Wirkung vs AiGanak
A side-by-side comparison built from both listings. Blank cells mean the company has not given that detail; we never guess.
Wirkung compared with AiGanak
| Detail | 
Wirkung | 
AiGanak |
|---|
| What it does | Everything has an algorithm
| AI systems engineering, language model research and cost optimization
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| Stage | Not given
| Established
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| Founded | Not given
| 2021
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| Based in | Not given
| IN
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| Pricing model | Not given
| Not given
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| Pricing | Not given
| Free tools: LLM Bill Analyzer, API pricing calculator, price alerts. LLM Cost Report: USD 149 (2 working days); Founding clients: USD 750. LLM Cost & Reliability Audit: USD 1,500 (5-7 working days). Sample report available free by email.
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| Key features | - Constant-state attention: models serve any context length from the same memory per request
- HCGM: models run from disk storage instead of memory
- M-Rail: context processed by a model can be reused instantly in future requests
- CPUDA/sGPU: run unmodified CUDA programs on CPUs with bit-identical results
- Organ grafting: transfer capabilities between models without training
- Personal instruction set (ISA) compiled on device from phone signals
- Population ISA: compile laws from many people without personal records
- Digital twins of customers, fleets, cities or markets
- Results identical across all machines and architectures
- Computation cost follows novelty not data size
| - LLM Bill Analyzer for usage export analysis
- API pricing calculator for OpenAI, Anthropic and Google models
- Multi-agent workflow automation on n8n and Make
- Prompt hardening against injection and token waste
- Private and on-premise LLM deployment
- Custom small and large language model development
- LLM Cost & Reliability Audit with log analysis
- Price change alerts for OpenAI, Anthropic and Google
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| What makes it different | - Constant memory cost regardless of context length or data size
- Exact reuse: every computed result is kept and reused instantly
- Portable execution with bit-identical results across all hardware
- Models run from storage without retraining or fitting in memory
- Privacy-preserving: raw data never leaves the device
| - Client retains ownership of all code, prompts, workflows and fine-tuned model weights
- Private and on-premise deployment option protecting enterprise data
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| Community votes | 0 | 0 |
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| DR (Domain Rating by Ahrefs) | Not checked yet ● no change since last check | 4 ● no change since last check |
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| Trust Flow (Majestic) | 0 | 0 |
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| Citation Flow (Majestic) | 0 | 0 |
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| Referring domains (Majestic) | 0 | 2 |
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3 details filled in for both companies.
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