Shcherbak AI
Shcherbak AI builds custom, enterprise-specific AI agents that are verified, constrained, and governed throughout their entire operational pipeline. The company pairs engineering depth with technology law expertise, ensuring each agent operates within defined boundaries and provides auditable outputs. Its open-source ContextGem framework reads every paragraph of a document, extracts typed values, and cites each fact to its source sentence.
- Artificial Intelligence
- AI Agents
- Enterprise Software
- Software Only
- Regulatory & Compliance Technology
Funding
Founders
Founder details are not available yet.
Product
Problem
Off-the-shelf AI agents often fail in enterprise settings because they are not aligned with an organization's unique workflows, systems, data, or culture. Deploying generic agents without rigorous safeguards creates risks around compliance, accuracy, and governance, while a lack of verifiability undermines trust and hampers adoption of automation in high-stakes business processes.
Solution
Shcherbak AI builds and deploys custom AI agents tailored to each enterprise's specific needs, workflows, systems, data, and culture. The company pairs engineering depth with technology law expertise so that every agent is verified, constrained, and governed throughout its observe-ingest-plan-reason-execute-verify pipeline. Each agent's runtime performance is continuously monitored through checkpoints, logs, traces, and metrics, feeding into a feedback loop. Improvements are only released via an evals release gate, ensuring that any change meets predefined behavioral thresholds before being promoted. This structured approach ensures agents operate within defined boundaries, provide auditable outputs, and align with enterprise governance requirements.
Target Audience
Enterprises that need dependable, compliant AI automation for complex, high-volume business processes such as contract review, document analysis, or customer operations. This includes legal teams, operations leaders, and engineering organizations seeking governed AI deployment with verifiable output.
Features
- Custom agent design that maps directly to client workflows, data sources, and enterprise policies, rather than applying a generic template
- Full-observability runtime including logs, traces, metrics, tokens, errors, and cost tracking at each pipeline stage from ingest through execution
- Series of guardrails and validation checkpoints at the verify stage, enforcing compliance rules and preventing out-of-policy actions
- Continuous improvement loop where production outputs are evaluated against a release-gate evals system, and only improvements passing rigorous tests are redeployed
- ContextGem, an open-source extraction framework that reads every paragraph of a document, extracts typed values, and cites each fact to its source sentence
- Use of new decision models that return probability distributions for closed questions, improving calibration over LLM confidence scores and avoiding prompt-based extraction only