Define the metric that matters to your business. Kovil AI builds the system to move it. Part of our fee is paid only when the target is hit. Not just shipped. Delivered.
Most AI development is billed by the hour or by the deliverable. You pay for engineering time regardless of whether the system works. Outcome-based development flips this: a portion of Kovil AI's fee is contingent on the system moving a business metric you define.
This is not a gimmick. It requires genuine commitment from both sides. You need clean data, an established baseline, and a metric that is attributable to the AI system. We need confidence in the technical approach and realistic timelines. When those conditions are met, the model is transformative.
The practical structure is usually a hybrid: a base fee covers engineering costs so the project is commercially viable, and a performance fee is paid when the metric target is confirmed in the measurement window. The split and structure depend on the project.
A plain-English example
A healthcare company's support team handles 8,000 tickets per month. Today's chatbot deflects 18%. They want 55%. We agree: base fee covers build costs, performance fee is paid at 60 days if deflection is 50% or higher (measured by Zendesk source tag). We ship at week 8. At day 47, deflection reaches 58%. Performance fee is triggered.
Four stages from metric definition to performance fee payment. Every stage has a clear purpose and deliverable.
Metric Definition Scoping
2 weeks
We define the success metric together. Establish the baseline, agree the target, confirm the measurement methodology, and assess technical feasibility. No ambiguity about what "success" means.
Example: "Support ticket deflection rate from 22% to 55% within 60 days of launch, measured by Zendesk ticket source tag."
Contract and Fee Structure
1 week
We agree the fee structure: base retainer (covers engineering cost) plus performance fee (paid when the metric is hit). Or a pure performance model for the right projects.
Example: 40% base on delivery, 60% performance fee paid at 30-day measurement window if target is met.
Build and Deploy
4-12 weeks
We build the AI system with the metric as the north star. Every technical decision is made with the outcome in mind, not just feature completion. Weekly progress against eval benchmarks.
Example: RAG pipeline tuned against RAGAS context-recall score, with weekly benchmark reports against baseline.
Measurement Window
30-90 days
After production deployment, the agreed measurement window runs. We monitor the metric and iterate rapidly on any issues. Performance fee is triggered when the target is confirmed.
Example: 60-day window. Deflection rate hit 61% at day 42. Performance fee paid. Monitoring continues.
Real metrics from real use cases. Each one measurable, attributable, and time-boxed.
That is the commitment. Tell us the metric you care about and we will tell you honestly if it is achievable.
Define Your Outcome MetricWe are selective about which projects we take on this model. Here is our honest assessment.
A B2B SaaS company had a 9-person support team handling 6,200 tickets per month. Their chatbot deflected 14%. They wanted 50%. We agreed: base fee covers engineering, performance fee paid at day 60 if deflection exceeds 45%.
We built a RAG pipeline over their help center, product changelog, and support macros. Added a confidence-gating layer that escalated to humans when the model was uncertain. Shipped at week 7.
Outcome-based AI development is an engagement model where part or all of the fees are tied to measurable business results rather than time or deliverables. Instead of paying for engineering hours, you pay when a specific metric moves: support ticket deflection rate, contract review time, lead qualification accuracy, or another agreed KPI. Kovil AI takes shared risk on the project succeeding.
We define the success metric together during a 2-week scoping engagement before any build begins. The metric must be: measurable with existing instrumentation (or instrumentation we add), attributable to the AI system (not confounded by other changes), and achievable within the agreed timeline based on benchmark data. Common metrics include deflection rate, processing time reduction, accuracy on a classification task, and cost per transaction.
This depends on the specific contract structure. In a pure outcome-based model, the performance fee is not paid if the metric is not reached. In a hybrid model, a base fee covers engineering costs and the performance fee is paid on top when the metric is hit. Either way, Kovil AI has real skin in the game, which aligns incentives toward measurable success rather than just shipping code.
Outcome-based works best when: there is a clear, measurable metric you care about; the baseline is established (you know what "good" looks like today); the AI system is the primary lever for moving that metric; and the measurement window is 3-6 months. Good examples include customer support deflection, document processing throughput, fraud detection precision, and sales lead scoring accuracy.
Fixed-price ties the fee to delivering a defined set of features by a defined date. Outcome-based ties the fee (or a portion of it) to a business metric moving by a defined amount. Fixed-price is about delivery risk. Outcome-based is about performance risk. Many engagements use a hybrid: a fixed base fee for the build, plus a performance component paid when the metric is reached.
Typically 30-90 days after production deployment. This window must be long enough for statistical significance and to exclude launch effects, but short enough to keep the engagement commercially reasonable. The measurement window and method are agreed in the contract before work begins.
Yes, but the first step is establishing the baseline. If you do not have historical data on your current process, we instrument it during the scoping engagement. This usually takes 2-4 weeks of data collection before we can define a meaningful target. We cannot agree a performance metric without knowing where you are starting from.
We are selective. Outcome-based engagements require genuine confidence in the technical approach and the quality of the client's data. We will decline engagements where the data is too noisy, the metric is unattributable, or the timeline is unrealistic. This selectivity is what makes the model credible: we only take on outcome-based work where we genuinely believe the system will perform.
Related engagement models and services
Tell us the KPI that matters. We will assess whether it is achievable and what an outcome-based structure would look like for your project.
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