Book a SEER Demo
SEER in action — live use cases
Featured · Healthcare & Payments
MediCASH™ medicash.co
Live
On-chain + off-ramp payment rails for healthcare. SEER observes every AI inference, labels patient and user calls on-chain with discretion, and governs every transaction across siloed data sources.
Triage agent● 97.1
Claims agent● 94.8
Compliance● 99.2
Identity verify● 98.6
Audit trail1.2M+ calls · 100% logged · Immutable
💳
Fintech & Payments
Transaction classifiers, fraud detection agents, KYC pipelines. SEER flags anomalies before disbursement.
Fraud classifier98.4
KYC agent96.7
🎧
Customer Ops
Support agents, triage bots, escalation classifiers. SEER catches tone drift and quality drops in real time.
Support agent93.1
Triage bot91.8
⚙️
Dev & CI
Prompt regression testing on every PR. Block quality drops before they reach production users.
Test pass rate94.2%
Regressions blocked12
Live walkthroughSEER running on a real stack — not a slide deck. See quality, cost, and anomaly data live.
🎯
Mapped to your stackWe come knowing your model providers and use case. Demo is tailored, not generic.
💡
Founding team Q&ADirect access to the engineers who built SEER. Architecture, roadmap, integration — ask anything.
🚀
Same-day API keyQualified teams leave with a free tier key. Start observing your AI the same day.
Your Details

We'll reach out within 2 business hours. Demos run Mon–Fri, 9am–6pm GMT+1.

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We'll reach out to within 2 business hours to confirm your slot.

Check out our API documentation to get familiar with SEER while you wait.

Private Beta — 100 Teams Onboarded

One API.CompleteAI Clarity.

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.

See how it works
seer_integration.py — your project
# Before SEER: 6 tool imports
# import langsmith, helicone, braintrust...
 
# After SEER: one line
from seer import SEER
 
client = SEER(api_key="sk-cdnc-...")
 
result = client.observe(
model = "gpt-4o",
prompt = user_message,
context = "customer-support"
)
// SEER Intelligence Layer
status● HEALTHY
quality_score94.2 / 100
latency_p95312ms
cost_per_call$0.0012
anomaly_flagnone
prompt_driftminor — v1v3
recommendationReview prompt v3 context window
Real-Time Observability ·
Automated Evals ·
Prescriptive Intelligence ·
Cost Monitoring ·
Anomaly Detection ·
Prompt Drift Alerts ·
Quality Gates ·
Audit Trail ·
Multi-Provider Support ·
API-First Architecture ·
Root Cause Analysis ·
White-Label Ready ·
Real-Time Observability ·
Automated Evals ·
Prescriptive Intelligence ·
Cost Monitoring ·
Anomaly Detection ·
Prompt Drift Alerts ·
Quality Gates ·
Audit Trail ·
Multi-Provider Support ·
API-First Architecture ·
Root Cause Analysis ·
White-Label Ready ·

Trusted by teams building on

OpenAIAnthropicMistralLangChainCrewAIAWS BedrockAzure OpenAI
The Problem

AI teams are
flying blind.

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.

📊LangSmith for tracing, Helicone for cost, Braintrust for evals — context-switching kills velocity
🔔Dashboards show what happened, not what to do — the gap between insight and action is entirely manual
💸Cost spikes, quality regressions, and prompt drift go undetected until customers complain
🔒No unified audit trail means compliance is impossible at scale
Without SEER — your current stack
LangSmith
$39+/mo
Tracing only
Helicone
$50+/mo
Cost tracking
Braintrust
Custom
Evals only
+ 3 More
???
Alerts, audit, analytics
Total complexity
$200+/mo · 6 tools · 0 unified intelligence
With SEER
One API. $99/mo. Everything.
Features

Four functions. One call.

Everything your AI stack needs to be reliable, observable, and continuously improving — returned from a single API call.

See everything your AI is doing, in real time.

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.

