seersearch.com

What It Is

Seer is a production-grade context quality observability platform for RAG, search systems, and AI Agents. Rather than focusing on general application logs, it asks a more specific question: “Is the retrieved context sufficient to answer the user’s question?” The website notes that when many Agents suffer from hallucinations, context drift, or declining recall, teams often only discover the issue after users complain. Seer’s positioning is to continuously evaluate production traffic and trigger early alerts.

Core Capabilities

Seer uses fine-tuned evaluation models to automatically score each query, with metrics including groundedness, recall, precision, citation coverage, and latency. According to the website, its Qwen3-4B version achieves a Micro F1 of 0.87 on benchmarks, close to the listed GPT-5 comparison result, while offering lower inference costs. It also supports change testing: different embeddings, rerankers, prompts, or toolchain variants can be assigned feature flags, compared side by side on real traffic, and analyzed with statistical significance and query-level breakdowns.

Integration, Alerts, and Pricing

The integration approach is engineering-oriented, with Python and TypeScript SDKs available. The example only requires calling client.log in the retrieval service or Agent orchestration layer to upload the task, context, and metadata. Alerts can be connected to Slack, PagerDuty, and webhooks, and Seer also supports separating production, staging, and development environments. Pricing is based on the number of evaluations: $0.00016 per evaluation for the 4B model and $0.00002 per evaluation for the 1.7B model; 1M evaluations per month cost about $160 and $20 respectively. Enterprises can choose self-hosting, but the public page does not list plans, free quotas, or payment methods.

Pros, Cons, and Who It’s For

Its main strength is its highly focused positioning. It is well suited to engineering teams that already run RAG/Agent systems and need to monitor retrieval quality and the impact of changes. Compared with manual labeling or evaluating each item with a large model, its cost structure is clearer. The limitations are also obvious: the website does not specify performance in Chinese-language scenarios, privacy and compliance details, or data retention policies. Its core accuracy claims are also mainly based on its own benchmarks, so they still need to be validated on each company’s own data.

Access from China and Alternatives

The source material does not provide information on access from mainland China, so network connectivity and payment options are both unknown. Teams deploying in China or with data compliance requirements should first ask about self-hosting, whether data leaves the country, log retention periods, and related issues. Comparable tools include LangSmith, Arize Phoenix, Langfuse, Helicone, Ragas, and TruLens.