StereoCognition™
The Epistemic AI Layer
Reduces compute costs 40–60% and cuts error rates up to 50% — without modifying your models, your data, or your security boundaries.
Observed across tested configurations and workloads.
The Scaling Problem
As organizations deploy large language models across critical workflows, two systemic challenges emerge — and neither is solved at the model layer.
Compute costs grow faster than value delivered. Inference at scale becomes the dominant line item, and optimization within individual models yields diminishing returns.
Error rates remain stubbornly high. Hallucinations, inconsistent outputs, and quality variance are not edge cases — they are baseline behaviors of probabilistic systems. Current mitigation approaches treat symptoms after generation. The problem is upstream.
“As generative AI scales, reliability and cost control become system-level requirements — not model features.”
The Solution
StereoCognition operates at the inference control layer — the architectural position between model execution and committed output. It governs how AI systems process and deliver results, without altering the models themselves.
Unlike post-hoc filtering or model-level optimization approaches, StereoCognition intervenes before output is committed. The result: fewer wasted compute cycles, fewer errors reaching production, and measurable improvement in output reliability — across any model architecture.
StereoCognition™
Inference Control Layer
Model-agnostic. Architecture-independent. Validated across 8+ model families.
Validation
Performance claims are grounded in structured experimentation — not benchmarks selected for favorable comparison.
40–60%
Compute cost reduction
∼50%
Error rate reduction
0.935
AUC hallucination detection
100K+
Total evaluations
113
Controlled experiments
8+
Model architectures tested
All metrics observed across tested configurations and workloads. Results are reproducible under controlled conditions. Evaluation engagements are designed to validate these results on your workloads, in your environment.
Deployment
API-based deployment. Connects to your existing inference pipeline. No architectural changes required.
The control layer governs inference execution in real time. Models run as-is. StereoCognition manages what happens between request and response.
Every intervention is logged and auditable. Performance gains are quantified against your baseline — verified independently, not self-reported.
Patent pending. Fully testable without methodology exposure. Your models stay yours. Your data stays yours.
Economics
StereoCognition is priced on verified performance. You pay a share of the value demonstrably created. If compute savings and error reduction are not measured and confirmed, there is no charge.
Performance-based fee
30%
of verified savings
If savings are not verified
$0
No commitment, no risk
This is not a SaaS subscription. This is infrastructure that earns its place in your stack by producing measurable, auditable results — every billing cycle.
Enterprise Trust
Inference data is processed in transit. Nothing is stored, cached, or retained.
Your models are not modified, fine-tuned, or accessed beyond the inference API surface.
Deployment does not require new network permissions, elevated access, or changes to your security posture.
Every decision the control layer makes is logged and available for independent review.
Who It Serves
Reduce inference cost per query. Improve output reliability at the platform level. Offer your customers better performance without retraining or architecture changes.
Add a governance layer to your AI services portfolio. Differentiate on reliability and cost efficiency — the two dimensions enterprise customers prioritize.
Deploy AI with confidence. Reduce the operational cost of inference-heavy workflows. Cut hallucination rates in production. Maintain full control over your data and models.
Deployable across models, platforms, and infrastructure without modification.
Run it on your infrastructure, with your models, on your data. We measure the results together. If the numbers do not justify deployment, you owe nothing.
2–4
Weeks
100K
Queries
Full
Telemetry
$0
Client Cost