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LLM observability · Quality assurance · Evaluation

We make sure your AI agents work in production, and keep working.

What was your agent asked, what did it answer, and what happened in between? We make its behaviour visible and measurable, so you can improve it with data instead of guesswork.

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We give your AI agent automated quality control and a dashboard.

From pilot to productionA partner that takes your AI pilot reliably into production, with quality, security and monitoring in place.Read moreEvals and guardrailsAutomated quality scoring and systematic testing against misuse.Read moreMCP servers and MCP AppsYour product's data and actions inside AI assistants, with interactive views.Read moreAgent observabilityTracing, analytics and dashboards: see what your agent does and what it costs.Read more
Why Semantly?

Quality assurance is our core expertise, not an afterthought.

We have decades of experience with AI, from classic machine-learning systems to today's large language models. In every one of them, what mattered most was continuous improvement based on how the system behaved in production.

Building an AI agent is easy today. The hard part is knowing whether it really works, and keeping it working as users, data and models change. That is what we specialise in.

We make your agent's behaviour visible and measurable, so every improvement is based on data instead of guesswork.

Two specialists reviewing a dashboard on a laptop in a meeting room

Hands-on builders

We have built machine-learning algorithms, AI agents, MCP servers and AI features for production ourselves, so we know the problems first-hand.

Measurement before opinions

Every recommendation is based on your agent's own data rather than generic best practices or gut feeling.

Open standards

We build on open standards such as OpenTelemetry, so the solution doesn't lock you into a single vendor.

Customer insight

See what your customers ask, and whether they got help.

In this example a customer changes the delivery address of their order in the chat. We show you what happens in conversations like this: what customers ask about, whether the agent succeeded and where conversations break down.

  1. 01

    What customers ask

    Conversations are grouped by topic, so you see which questions are growing and what the agent is really used for.

  2. 02

    Whether the customer got help

    Every conversation is evaluated: was the issue resolved, was the answer correct and did the customer end up asking for a human.

  3. 03

    Where conversations break down

    We find the points where customers get frustrated or give up, and propose fixes.

References

Two decades of AI in production.

We have built AI solutions for real users, from semantic text analysis to modern LLM assistants. Here is the latest.

MCP server and interactive charts for Claude

We brought an analytics platform's features to AI assistants such as Claude and ChatGPT through an MCP server, including analytics capabilities that weren't previously available in the product's own UI. An MCP App extension renders interactive charts right in the conversation, so users can filter and drill into the data without leaving the chat.

MCP serverMCP AppsInteractive visualisation
More references