AICRAFT

We build structured logic layers for enterprise LLMs — reducing hallucinations, standardizing output, and controlling costs. Boutique consul...
Dordrecht, NL
Created byProfile picturesuperlineup9f
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@superlineup9fProfile pictureMar 28
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Welcome to AICRAFT

Hey — glad you're here.


Here's how to get the most out of AICRAFT:


1. Start with the Audit. Upload your ChatGPT or Claude usage logs and we'll map out exactly where you're leaking cost, generating hallucinations, or exposing PII.


2. Jump into Chat. Got questions about your report, or want to talk through architecture decisions? That's what it's for. You'll hear directly from the founder — no support reps, no ticket queues.


3. Check Updates. This feed is where we post new prompt templates, case studies, and methodology updates. Worth keeping an eye on.


If you're on the Starter Audit, you'll have your full risk report within 5 business days. If you need something deeper — custom Super-Prompts, full integration, or ongoing retainer — just ask in chat.


Let's build something bulletproof.

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@superlineup9fProfile pictureMar 28

The 3 places every enterprise LLM deployment leaks money

We've audited dozens of enterprise ChatGPT and Claude deployments over the past year. The same three cost leaks show up almost every time:


1. Redundant context stuffing.

Most teams dump their entire knowledge base into the system prompt on every call. At $15-60/M tokens for GPT-4-class models, a 4,000-token system prompt across 10,000 daily API calls is $600-2,400/month in pure waste. The fix: constrained retrieval that injects only what's needed per query.


2. No output validation = hallucination rework.

When your customer support bot makes up a refund policy that doesn't exist, someone has to clean it up manually. We've seen teams burning 15-20 hours/week on hallucination cleanup alone. Structured output schemas + assertion layers cut this to near zero.


3. Model routing ignorance.

Not every query needs GPT-4. 60-70% of typical enterprise queries can be handled by smaller, cheaper models with identical output quality. But most teams route everything through their most expensive model because they never built a classifier.


If you're a CTO or engineering lead deploying LLMs into production — run a quick audit. Pull your last 30 days of API logs, sort by token count, and check what percentage of calls actually needed your top-tier model.


If the answer is "I don't know" — that's the problem.


We built AICRAFT to fix exactly this. Full audit, custom prompt architecture, API integration, and team training. Start with a free trial or a $249 Starter Audit.