Hi HN! We're Kaushik and Ishir. We’re building SiftDev (https://app.trysift.dev/docs), an intelligent logging tool that understands your observability data in real time, automatically identifies anomalies, and lets you interact with your logs through natural language queries. Here’s a demo video: https://www.youtube.com/watch?v=uQ-TTdiu3fc&t=20s, and there's a demo playground you can try out here: https://app.trysift.dev/.

We used to work on product and engineering at Datadog and Splunk. We saw how even teams using these industry-leading tools were struggling to effectively interpret and use their logging data. The sheer volume of logs overwhelmed experts and newcomers alike, making it difficult to quickly identify meaningful issues or patterns. Despite powerful indexing and search capabilities, developers still had to manually piece together context from different logs, dashboards, and sources—a tedious and error-prone process.

The “noisy logging” problem—that is, the gap between overwhelming amounts of raw log data and insights people can act on—ultimately is a gap between machines (which generate all this data) and humans (who want and need the insights). SiftDev is built to bridge that gap and to automate the tedious, manual aspects of debugging and observability. In marketing-speak: “humans should never have to look at a log again!” We think people should interact with their data in terms that make sense on a human level.

What makes SiftDev different is its understanding of application context over time. While traditional tooling typically lets developers analyze logs in isolation, or with minimal surrounding context, SiftDev builds comprehensive profiles of your application's normal behavior patterns. This awareness allows us to understand what's truly abnormal versus what might appear unusual in a single snapshot but is actually expected behavior for your specific application. SiftDev applies semantic analysis and profiling to understand your application's logging behavior holistically. Instead of relying solely on manual search, Sift identifies core application processes, automatically detects patterns, and surfaces anomalies, including clear explanations and context.

Here are some examples of what this can look like in practice: Identify core processes: SiftDev instantly recognizes your payment workflows—like authorization, capture, and refunds—without manual tagging. Detect performance patterns: SiftDev learns your nightly batch job typically handles 10,000 records in 45 minutes, establishing a clear baseline. Surface hidden anomalies: SiftDev flags silent failures, such as two microservices updating the same record within 50ms—issues normally hidden by routine logs.

You can then directly ask your logs questions like, “What's causing errors in our checkout service?” or “Why did latency spike at 2 AM?” and immediately receive insightful, actionable answers that you’d otherwise manually be searching for.

We’d love for you to test out our product via our demo playground at https://app.trysift.dev/! It’s a slightly less functional version of our platform but shares a lot of the core features. Note: we do need users to sign up to do this but waitlist is optional (of course).

We'd love your feedback, thoughts, and experiences dealing with logging and observability challenges!