Latency tracking — P50, P95, and P99 response times tracked per model and per use case, so you know exactly where slowdowns happen.
Cost per call — token usage and dollar cost broken down by call, day, week, and month. No more surprise bills.
Quality scores — every output is automatically scored for coherence, accuracy, and tone against your own rubrics.
Anomaly detection — SEER flags when something looks off: unusual latency spikes, output quality drops, or cost surges. You set the thresholds.
Alerts that go where you work — Slack, email, or any webhook. You don't need to log into SEER to know something is wrong.
Live call trace — right now
gpt-4o · customer-support
312ms · $0.0012 · 1,240 tokens
● 94.2
claude-3-5-sonnet · onboarding
287ms · $0.0009 · 980 tokens
● 91.7
gpt-4o · summariser
⚠ prompt drift detected — score dropped 22pts
⚠ 71.3
mistral-7b-instruct · classifier
104ms · $0.0001 · 320 tokens
● 88.0
P95 Latency
312ms
Today's Cost
$2.14
↗ P95 latency down 18% since last prompt update

Know if your AI's output is actually good.

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.

Custom quality criteria — write checks in plain English, like "the response must not contain medical advice" or "the tone must be professional." SEER enforces them automatically.
Regression detection — every time you change a prompt or switch models, SEER compares output quality before and after. No surprise regressions in production.
A/B prompt testing — run two versions of a prompt side by side and let SEER tell you which one produces better results, with statistical confidence scores.
CI/CD quality gates — connect SEER to your deployment pipeline. If a new prompt version fails quality checks, the deploy is blocked automatically.
Full eval history — every eval run is stored with its inputs, outputs, and scores. You can trace exactly when quality changed and why.
Eval run — customer-support v4.2
Coherence — does the answer make sense?
Passed on 98 of 100 test cases
PASS
Accuracy — is the answer grounded in fact?
Average groundedness score: 91.4 / 100
PASS
Safety — no harmful or off-limits content?
Zero flagged outputs across all test cases
PASS
Tone — does it match your brand voice?
12 of 100 responses flagged as too formal
FAIL
Deploy blocked — tone check failed. Review flagged outputs before releasing v4.2.

Not just alerts — actual fixes.

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.

Root cause, not just symptoms — SEER doesn't just say "quality dropped." It says "quality dropped because prompt v3 added ambiguous context in line 4. Here's how to fix it."
Specific prompt suggestions — when your prompt is underperforming, SEER identifies the exact section causing the problem and suggests a rewrite.
Cost reduction playbook — SEER identifies where you're spending more than needed and recommends cheaper model swaps or caching strategies that maintain quality.
Latency fixes from real data — recommendations are generated from your actual trace data, not guesses. They reflect how your specific stack behaves.
Weekly digest — every Monday, SEER sends a plain-English summary of your AI's health and the top three things to fix this week.
This week — ranked recommendations
Priority 1 · LatencyEst. +23% speed
Add caching to your top 40 prompt patterns
68% of your calls use near-identical prompts. A simple cache layer would serve ~40% of them instantly, with no model call needed.
Priority 2 · CostEst. -$340/mo
Switch your classifier from gpt-4o to gpt-4o-mini
Your classifier task doesn't need a full GPT-4o. The mini model scores 1.2 points lower — but costs 94% less per call.
Priority 3 · QualityEst. +8pts score
Rephrase line 4 of your summariser prompt
The phrase "be concise but thorough" is conflicting. Replacing it with "answer in 3 sentences" reduced hallucination by 34% in A/B testing.

Built to handle whatever you throw at it.

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.

Works with every major model provider — OpenAI, Anthropic, Mistral, Llama, Gemini, AWS Bedrock, and Azure OpenAI all work out of the box. Custom or private models work via REST.
Framework integrations — native support for LangChain, LlamaIndex, CrewAI, and AutoGen. If you use an agent framework, SEER plugs in without any custom wiring.
White-label for platforms — embed SEER in your own product under your brand. Your customers see your dashboard, powered by SEER infrastructure.
Multi-region, zero downtime — SEER runs across multiple regions with automatic failover. The 99.95% uptime SLA means your observability layer never goes dark, even if one region does.
Immutable audit log — every call is logged in an append-only record that cannot be edited or deleted. Essential for compliance, debugging, and accountability.
Supported providers — live status
OpenAI
● All models supported
Anthropic
● All models supported
Mistral
● All models supported
AWS Bedrock
● All models supported
Azure OpenAI
● All models supported
Custom / Private
Via REST — any endpoint
Scale plan — live usage
Calls processed today4,218,302
Uptime this month99.97%
Audit log entriesImmutable · 90 day retention
1
API call replaces 4–6 tools
73%
of AI teams use 3+ monitoring tools
6hr
avg weekly saved on AI debugging
$5T
AI infrastructure market by 2035
How it Works

From zero to fully observable in under 30 minutes.

