The Mentiora Blog

A higher evaluation score cannot show which customer experience improved. Mentiora keeps each result connected to the Scenario the agent faced, the business standard applied to its answer, and the conversation trace behind it, so a CX team can see what changed for the customer before deciding to release.

The agent alone is not a product anymore. It is the system behind your agents which learns your business context, your goals, and every conversation your customers have with you.

Mentiora Copilot is also the agent your customers talk to. It drafts beside your team in Zendesk, takes plain-language edits in the platform, and does the same work from Claude or ChatGPT.

Mentiora is SOC 2 Type II compliant, audited by Prescient Security. Report available via Trust Center.

Designing agents that design agents is less futuristic than we think.

At Mentiora, we use the same measurement-driven method whether we are tuning a small on-device model or building a large support system.

Playwright was built for humans. Loom is built for the agent driving the browser — open source, deterministic, replayable, MCP-native.

A look at the structured Claude Code workflow that built the deck describing it. Plus the generative-algorithm pipeline it sits inside at Mentiora.

How we use AI agents to turn customer interviews into structured product requirements — a repeating pipeline where competing agents audit each other and improve quality with every cycle.

We are pleased to announce our partnership with Unique AI, a global leader in enterprise agentic AI for the financial sector. We are very much looking forward to supporting Unique AI and their customers by enabling Unique AI engineering to use our AI performance technology, helping them further evolve their reliable, trustworthy AI solutions.

In this guest post, Flo's VP of Engineering Andrei Varanovich argues that the real challenge in AI agents isn't intelligence — it's engineering discipline. Drawing on Google's ML Test Score, he introduces an 'Agent Test Score' framework to help teams ship agents that don't just demo well, but hold up in production.

Prompt optimization plays a key role in improving the performance of AI assistants. This article reviews manual and automatic optimization methods, explains how to evaluate prompts reliably, and shows how practitioners can compare approaches to identify high-quality prompts.

Agent quality extends beyond accuracy. At scale, subtle behavioral issues can erode trust long before metrics signal a problem. Mentiora's Insights transform evaluation into a disciplined loop for understanding and improving agent behavior.

Moving from a demo to a reliable product is a bumpy road. Learn how Mentiora powers successful LLM workflows by anchoring development in clear specs, turning evaluation into improvement, and embedding governance directly into the shipping rhythm.

Prompting seems simple, but it steers how AI agents behave. In production, prompts aren't one-off crafts, but they're living system artifacts that need versioning and upkeep. Mentiora helps teams manage and optimize prompts, delivering measurable, repeatable performance gains at scale.

Your agent can look great on paper yet still miss the mark—failing to close tickets, earn trust, or drive outcomes. Learn how Mentiora closes the gap by measuring what stakeholders care about and optimizing agents against those signals.