OrbDB provides an enterprise AI reliability platform that mathematically guarantees model performance, addressing the gap between AI reasoning capability and error awareness. The platform enables organizations to set specific accuracy thresholds—such as 90% recall for drug discovery—and delivers statistically bounded predictions across use cases like recommendations and spam detection. By distinguishing confident answers from uncertain ones, OrbDB routes ambiguous cases to human review while automating decisions where confidence is warranted.
Funding
SEK 15.8M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
KVTIFounders
Product
Problem
Large language models and AI systems generate confidence scores that are fabricated tokens rather than calibrated probabilities, creating a systematic gap between what models claim and what they actually know. This overconfidence leads enterprises to deploy AI without formal error guarantees, exposing them to liability from incorrect predictions in critical business processes.
Solution
OrbDB delivers error-conscious AI by mathematically bounding error rates and providing statistical guarantees on model outputs. The platform lets organizations define exactly how wrong they are willing to be, then calibrates predictions accordingly—returning a clear answer when the model is certain and a narrowed shortlist when it is not. This approach transforms raw model outputs into reliable predictions with measurable confidence, enabling enterprises to automate decisions with formal assurance. OrbDB works across diverse use cases, allowing organizations to set different confidence thresholds for different policies, such as aggressive filtering for spam detection or high-recall requirements for drug discovery.
Target Audience
Enterprises deploying AI in high-stakes domains including pharmaceutical drug discovery, e-commerce recommendation systems, and email security, where prediction errors carry significant operational or financial consequences.
Features
- Mathematically guaranteed error bounds that replace probabilistic confidence scores with calibrated, measured certainty
- Configurable performance thresholds per use case, such as ≥90% recall for drug discovery or tightened confidence for aggressive spam filtering
- Adaptive prediction system that returns single-item picks for power users and candidate carousels for cold-start users, both with confidence guarantees
- Five-step pipeline that transforms raw data into guaranteed predictions with explicit error-consciousness
- Policy-based confidence controls that allow different thresholds across mailboxes, user segments, or query populations
- Human-in-the-loop routing that automatically directs only ambiguous cases to human review while automating confident decisions