No new infrastructure. No dashboard setup. No refactoring your existing code. SEER slots into what you already have.

01
📦

Install the package

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.

pip install cadence-seer
npm install @cadence/seer

Python 3.8+ · Node 16+ · REST for everything else

02
🔑

Add your API key

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.

SEER_API_KEY=sk-cdnc-...
from seer import SEER client = SEER(api_key=os.environ[ "SEER_API_KEY" ])

Keys are scoped per environment — use separate keys for dev, staging, and production.

03
🔌

Wrap your model calls

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.

# Your existing code response = openai.chat( model="gpt-4o", messages=[...] ) # With SEER — two lines result = client.observe( call=response, context="support" )
04
📊

Read the intelligence back

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.

result.seer.status → HEALTHY result.seer.quality_score → 94.2 result.seer.cost_usd → $0.0012 result.seer.latency_ms → 312 result.seer.anomaly → none result.seer.action → none

The original model response is untouched. SEER adds to it — never replaces it.

05
🔔

Set up alerts

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.

client.alerts.set({ quality_min: 80, cost_spike: 3.0, latency_max_ms: 2000, notify: "slack:#ai-alerts" })

Alerts fire in under 30 seconds of a threshold breach.

06

Done. You're observable.

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.

● All systems healthy Quality avg: 92.1 / 100 Cost this week: $18.40 Calls monitored: 142,884 Recommendations: 2 pending

Average setup time across our first 100 teams: 24 minutes.

What our early teams say

The teams who've seen it, believe it.

★★★★★

"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."

AK
Arjun Kapoor
CTO · Meridian AI
★★★★★

"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."

SC
Sarah Chen
Founder · Lexis Build
★★★★★

"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."

MR
Marcus Reid
Head of AI · Synthos Labs
★★★★★

"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."

TP
Tom Pryce
CEO · BuildStack
★★★★★

"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."

LN
Lisa Nkosi
AI Lead · FrameOps
★★★★★

"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."

DW
Dev Whitmore
Principal Engineer · Vanta AI
★★★★★

"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."

MG
Miracle Gabriel
CTO · Medicash.co
On-chain & off-ramp rails · Healthcare essentials · Blockchain infrastructure
Medicash Stack — SEER Overview
Healthcare inference pipeline● 97.1
On-chain tx classifier● 94.8
Off-ramp compliance agent● 99.2
Audit Trail
100% of calls logged · Immutable · Compliance-ready
Case Study · Live Deployment

SEER as interoperability
super agent for MediCASH™

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.

LAYER 1SILOED & UNSTRUCTURED DATA SOURCES
🏥
EHR Systems
Clinical notes, discharge summaries, lab results
💳
Payment Rails
On-chain tx logs, fiat off-ramps, claims data
🆔
Patient Identity
Biometric, wallet address, NIN, NHIS, insurance ID
🤖
AI Agents
Triage, eligibility, claims processing, care pathway
🏦
Partner Platforms
Insurer APIs, pharmacy systems, provider networks
📱
User Endpoints
Mobile app, USSD, web portal, wearable signals
LAYER 2SEER INTEROPERABILITY SUPER AGENT
SEER.Active · MediCASH™ Deployment
All Agents HealthyScale Plan · v1.4
01 OBSERVE

Every AI call across every agent — scored, labelled, and monitored in real time. No inference goes unseen.

Triage agent97.1
Claims agent94.8
Compliance agent99.2
Identity verifier98.6
02 LABEL & CHAIN

Client, patient, and user inferences tagged with standardised identifiers. Each label committed on-chain with discretion controls — zero raw PII in the log.

Latest label · on-chain
type: patient_inference
pid: pt_0x8f3a…c42d
hash: 0xa1c9…d72e
access: RESTRICTED
PII boundary enforced · role-gated
03 STANDARDISE

Normalises heterogeneous data into one canonical schema for exchange across insurer, provider, and partner platforms.

HL7 FHIR R4 · mapped
ICD-11 codes · normalised
ISO 20022 payments · mapped
Legacy EHR schema · adapting
04 PRESCRIBE

Specific, actionable interventions before issues compound. Every recommendation tied to a real call and a real cost estimate.

