Tangentic provides AI trust and robustness tools that embed interpretability, monitoring, and prompt‑engineering into the model lifecycle. Its Mesh workspace optimizes prompts, Navigator delivers diagnostics with sparse autoencoders and data‑poisoning assessments, and Manager offers real‑time drift detection and policy‑compliance alerts via REST/gRPC APIs for seamless MLOps integration.
Funding
Funding not disclosed
Founders
Product
Problem
Enterprises and developers increasingly rely on large language models (LLMs) and multimodal AI systems, yet they lack reliable mechanisms to verify model behavior, detect hidden biases, and respond to adversarial inputs. This opacity hampers regulatory compliance, erodes user confidence, and makes it difficult to debug or steer deployed AI applications.
Solution
Tangentic delivers a suite of AI trust and robustness tools that embed interpretability, monitoring, and advanced prompting directly into the model lifecycle. The Mesh component provides a prompt‑engineering workspace that optimizes instruction design through iterative feedback loops. Navigator offers model‑level diagnostics, including feature‑aligned sparse autoencoders and data‑poisoning assessments, to surface latent representations and vulnerability hotspots. Manager continuously tracks AI “employees” in production, surfacing drift, performance regressions, and policy violations via real‑time dashboards and alerting APIs. All modules expose RESTful and gRPC endpoints for seamless integration with existing MLOps pipelines, enabling organizations to audit, debug, and steer AI with measurable confidence.
Target Audience
Primary customers are AI‑driven startups, government agencies overseeing critical decision‑making systems, and large enterprises deploying AI assistants or automated workflows that require continuous oversight and interpretability.
Features
- Prompt‑crafting engine (Mesh) with automated suggestion generation, version control, and A/B testing for LLM instructions
- Interpretability layer (Navigator) leveraging sparse autoencoders to map high‑dimensional activations to human‑readable feature spaces
- Data‑poisoning detection suite (PoisonBench) that quantifies susceptibility of fine‑tuned models to malicious data injection
- Real‑time monitoring console (Manager) for drift detection, usage analytics, and policy compliance alerts across distributed AI agents
- Open APIs (REST/gRPC) and SDKs for Python and JavaScript to embed trust metrics into CI/CD and MLOps workflows
- Role‑based access control and audit logging to satisfy enterprise governance and regulatory requirements
- Scalable cloud‑native deployment with containerized microservices supporting on‑premise or hybrid environments