Hyperparallel LLM experimentation engine for RAG tuning, fine-tuning, and agentic workflows.
Best for: ML engineers tuning RAG pipelines with multiple retrievers and rerankers, AI researchers comparing fine-tuning methods (SFT, DPO, GRPO) at scale
Real-time hallucination detection and auto-correction for production LLMs.
Best for: Developers building production LLM applications needing robust hallucination defense, Customer support teams using AI chatbots where accuracy is critical
AI spend intelligence: cost per customer, feature, and pull request
Best for: Engineering leaders tracking AI spend across teams, projects, and pull requests, Finance teams reconciling multi-provider AI invoices and catching billing errors
Enterprise AI agent analytics for adoption, fluency, and ROI tracking
Best for: Enterprise AI program managers tracking adoption and ROI across departments, Customer experience leaders monitoring churn signals and satisfaction from AI chat
DataGrout is an infrastructure layer giving production AI agents persistent memory, cost governance, and cryptographic audit trails across any LLM provider.
Best for: CTOs and engineering managers deploying multi-agent systems in production, DevOps teams needing cost-aware AI infrastructure
Version, test, and compare prompts side by side — with rollback.
Best for: Teams running production prompts that need version control and rollback, Agencies managing client LLM applications with multiple stakeholders
AI guardrails, evaluations, and rogue agent red teaming platform
Best for: Enterprises deploying LLM agents in production with strict reliability requirements, Developer teams building agentic AI systems that need to harden against security flaws
A local-first framework for building durable, replayable, AI-orchestrated agent systems.
Best for: Developers building production agent systems that need deep observability, Teams requiring durable, replayable execution for auditing and compliance
Test, monitor, and improve voice & chat AI agents with realistic simulations and deep observability.
Best for: AI voice agent teams at customer service companies shipping to production daily, Engineering teams deploying conversational AI in healthcare, finance, or logistics with compliance needs
Build, evaluate, and version LLM deployments in one platform.
Best for: Teams building LLM-powered products needing systematic testing and versioning, Product managers tracking LLM performance with non-technical stakeholders
MCP server observability: traces, security scans, and auto-fix patches
Best for: Engineering teams running MCP servers in production who need protocol-level visibility, B2B SaaS companies embedding MCP tool calling into their products
MAEUM ships custom AI apps fast — describe your need, get a working demo in ~10 minutes.
Best for: Non-technical founders who need a working AI or web app prototype fast, without hiring a dev team, SMEs wanting custom AI integration or workflow automation handed off entirely to an external builder
Inherently interpretable AI models for auditing and steering outputs
Best for: AI safety researchers needing model transparency for alignment studies, Regulatory compliance officers auditing AI decisions in high-stakes domains
