Continuous AI experimentation · Private beta

Keep your AI at peak
ROI.

A/B test models and prompts on real production traffic — and measure what actually matters: conversion, revenue, retention. Not benchmarks. So every model decision comes with data.

Join the beta → See how it works
Model-neutral
LIVE · PRODUCT DESCRIPTION GENERATOR
A/B · Product description · fr-DE
12,400 sessions · 6 days running
Control
GPT 5.4
Conv.3.20%
Rev/session€1.84
Cost / usage$0.006
Winner ●
Claude Haiku 4.5
Conv.+12% · 3.58%
Rev/session+€0.21
Cost / usage−67%
Sig: 97.4% Auto-routing to winner →
Not a mockup. Real test. Real money.
Manifesto

The best model on MMLU is not the best model for your product. Your users don't care about benchmarks. Neither should you.

The silent problem

Your AI ROI is quietly degrading.

New models ship every week. Your team picks one, benchmarks look good, the ML team says "seems better" — you deploy. Six months later, nobody knows if the switch moved the business forward.

The tools that exist measure technical quality: scores, latency, hallucinations. None of them measure what you need to defend at board level: transactions. Retention. Revenue per user.

Your AI stack ROI over 12 months

AI spend ↑ 2.4x · Business outcomes ↔ flat · Decision confidence ↓
−34% ROI
AI spend Business outcomes ROI
Six months in → nobody knows.

Three steps.
Then continuous.

Plug Skord into the AI call you want to optimize. Tell it the business metric you care about. That's it — we take it from there, on live traffic, forever.

01 / CONNECT
Point Skord at your AI call.
YOUR APP AI call skord. SDK LLM PROVIDER GPT 5.4 / Claude metric: conversion · revenue · AOV

Lightweight SDK, a few lines of code. Recommendation engine, product description generator, chat assistant — any call. Define the business metric: conversion, AOV, retention.

≈ 1 afternoonNo migration
02 / SPLIT
Run A/B on real traffic.
TRAFFIC 100% CONTROL · 50% GPT 5.4 TREATMENT · 50% Claude Haiku 4.5 3.20% 3.58%

Skord splits production traffic between models and prompts. No synthetic datasets. No staging. Real users, real behavior, your KPIs. Guardrails run first; production tests run second.

Real trafficAuto guardrails
03 / DECIDE
Adopt the winner.
Or don't.
3.2% 3.5% 95% sig. WINNER Day 1 Day 7 Day 14 Statistical significance: 97.4%

See lift in business metrics — not eval scores. When significance hits, Skord auto-routes traffic to the winner. You can defend the decision in COMEX. The next model ships next week: loop repeats.

Data your CFO readsLoop continues
What you get

Built for product teams. Measured in outcomes.

● FLAGSHIP BENEFIT

The cheaper model doesn't have to cost you conversions.

When Skord proves a lighter model performs just as well on your KPIs — you switch. With data, not vibes. Some teams cut AI spend 30-40%.

−38%
● CONTINUOUS

New model? Test starts Monday.

As soon as a model ships, Skord queues it as a candidate. ROI never degrades silently.

● GUARDRAILS

Never ship a broken variant.

Pre-flight eval catches bad outputs before production traffic ever sees them.

● NEUTRAL

The only platform not owned by a model vendor.

Statsig is OpenAI. Humanloop is Anthropic. Skord is Skord — so the model we crown as winner is the one that's best for you.

Why Skord

Not benchmarks. Not eval scores.
Just business outcomes.

Braintrust
Eval scores
LLM-as-judge, latency, technical quality.
Measures output quality. Never connects it to revenue.
Statsig · Eppo
Generic A/B
Product engagement metrics. LLM as a side feature.
Owned by OpenAI · Datadog. Not neutral.
Leaderboards
Public benchmarks
MMLU, HumanEval, Chatbot Arena scores.
Generic. Not your users. Not your traffic.
skord.
Business outcomes
Conversion. Revenue. Retention. On your traffic.
→Decisions your board will accept. Every time.
Flore Morin — Founder, Skord
From the founder

"I spent 5 years watching teams pick models at feeling, defend AI spend with technical scores, and ship features nobody could prove were working. Skord is the tool I wished existed."

Flore Morin Founder · 5y AI Product · Paris
Model-neutral by design

Every model.
No tribes. No lock-in.

OpenAI Anthropic Mistral Meta · Llama Google · Gemini Cohere DeepSeek xAI Your OSS model
Things HoPs ask

Honest answers.

01
We already have an ML team running evals. Why Skord?
Evals measure output quality. They don't measure business impact. Your ML team can tell you a model is "better" — Skord tells you it converts 12% more. Different question, different tool.
02
How long does integration take?
An afternoon for the SDK. A day or two to wire up the business metric. You don't migrate models or rebuild anything — Skord routes the AI call you already have.
03
What if a test variant performs badly on real users?
Pre-flight guardrails run on sample data first. If a variant fails, it never reaches production. And traffic splits are small until confidence builds.
04
Is my prompt / traffic data used to train anything?
Never. Skord is model-neutral and data-neutral. Your data stays yours — no vendor sees what another vendor's variant is being tested against.
05
When does it cost something?
Usage-based pricing tied to traffic and experiments. Beta members lock in founding pricing for years — always cheaper than a wrong model choice.
Private beta

Join the beta.
Ship smarter AI.

Join the beta →
01
Founding member pricing — locked in for years.
02
Direct access to the founder.Slack channel, weekly review, shape the roadmap.
03
First number you'll prove.A lift in business metrics, in 30 days.