Skeptical
OrbDB provides error-conscious AI infrastructure that mathematically bounds how often model predictions can be wrong, replacing fabricated confidence scores with statistically guaranteed error rates. The platform lets enterprises set configurable error budgets—such as 95% confidence that cleared decisions stay at or below the target error rate—and adapts outputs from single picks to candidate shortlists based on certainty. It integrates into existing stacks without retraining, with methodology peer-reviewed at VLDB 2025 and VLDB 2026.
- Artificial Intelligence
- Software Only
- AI Agents
- Enterprise Software
Funding
Raised to date
SEK 15.8MRaised 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.
Est. $1.5Macross 1 round
KVTISEK 15.8M · Pre-Seed
Announced Aug 2026
Disclosed cumulative: $1.5M
Founders
Founder details are not available yet.
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. At a specified error budget, the error rate among cleared decisions stays at or below that level with 95% confidence on data like the customer's own, a number that can be presented to an auditor.
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 at a user-set error rate with 95% statistical confidence
- 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
- Integrates into existing stacks without requiring model retraining, maintaining guarantees over time
- Peer-reviewed methodology published at VLDB 2025 and VLDB 2026