
High Volume Sensitive App Health, wellness, or therapy AI 1,000,000 518 repeated tokens 80 tokens $5.00 / 1M input tokens Moves the heavy repeated instructions out of the expensive model call and replaces them with a short decision.
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The easiest way to understand SASKI is this:

Most companies are making the AI read a giant rulebook every time someone asks a question.

That rulebook may include privacy rules, safety rules, medical limits, crisis instructions, age rules, company policies, and legal disclaimers. The more rules you stuff into the prompt, the more expensive each AI request becomes. It can also make the AI less reliable because it has to sort through thousands of repeated instructions before answering one simple question.

It is like hiring a great chef, then forcing them to read a 40 page health and safety manual before every single food order. It slows everything down, costs more, and increases the chance of confusion.

SASKI works like a health inspector standing at the kitchen door. Before the message reaches the AI, SASKI checks it, applies the governance/rules, removes sensitive information when needed, and decides what the AI is allowed to do. Then it gives the AI a short, clear note:

-Approved "Be supportive. No medical advice. Crisis risk low.”

That tiny note can replace thousands of repeated prompt tokens. So instead of paying an expensive AI model to reread the same rulebook on every request, SASKI handles the rules in the background and sends the AI only what it needs for that moment.

The result is simple: Smaller prompts. Lower token costs. Less confusion. Better control.

Doctors do this every day. Instead of explaining every symptom from scratch, one doctor can say, “Patient presents with acute chest pain, elevated troponin, rule out MI,” and the other doctor instantly understands the larger clinical picture.

A few precise medical terms can replace a long conversation because both sides understand the shorthand. SASKI works the same way by turning a huge block of repeated AI instructions into a short, clear note the model can follow.

SASKI SDK

Prompt Compression Examples

v1.6.2 · May 2026
System Prompt - Developer Written

message_for_llm - SASKI Output

The developer removes governance language from their system prompt. SASKI evaluates the request deterministically and sends the LLM a compact, certified execution decision. The model receives clear instructions without probabilistic policy interpretation.

Pre-LLM Intercept Record deterministic · under 50ms

Every decision above is deterministic, logged, and cryptographically signed before the LLM receives the request. This record is immutable. No probabilistic model produced it. No system prompt was asked to remember it.

Most AI safety tools sit around the system prompt instead of shrinking it. This table shows which approaches actually reduce repeated prompt tokens and which ones simply add another layer of cost.

Approach Helps Liability? Shrinks Prompt? Main Weakness
Bigger System Prompts Stuffing rules into the LLM Somewhat No Expensive, unreliable, and causes massive token bloat.
Prompt Management Tools LangSmith, Portkey, etc. Somewhat Not Usually Organizes prompts, but does not enforce runtime policy.
Cloud Guardrails AWS Bedrock, Azure Safety Yes Sometimes Broad controls; lacks targeted statutory logic execution.
AI Guardrail Products Lakera, NeMo, Protect AI Yes Sometimes Often filter based; rarely handles deterministic governance.
PII Redaction Microsoft Presidio, Nightfall Yes Partly Privacy only; misses compliance, age, and crisis rules.
Output Moderation APIs Post-generation checks Yes Partly Happens after the LLM has already processed the data.
RAG Permission Control Knowledge base access filters Yes Yes Narrow scope; only limits context, does not enforce behavioral rules.
Legal Disclaimers Paper protection, consent screens Paper Only No Zero technical enforcement of the stated policies.
SASKI Middleware Deterministic Execution Layer YES YES Moves liability logic entirely out of the prompt. Replaces bloat with a compact execution command.

Most AI developers in regulated verticals are carrying 400 to 600 tokens of governance language in every system prompt, the crisis handling rules, the PII redaction instructions, the HIPAA and COPPA compliance clauses, the escalation logic. That language repeats on every single request regardless of what the user says.

SASKI pulls that entire governance layer out of your prompt and runs it deterministically before your LLM ever sees the message. What we send the model instead is a compact certified execution decision, under 100 tokens, that tells it exactly what it's allowed to do on this specific request.

You keep your product prompt. Your persona, your knowledge base, your tone. That stays exactly as you wrote it. What disappears is the governance overhead.

And what you get in return, at no additional latency cost, is a cryptographically signed receipt for every single decision, the attestation your underwriter needs and the audit record your legal team needs when AI law comes knocking.