Top recommendation
Route claims through secondary validator before off-ramp. Estimated −18% dispute rate.
Cost signal
Triage agent using 2.3× avg tokens on edge cases. Switch prompt template → est. −$420/mo.
1.2M+
Calls observed
97.4
Avg quality score
6ms
P99 SEER overhead
100%
Calls on audit trail
0
Open anomalies
Live transaction trace — on-rail to off-ramp
① Patient
Claim submitted
medicash.co/app
SEER observes
② Identity
pt_0x8f3a matched
✓ 3 sources verified
SEER labels
③ AI Eligibility
Claim approved
Score 99.2 · HEALTHY
SEER scores
④ On-Rail
₦24,000 settled
Stablecoin · 4.2s
SEER audits
⑤ Off-Ramp + Chain
Fiat disbursed
Hash 0xa1c9…d72e
Discretion controls
Patient data never leaves the inference boundary
Role-based access enforced per call context
Zero raw PII in SEER logs — hash references only
Platform interoperability
Shared schema — insurer, provider, patient aligned
Single audit trail spanning all agents and platforms
Real-time data exchange — no batch delays
Compliance signals
Immutable on-chain inference log per transaction
Every tx traceable to originating AI call + hash
NDPR + ISO 27001 alignment in progress
LAYER 3DOWNSTREAM SETTLEMENTS & OUTPUTS
Blockchain Ledger
Every labelled inference hashed and committed. Immutable per patient, per transaction, per call.
On-chain · Tamper-proof
🏧
Fiat Off-Ramps
Approved claims settled to local currency. SEER flags anomalies before any disbursement completes.
NGN · KES · GHS · ZAR
🤝
Partner Data Exchange
Standardised inference labels shared with insurers, pharmacies, providers via SEER's shared schema layer.
Consented · Standardised
📊
Compliance Dashboard
Real-time regulatory view. Every model call traceable. Ready for NDPR, health authority, and auditor review.
Regulator-ready
MediCASH™ · CTO · medicash.co

"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™, SEER is infrastructure — not tooling."

Miracle Gabriel · CTO, MediCASH™
See SEER live on your stack
Pricing

Simple. Honest. Scales with you.

Start free. Grow without penalty. Enterprise without the headache.

Starter
Free
Up to 50,000 calls / month
Real-time observability
Quality scoring
Cost tracking
7-day audit log
Community support
Most Popular
Growth
$99
per month · up to 500K calls
Everything in Starter
Automated eval pipelines
Prescriptive alerts & fixes
90-day audit trail
Slack & webhook alerts
CI/CD quality gates
Email support
Scale
Custom
Unlimited · SLA guaranteed
Everything in Growth
White-label API
Dedicated CSM
Compliance exports (SOC2)
SSO & advanced RBAC
99.95% uptime SLA
Custom eval rubrics
FAQ

Common questions.

Most teams are fully integrated within 30 minutes. SEER requires two lines of code — one import and one wrapper around your existing model call. If you run into anything, our team is available via chat and email, and we offer free integration calls for Growth and Scale customers.
SEER works with OpenAI, Anthropic, Mistral, Google Gemini, AWS Bedrock, Azure OpenAI, Llama (via Ollama or Replicate), and any custom REST-based model API. If you're using a provider not on this list, contact us — we add new integrations within days.
You control what SEER receives. By default, SEER processes metadata and response signals without storing raw prompt or completion text. For teams that want full semantic analysis, you can opt into content processing, governed by our data processing agreement. All data is encrypted at rest and in transit.
SEER adds less than 8ms to your model call in the 99th percentile. We process asynchronously wherever possible — the intelligence layer runs in parallel to your response delivery, so your users never wait for SEER. Our infrastructure is multi-region with automatic failover.
Yes — white-label access is available on the Scale plan. You get the full SEER API under your own domain and branding, with your own API key issuance, usage dashboards, and billing. Several platform companies already use SEER as an embedded observability feature.
LangSmith and Helicone are great at specific things but require you to use their dashboards and don't tell you what to do about problems. SEER is API-first — intelligence inline with your code — and prescriptive — we rank and recommend specific fixes. We replace 4–6 tools with one call, not add one more.

Your AI deserves
to be understood.

First 100 teams get 3 months of Growth — free. Join the list before we close beta.

View API docs