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Stop stitching together 4–6 monitoring tools. SEER gives your AI stack real-time observability, automated quality gates, and actionable intelligence — in a single API call.
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The moment you put an AI in production, you need to know: Is it working? Is it degrading? Is it costing too much? Answering those questions today requires 4–6 separate tools — and still leaves gaps.
Everything your AI stack needs to be reliable, observable, and continuously improving — returned from a single API call.
Every time your AI calls a model — whether that's GPT-4o, Claude, Mistral, or anything else — SEER records exactly what happened: how fast it was, how much it cost, how good the output was, and whether anything looks wrong. You get this back inline, in the same response object, with no extra dashboards to open.
Think of it as adding a flight recorder to your AI. You always know the state of every call, across every model, across every context — all in one place.
Sending a prompt to a model and getting text back is the easy part. Knowing whether that text is actually correct, safe, on-brand, and useful — that's the hard part. SEER evaluates every output against quality criteria you define, so problems get caught before your users ever see them.
You can write eval criteria in plain English — no ML experience required. SEER handles the scoring. If something fails, SEER can automatically block a deployment or send an alert, depending on how you configure it.
Most monitoring tools tell you something is wrong and leave it there. SEER goes further. When it detects a problem — a quality drop, a latency spike, a cost surge — it tells you exactly what to do about it, ranked by impact.
The recommendations come from analysing your actual call data — not generic advice. If your summariser is costing 3x more than it needs to, SEER will tell you which model swap would fix it and what the quality trade-off is.
SEER works on day one with your current stack and keeps working as you grow. Add new models, new agents, or new use cases without changing your integration. Whether you're handling 1,000 calls a day or 100 million, the API behaves exactly the same.
For platforms that want to offer AI observability to their own customers, SEER is available as a white-label API — your branding, your domain, your pricing, our infrastructure.
No new infrastructure. No dashboard setup. No refactoring your existing code. SEER slots into what you already have.
One line. SEER is available as a Python package and a Node.js module. If you use another language, the REST API works with anything that can make an HTTP request.
Python 3.8+ · Node 16+ · REST for everything else
Sign up, grab your key from the dashboard, and set it as an environment variable. SEER uses it to associate your calls with your account. That's all the configuration there is.
Keys are scoped per environment — use separate keys for dev, staging, and production.
Find where you call your AI model. Wrap it with seer.observe(). Your model still runs exactly the same way — SEER just watches what happens and attaches intelligence to the result.
seer.observe() returns everything your model returned, plus a seer object containing quality score, cost, latency, anomaly flags, and recommendations. Use what you need, ignore the rest.
The original model response is untouched. SEER adds to it — never replaces it.
Tell SEER what "bad" looks like — a quality score below 80, a cost spike over 3x average, a latency above 2 seconds. When those thresholds are crossed, SEER fires an alert to Slack, your email, or any webhook.
Alerts fire in under 30 seconds of a threshold breach.
From here, SEER runs in the background. Check your dashboard any time, or wait for SEER to come to you with a weekly digest. Most teams stop thinking about AI reliability problems within the first week.
Average setup time across our first 100 teams: 24 minutes.
"We replaced LangSmith, Helicone, and two internal monitoring scripts with SEER in an afternoon. The prescriptive recommendations alone saved us 6 hours in the first week. Best developer tool I've integrated this year."
"As a solo founder I couldn't justify the complexity or cost of enterprise monitoring. SEER's free tier gave me production-grade observability from day one. It's the equaliser I needed."
"SEER caught a prompt regression our entire team missed and suggested the exact change that fixed it. I've used plenty of monitoring tools. None of them told me what to actually fix."
"We embed SEER into our platform via the white-label API. Our customers get AI observability as a first-class feature without us building a single line of monitoring infrastructure."
"SEER flagged within 48 hours that one prompt was triggering 3x more tokens on edge cases. Fixed it in an hour, saved $800 a month. The ROI was immediate."
"I wanted intelligence that travels with my AI pipeline, not another tab to open. SEER's response object slots into my existing logging with zero friction. Should have existed years ago."
"We're building on-chain and off-ramp payment rails for everyday essentials and healthcare — environments where AI reliability isn't a nice-to-have, it's a compliance requirement. SEER is the only tool that gave us a unified audit trail across every model call, real-time anomaly detection on our inference pipeline, and actionable fixes we could act on immediately. For a team moving as fast as Medicash, having one API that replaces six tools and tells us exactly what to fix is not just convenient — it's foundational to how we ship. SEER is infrastructure, not tooling."
How SEER bridges siloed and unstructured data across the full MediCASH™ stack — labelling client, patient, and user inferences on-chain with discretion and standardisation, governing every on-rail and off-ramp transaction at scale.
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