Keywords AI
The enterprise-grade software to build, monitor, and improve your AI application. Keywords AI is a full-stack LLM engineering platform for developers and PMs.
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What is Keywords AI?
Keywords AI is a full-stack platform designed to help developers and product managers build, test, and operate AI applications at scale. It brings together observability tools, evaluation frameworks, prompt optimisation, and a unified LLM gateway in one place. Rather than juggling multiple services, teams use Keywords AI to monitor how their AI applications perform in production, identify issues quickly, and iterate on prompts and models with confidence. The platform is aimed at organisations moving beyond prototypes to shipping reliable AI features.
Key Features
Observability and monitoring
Track how your LLM applications behave in production, including latency, costs, and error rates
Evaluation framework
Test and compare different prompts and models against your own benchmarks before deployment
Prompt optimisation
Iterate on prompts systematically with built-in tools to measure improvements
Unified LLM gateway
Route requests to different LLM providers through a single interface, making it easier to switch models or run A/B tests
Logs and analytics
Centrally store and analyse conversation logs to spot patterns and debug issues
Pros & Cons
Advantages
- Consolidates multiple tools into one platform, reducing tool sprawl for AI teams
- Freemium model lets you start without commitment and scale as your application grows
- Designed specifically for the workflows of LLM engineering rather than general monitoring
- Unified gateway simplifies multi-model testing and provider switching
Limitations
- As a specialised platform, it may have a learning curve for teams unfamiliar with LLM observability concepts
- Requires integration into your application, so adoption depends on your development setup and existing infrastructure
Use Cases
Monitoring production LLM applications to catch performance degradation or cost overruns early
Running prompt experiments and A/B tests to find the best version before rolling out to users
Evaluating whether switching to a cheaper or faster LLM provider will affect application quality
Debugging unexpected behaviour in AI-powered features by reviewing logs and conversation history
Building a feedback loop where production data informs prompt and model improvements