Wrong decisions create real costs.
A misrouted support request, an incorrectly flagged transaction, or a missed security escalation creates downstream work and frustrated customers.
AI THAT WORKS THE WAY YOUR BUSINESS WORKS
Custom AI Models for critical business decisions
Generic AI doesn’t know your business rules, exceptions, or what a good decision looks like. Covy helps you get more decisions right, reduce costly mistakes, and improve performance against your own standards.
Built around your business rules. Measured against your criteria. Your data stays yours.
Incoming support request
“Our entire team is locked out after enabling SSO.”
Generic answerSent to general support
Treated as a routine question. Your escalation policy and account access rules are missed, so the team waits.
Decision with your rulesIdentity support, escalated
Recognized as a team-wide access incident. Escalated and routed to a person for review, as your policy requires.
Why generic AI isn’t enough
Generic AI is smart.
But it doesn’t know your business.
Your business has its own rules, exceptions, and standards. When AI doesn’t understand them, small mistakes become costly operational problems.
Your team becomes the safety net.
When AI can’t handle business-specific decisions consistently, people spend their time reviewing outputs, fixing mistakes, and managing exceptions.
Improvement is harder than it should be.
As your business changes, keeping decisions accurate can mean endless prompt edits, manual evaluation, and engineering effort.
What you gain
AI that gets more of the
important decisions right.
Turn the decisions your business makes every day into a competitive advantage.
01
Better decisions, fewer costly mistakes
Improve the accuracy and consistency of decisions that depend on your own rules, categories, and edge cases.
02
Less human intervention
Reduce avoidable corrections, reviews, and escalations as decision quality improves, so your people can focus on higher-value work.
03
AI that keeps getting better
Build toward improving decisions with validated examples and real-world feedback, with progress measured before new versions go live.
On our roadmap04
Your intelligence. Your control.
Shape AI around your business standards and keep control of your data, evaluation criteria, and deployment decisions.
Use cases
Start with one decision
that matters.
You don’t need to reinvent your entire AI stack. Start with a recurring decision where better accuracy could make a meaningful difference.
Get customers to the right resolution faster.
Improve ticket routing, recognize high-priority issues, and reduce unnecessary handoffs.
Improve a decision with CovyIllustrative example
The situation
“Our entire team is locked out after we enabled single sign-on.”
Why generic AI struggles
A generic model sees a login question and sends it to general support. It doesn’t know team-wide lockouts are urgent for you.
How it works
Better AI decisions.
Without building the machinery yourself.
Covy helps you turn one important business decision into a dedicated AI capability, defined by your criteria and measured against your standards.
- 01
Choose what needs to get better.
Identify a recurring decision that matters to your business. Define the rules, examples, and success criteria.
Task, context and success criteria - 02
Measure what works.
Customize and evaluate a dedicated model against your criteria. Compare performance on examples kept separate from training.
Customize, then evaluate - 03
Put better decisions to work.
Deploy an approved model through a hosted API and use measured results to guide future improvements.
Approve, then deploy
On our roadmap
Improve a decision with Covy The longer-term vision: AI that improves with experience.
As new examples and feedback become available, Covy is building toward a repeatable cycle of validating, evaluating, and proposing model improvements. Customers approve what goes live.
Data & control
Your business intelligence
should stay yours.
Better AI shouldn’t require giving up control of your data or accepting changes you can’t evaluate.
Built around your business.
Your rules, categories, and criteria define what a good decision looks like.
Your data stays private.
Your data and the examples built from it stay isolated from other customers and from shared model training. They are used only for your model.
Measured improvement. Controlled change.
Evaluate new versions against the benchmarks you define, and decide deliberately what gets deployed.
Get early access
What decision do you wish your AI got right more often?
Start with one important decision. Let’s explore whether a model built around your business can deliver better results.
- Start with a high-value decision.
- Define what better looks like.
- Build toward a model that keeps learning.
A few good questions
Before we
start building.
Why not just use a better prompt or a general-purpose model?
Prompts are a good place to start. But a prompt can only describe your rules, and long lists of exceptions are hard to keep consistent. A dedicated model is trained on validated examples of your decision. What matters is whether it does better on examples it has never seen, so that is what we measure.
How do we know Covy will improve our results?
We don’t ask you to take it on trust. Together we define success for your task, then compare a candidate model against your current approach on examples kept apart from training. You see the results and decide whether to deploy. Improvement is measured, not guaranteed.
Do we need production logs to get started?
No. You can start with a clear task, the output you need, and a way to judge success. Permitted data and independently prepared examples can set a starting point before production logs exist.
Does our data train other customers’ models?
No. Each model is dedicated to one customer and task. Your data and the examples built from it stay isolated from other customers’ models and from shared model training.
What is available today, and what is on the roadmap?
Our focus today is building, evaluating, and hosting a dedicated model for one decision. Learning from production feedback and proposing new versions on a regular cycle are on our roadmap. When they arrive, you will still approve every release.