Shellexa embeds with product teams to build, test, and validate high-stakes AI systems and regulated workflows, delivering QA as a service from development through production. The company combines AI-led development with production reliability, ensuring systems are auditable, compliant, and free of critical issues. For example, one legal client achieved a 64% reduction in contract review time with zero hallucinated clauses over six months of production use.
Funding
Funding not disclosed
Founders
Product
Problem
High-stakes AI systems and regulated workflows often face a trade-off between speed and reliability, where fast delivery can compromise auditability, compliance, and control. Teams struggle to scale manual processes like contract review or claims verification without introducing errors, hallucinations, or compliance violations, especially when handling sensitive data or complex decision paths.
Solution
Shellexa embeds with product teams to own architecture, build, UI, and validation in a single delivery loop, ensuring quality is built in from day one. The company provides QA as a service, AI-led development, and production reliability, with a focus on deterministic outputs, human escalation for low-confidence cases, and full audit trails. By mapping user paths and integrating QA into every sprint, Shellexa ships purpose-built systems that hold up under real production volume, as demonstrated by a workflow platform delivered in three weeks with zero critical issues and no rework in the first 60 days.
Target Audience
Primary customers are product teams building high-stakes AI systems, regulated workflows, or custom internal tools in sectors like legal technology, digital health, cybersecurity, and SaaS, who need to move faster without losing reliability, auditability, or control.
Features
- Deterministic AI pipelines with citation verification and confidence scoring, ensuring every output is source-grounded and low-confidence cases are escalated to human reviewers
- PII/PHI de-identification and zero-retention processing with full audit logs, enabling HIPAA-compliant automation for health data workflows
- Schema-enforced output validation and structured data normalization across payer formats, reducing rework and manual review in claims processing
- Embedded QA from day one, including regression coverage, edge case documentation, and operational handoff context, ensuring systems are production-ready at launch
- Evaluation harnesses that track recall, drift, and exceptions in production, maintaining reliability as new templates or edge cases emerge