SEER is a single API that gives every AI-powered product complete observability, automated quality evaluation, and prescriptive intelligence. Born from our AI Systems & Observability research programme at Cadence Labs.
In 2023, the Cadence Labs AI Systems & Observability team was running evaluations on a multi-model pipeline for a robotics application. They were using five separate tools to monitor latency, cost, output quality, and anomaly signals — none of which talked to each other.
Every time something went wrong, the diagnosis took hours. The data existed, but it was fragmented across dashboards. The team spent more time debugging their monitoring setup than improving their AI.
SEER was built to solve that specific frustration. One API call. Everything returned inline. No dashboards to context-switch into, no alerting pipelines to maintain, no fragmented data to reconcile.
It shipped as an internal tool in January 2024. By March, teams outside Cadence Labs were asking to use it. By June, it was a product.
73% of AI engineering teams use three or more separate monitoring tools. None of them were designed to work together. The result is fragmented visibility, slow incident response, and no clear path to improvement.
Model quality drifts silently. A prompt change, a model update, or a shift in input distribution can degrade output quality for hours or days before anyone notices. By then, the damage is done.
Token costs accumulate invisibly. Teams routinely discover at month-end that one AI feature is consuming 60% of the infrastructure budget. SEER tracks cost per call, in real time, attached to every response.
Latency spike? Quality drop? Cost surge? Without a single source of truth, engineers cross-reference three dashboards, check deployment logs, and compare model versions manually. This is not a good use of anyone's time.
Wrap your existing model call with seer.observe(). Every other capability is automatic, returned inline in the same response, with no infrastructure to maintain.
Every call traced: latency at P50, P95, and P99 — token cost to the cent — quality score against your rubric — anomaly flags the moment something looks wrong.
Continuous evaluation against your quality criteria. Write checks in plain English. SEER enforces them on every call. CI/CD integration blocks deploys that would degrade quality.
When something's wrong, SEER tells you exactly what to do about it. Root cause, not just symptoms. Specific prompt fixes, model swap recommendations, caching strategies — ranked by impact.
Works on day one with your current stack. Handles billions of calls without configuration changes. Every model provider, every agent framework, white-label ready.
No new infrastructure. No dashboards to set up. No refactoring. SEER slots into what you already have.
One command. Works with Python 3.8+, Node 16+, and any language via REST. Nothing else to configure beyond your API key.
Wrap your existing model call with seer.observe(). Two lines of code. Your model still runs exactly the same — SEER just watches what happens and attaches intelligence to the result.
SEER returns the model's response plus a seer object containing everything you need to know. Use it inline, push it to Slack, or let SEER alert you automatically.
SEER is the commercial expression of our AI Systems & Observability research domain. The underlying methods — quality scoring, anomaly detection, prescriptive analysis — come from active research programmes inside Cadence Labs.
As the research advances, SEER gets better. Automatically, for every customer. When we publish a paper on a new evaluation method, it ships to production within weeks.
Our foundational research domain. We study how AI systems behave at runtime — not what they produce, but how reliably and consistently they produce it. This includes latency distributions, token economics, output stability across repeated calls, and the cascading effects of model changes in production pipelines.
SEER is the productised outcome: a lightweight SDK that instruments your existing model calls and returns a structured intelligence object alongside every response — no infrastructure changes, no data leaving your environment.