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.
Deployable across models, platforms, and infrastructure without modification.
As generative AI scales, cost and reliability become system-level constraints.
The Scaling Problem
LLMs are moving into critical workflows.
Two systemic problems emerge — neither is solved at the model layer.
Cost scales faster than value.
Inference becomes the dominant line item. Optimization within individual models yields diminishing returns.
Errors remain structural.
Hallucinations and quality variance are baseline behaviors of probabilistic systems.
Current 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 inference execution without altering the underlying models.
Unlike post-hoc filtering, it intervenes before output is committed.
Fewer wasted cycles. Fewer errors reaching production.
Measurable improvement — across any model architecture.
Inference Request Layer
(Applications, Agents, APIs)
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.
Deployment
API-based deployment. Connects to your existing inference pipeline. No architectural changes required.
StereoCognition governs inference execution in real time, without modifying the underlying models. Your architecture stays intact.
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.
No model modification. No data retention.
Economics
Priced on verified performance.
You pay a share of the value demonstrably created.
If savings 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 while increasing output reliability at the platform level. Deliver better performance without retraining or architectural changes.
Add a governance layer to AI services. Differentiate on reliability and cost efficiency — the dimensions enterprise customers prioritize.
Reduce operational cost of inference-heavy workflows while improving production reliability and consistency.
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