Bio-inspired adapter enhancement with pay-for-results pricing, plus research-based bias detection. Built on validated results — 17/3 win rate across 20 benchmarks.
You only pay for accuracy gains actually achieved. Price scales with difficulty — you control the cap.
Bio-inspired adapter technology validated on public benchmarks. The model is published on HuggingFace — everything is verifiable.
These results are on all-MiniLM-L6-v2 — a 22M-param, 6-layer model already distilled and heavily optimized. One of the hardest targets to improve. Larger models with more layers and parameters offer substantially more room for bio-adapter improvement.
Bio-inspired adapters discovered through intelligent search, applied to your models.
Your model, baseline accuracy, target. What is each accuracy point worth to your business?
~1,000 experiments on your data, discovering optimal bio-adapter configurations.
Results validated on your held-out test set. You see the improvement before paying.
Enhanced model delivered. Adapters are lightweight — same inference infrastructure.
Optimized bio-adapter search and deployment. We find the optimal configuration across the pruned search space and deliver a production-ready enhanced model.
A 5-point accuracy improvement on a production model typically translates to 10–30% fewer errors reaching customers. For models processing thousands of queries per day, this compounds into significant value. Pay-for-results pricing means zero risk — you only pay for improvement actually delivered.
Legal AI, customer support, enterprise search — where retrieval accuracy directly impacts user trust.
Competitive performance without competitive GPU costs. Enhance what you have instead of training from scratch.
Banks, legaltech, medtech — can't expose data externally, need improvement on existing infrastructure.
Model-as-a-service vendors needing consistent performance boosts across multiple client deployments.
Research-based bias analysis built on the GENbAIs framework — 8 models tested, 2,960 responses analyzed, 5,807 bias instances documented.
Stanford Law research shows companies deploying AI face direct legal liability for bias. Most don't know their exposure.
Systematic analysis of bias patterns in your AI deployments using the GENbAIs six-dimensional methodology.
Build internal capabilities for ongoing bias monitoring based on proven research methods.
Bias detection reveals cognitive weaknesses in your AI deployment. Model enhancement fixes them. Organizations that do both get a clear picture of what's wrong and a concrete solution — with measurable before-and-after metrics.
Published model on HuggingFace. 20 benchmarks. You can verify everything before engaging.
Enhancement pricing tied to achieved improvement. No improvement, no payment.
You work directly with the researcher — not junior consultants.
Independent analysis not influenced by relationships with AI providers.
PoC in 3–5 days. Full enhancement in 1–2 weeks.
Open methodology, published paper, and public benchmark results.
Tell us your model, your baseline, and your target. We'll tell you what's realistic and what it would cost — no obligation.