Scaling human-like reasoning for Enterprise AI.
General AI models fail at specialized tasks because they rely on statistical patterns rather than domain logic. We solve this through two distinct offerings: Specialized Reasoning Datasets rigorously extracted from human experts to fine-tune your own models, and Custom Small Language Models deployed natively on-premise to guarantee absolute data sovereignty and domain accuracy.
What we offer
Our Offerings
We enable robust and secure Enterprise AI through two core solutions: specialized reasoning datasets for fine-tuning, and customized small language models deployed on your own infrastructure.
Specialized Reasoning Datasets
Acquire our off-the-shelf, expert-validated reasoning traces to fine-tune your own models on validated expert logic across Legal, Finance, Medical and other high-expertise domains.
Enterprise SLMs
We fine-tune specialized models on your domain logic and deploy them securely on-premise, with licensing and zero external API dependency.
Custom Extraction
Have specialized internal data? We process your unstructured corporate documents through our engine to output tailored reasoning traces for your SFT pipeline.
Why reasoning traces?
Massive public APIs are sophisticated probability engines, not thinking machines. When their deeply ingrained statistical patterns clash with your domain-specific logic, the math of next-token prediction almost always overrides adherence to new rules.
To build reliable Enterprise AI, models require fine-tuning on specialized reasoning traces. These traces teach the models to mathematically align with your own logic.
Semantic Overrides Logic
// Prompt
Assumptions:
The letter s should be written p
The letter p should be written s
The letter a should be written i
Question:
How do we write space?
// SOTA LLM Output
Based on your assumptions, let me work through this:
1. The letter s should be written as p
2. The letter p should be written as s
3. The letter a should be written as i
The word "space" contains: s-p-a-c-e
Applying your rules: s→p, p→s, a→i, c→c, e→e
So "space" would be written as: spice.
The Flaw: The model perfectly narrates the steps, but its statistical bias toward forming a real word overrides the logic. The strict application should yield psice, but the probabilistic weight of spice breaks the deductive chain.
Proven Performance
Appellate Law Reasoning.
SR-AppellateLaw is an example of our specialized Enterprise SLMs, fine-tuned on proprietary legal reasoning traces. Designed for secure on-premise deployment, it significantly outperforms massive public APIs like Claude Sonnet 4.5 on complex legal outcome prediction, for a fraction of the inference cost.
Appellate Law · Comparative Benchmark
Cost Efficiency
27x Less
Inference vs. Claude 4.5
Inference Speed
63x Faster
vs. DeepSeek R1