Evals

Catch bad answers before customers do

We turn real AI failures into tests your team can run on every release. Your experts define what good looks like, we build checks that catch bad answers and wrong actions, and you decide what ships with the evidence in front of you.

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Our Process

Evals

Define a job well done

We work with your product and domain experts to turn customer tasks and real examples into completion criteria, unacceptable behavior, permissions, and handoff rules. Every rubric change is versioned, so you can see when the definition of good moved.

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Evals

See where the task breaks

We capture the full run from request to outcome, including tool calls, results, state changes, versions, latency, and cost. A reviewer can point to the first step that went wrong, with the evidence attached.

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Evals

Turn each failure into a test

We sample ordinary traffic and difficult cases, turn reviewed failures into reproducible tests, and rank defects by customer consequence. Stress cases stay separate from the samples used to estimate how often things fail in production.

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Evals

Check the checkers

Code handles exact rules. For judgment calls, we compare model evaluators with expert labels and surface the failures they miss and the alarms they raise for nothing. Reserved examples stay out of tuning.

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Evals

Catch regressions before release

We rerun the changed agent on versioned tasks from a clean starting state and record every agent, prompt, tool, and dataset version. Your team can reproduce a failure and compare the candidate with the baseline.

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Evals

Keep the tests honest

We protect release tests, labels, and fixtures from the tuning process and flag any change to them. Adversarial cases run in a controlled environment, where we test whether tool access and permission limits hold.

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Evals

Decide what is ready to ship

We compare task completion, regressions, latency, retries, and human review cost across candidate versions, and verify experiment results before anyone reads them. Your team gets a written summary of tradeoffs and open risks, and makes the call.

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Evals

Keep quality high in production

We sample routine runs and known risk areas, group new failures with trace evidence, route ambiguous cases to a reviewer, and watch for drift by segment. New failures join the suite, and fixes are ranked against the rest of the backlog.

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Evals

Connect AI quality to customer value

We connect quality reviews and product experiments to onboarding, usage, retention, and Stripe revenue. Cohort analysis shows which improvements help customers succeed and what the product team should test next.

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Agents in production

Your strategist drives the bus, making calls on onboarding, activation, and retention. They also build the agents to accelerate every stage of your product growth roadmap, from finding friction to learning what keeps customers coming back.

Success criteria

Define a job well done

Kiko agent 01

Success Criteria Writer

Turns customer tasks and expert examples into proposed completion criteria for product review.

Kiko agent 02

Customer Task Mapper

Maps the customer’s intended job, starting conditions, and useful outcome from approved research and product data.

Kiko agent 03

Rubric Version Tracker

Records rubric versions and review decisions so teams can see when the definition of success changed.

Kiko agent 04

Trace Review Assistant

Finds agent failures in real runs for your team to review

Trace review

See where the task breaks

Kiko agent 05

Trace Review Assistant

Finds agent failures in real runs for your team to review

Kiko agent 06

Tool Call Auditor

Reviews agent tool calls, inputs, results, and state changes to find the first step that went wrong.

Kiko agent 07

Instrumentation Auditor

Detects missing or duplicate events, property drift, impossible sequences, identity failures, and broken assignments.

Kiko agent 08

Failure Triage Agent

Groups observed AI failures, attaches trace evidence, and routes ambiguous cases to a reviewer.

Failure cases

Turn each failure into a test

Kiko agent 09

Quality Review Sampler

Selects real agent runs for routine quality review

Kiko agent 10

Failure Case Builder

Turns reviewed failures into reproducible test cases with known inputs and expected outcomes.

Kiko agent 11

Test Coverage Reviewer

Maps tests to customer tasks and known failure types, then flags gaps in coverage.

Kiko agent 12

Risk Prioritizer

Ranks observed failure types by customer consequence, frequency, and evidence for product review.

Evaluator calibration

Check the checkers

Kiko agent 13

Evaluator Calibration Assistant

Checks AI evaluators against expert judgments

Kiko agent 14

Expert Label Coordinator

Organizes expert review batches and tracks disputed labels until a reviewer resolves them.

Kiko agent 15

Grader Disagreement Reviewer

Compares evaluator scores with expert labels and surfaces missed failures and false alarms.

Kiko agent 16

Holdout Set Keeper

Maintains a versioned record of reserved test cases and flags their use during tuning.

Regression testing

Catch regressions before release

Kiko agent 17

Regression Test Runner

Tests agent releases for new failures before shipping

Kiko agent 18

Test Fixture Builder

Prepares task inputs and starting-state fixtures for repeatable agent tests.

Kiko agent 19

State Reset Verifier

Checks that each test starts with the intended data and state so prior runs do not contaminate results.

Kiko agent 20

Release Version Tracker

Records agent, prompt, tool, dataset, and evaluator versions for each candidate test run.

Test integrity

Keep the tests honest

Kiko agent 21

Holdout Set Keeper

Maintains a versioned record of reserved test cases and flags their use during tuning.

Kiko agent 22

Permission Boundary Tester

Tests whether tool access and permission controls hold under the agreed adversarial cases.

Kiko agent 23

Adversarial Case Generator

Proposes difficult inputs based on known failure types for controlled testing and expert review.

Kiko agent 24

Test Integrity Monitor

Flags changes to protected labels, fixtures, and release checks for the evaluation owner.

Release decisions

Decide what is ready to ship

Kiko agent 25

Regression Test Runner

Tests agent releases for new failures before shipping

Kiko agent 26

Quality Cost Reviewer

Compares completion quality, latency, retries, and human review costs across candidate versions.

Kiko agent 27

Experiment Results Agent

Verifies exposure integrity, primary and guardrail metrics, sample balance, and preplanned segments.

Kiko agent 28

Release Decision Writer

Summarizes candidate results, regressions, and open risks for the team’s release decision.

Production quality

Keep quality high in production

Kiko agent 29

Quality Review Sampler

Selects real agent runs for routine quality review

Kiko agent 30

Failure Triage Agent

Groups observed AI failures, attaches trace evidence, and routes ambiguous cases to a reviewer.

Kiko agent 31

Quality Trend Monitor

Tracks production failure patterns and segment changes so reviewers can spot quality drift.

Kiko agent 32

Growth Backlog Prioritizer

Scores work against the binding constraint, expected movement, confidence, cost, dependencies, and time to evidence.

Learning Loop

Connect AI quality to customer value

Kiko agent 33

Product & Revenue Data Agent

Connects product experiments and quality reviews to usage, customer records, and Stripe revenue. Flags missing data before results are compared.

Kiko agent 34

Customer Cohort Analyst

Compares activation, conversion, retention, and revenue across customer cohorts to find who stays and pays.

Kiko agent 35

Onboarding Journey Analyst

Uses product events in tools like Mixpanel to find where new customers get stuck and whether the experience delivers on its promise.

Kiko agent 36

Learning-to-Brief Agent

Turns usage, cohort, and revenue findings into proposed product changes and growth experiments for the team to review.

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