KETUPADiscuss a Journey

WHAT WE DO

Make the AI shopping agent you already use work better across the shopping journey.

KETUPA improves how your agent understands what shoppers need, finds the right products, asks useful questions, recommends clearly, and adapts as the conversation changes.

We implement or co-implement accepted improvements in the existing agent stack and retain reusable coverage for future checks as your products, business, and AI change.

Keep your agent. Improve the shopping experience.

Review a Shopping JourneySee the Improvement

WAYS TO WORK WITH KETUPA

Bring KETUPA in at launch, when a live experience needs improvement, or when quality must hold through change.

  1. Merchant Launch & Implementation

    Best for:
    A new merchant, category, shopping capability, or agent launch.
    KETUPA owns:
    Merchant and category requirements, shopping behavior design, agent configuration, product-data and knowledge readiness, retrieval and ranking, decision logic, tools, workflows, targeted integrations, and production verification for the agreed scope.
    Outcome:
    A merchant-specific shopping capability implemented and verified in the platform and commerce stack already selected.
    Engagement:
    Scoped implementation project

  2. Shopping Journey Optimization

    Best for:
    A live agent that retrieves the wrong products, misses important requirements, asks unhelpful questions, recommends poorly, or breaks as the conversation changes.
    KETUPA owns:
    Reproducing the failure, establishing the approved behavior, tracing the root cause to the responsible system layer, implementing or co-engineering the solution, and verifying the released behavior across affected journeys.
    Outcome:
    Materially improved production behavior with before-and-after evidence—not only a diagnostic report.
    Engagement:
    Focused optimization workstream

  3. Release Assurance & Continuous Improvement

    Best for:
    Teams releasing changes, expanding categories, or protecting important behavior as products, policies, catalogs, prompts, models, and platform configurations change.
    KETUPA owns:
    Merchant-approved scenarios, important exceptions, failed-and-fixed regression cases, release verification, and ongoing quality coverage through supported evaluation workflows.
    Outcome:
    Reusable assurance coverage that protects released behavior and makes category-by-category expansion faster.
    Engagement:
    Ongoing assurance program

IMPLEMENTATION SCOPE

We work at the system layer responsible for the shopping outcome.

A poor shopping decision does not always come from the prompt. KETUPA traces the observed behavior to the responsible layer and implements the most appropriate change across:

  • Agent configuration and instructions
  • Product and catalog data
  • Knowledge sources and RAG
  • Search and retrieval
  • Ranking and decision logic
  • Policies and guardrails
  • Tools and workflows
  • Targeted commerce integrations
  • Evaluation workflows
  • Supported model configuration and tuning

SEE WHAT CHANGES

See how the shopping experience changes when the journey is optimized.

These illustrative journeys are based on observations of publicly accessible shopping agents and representative merchant scenarios. Brand and product details may be anonymized or adapted.

The proposed optimized journeys demonstrate target behavior and do not represent deployed client work or measured production results.

N
Skincare Shopping AssistantNerovia Skin
OBSERVED JOURNEY
You

I want one product for blackheads. My skin is combination and mildly sensitive. I already have a cleanser, moisturizer, and sunscreen, and I want to stay under $40. What should I buy?

Turn 1 of 6

WHY KETUPA

The specialist team that turns merchant judgment into implemented and verified shopping behavior.

Your shopping agent platform provides powerful models, product connections, workflows, analytics, testing capabilities, and implementation controls. Your team provides the product truth, category expertise, customer knowledge, policies, and business priorities.

KETUPA connects the two in one specialist optimization workstream: define the required shopping behavior, evaluate realistic journeys, implement or co-implement the accepted changes, verify the result, and retain reusable coverage for future changes.

Our team brings senior and principal-level experience across Microsoft AI and search, Amazon production systems, commerce, retrieval and ranking, applied AI, engineering, and reliability.

The result is faster improvement of priority shopping journeys, less repeated work across internal teams, and more value from the agent already in place.

Because strong AI capability does not automatically produce the right merchant-specific decision behavior.

An agent can understand the shopper and know the product facts while still applying the wrong condition at the wrong moment.

Shopper needs, product capabilities, merchant policies, ranking priorities, exceptions, and later-turn information can all compete inside the same journey.

The failure is often not missing knowledge. It is deciding which signal should control product selection, ranking, confidence, follow-up, or the next action.

KETUPA specializes in how those signals should work together across the complete shopping journey—not simply whether the agent can produce a convincing answer.

HOW KETUPA WORKS

From merchant requirements to verified production behavior.

KETUPA leads the merchant-specific behavior workstream—from approved requirements through verified release—for one agreed category and set of priority shopping journeys.

  1. 01

    Establish the required shopping behavior

    Turn approved product truth, category expertise, customer needs, policies, and business priorities into a Merchant AI Shopping Agent Playbook.

    The playbook defines what the agent should understand, ask, select, rank, recommend, explain, and do across important shopping journeys.

  2. 02

    Build the executable evaluation suite

    Convert the playbook into a Category-Specific Shopping Agent Evaluation Suite covering realistic journeys, important variations, edge cases, and measurable success criteria.

    Run the suite against the current experience to establish a baseline and identify the highest-priority behavior gaps.

  3. 03

    Determine the root cause and implementation path

    Trace each priority failure to the responsible layer—agent instructions, retrieval and RAG, product data and knowledge, decision and ranking logic, policies, workflows, tools, integrations, or model behavior.

    Define the required solution, implementation path, and agreed release target.

  4. 04

    Implement and verify the solution

    KETUPA implements the required changes through available platform controls or co-engineers platform-owned changes with the responsible product and engineering teams.

    The released experience is verified against the merchant-approved evaluation suite, including neighboring journeys that could be affected by the change.

  5. 05

    Operationalize regression coverage

    Preserve approved scenarios, important exceptions, and previously failed-and-fixed cases as reusable regression coverage.

    The coverage supports ongoing checks as products, policies, models, and shopping journeys change.

WHAT KETUPA BUILDS AND LEAVES BEHIND

Implemented improvements, approved shopping behavior, category-specific evaluation, and retained regression coverage.

AI commerce platforms already provide powerful Agent controls, testing, analytics, and optimization capabilities. KETUPA specializes in the merchant- and category-specific shopping behavior needed to make important buying journeys work for each merchant.

We establish the merchant-approved behavior, build category-specific evaluation coverage around it, implement or co-implement the accepted improvements through the existing platform, and retain critical cases as ongoing quality coverage.

  1. Implemented Shopping Agent Improvements

    KETUPA carries accepted changes into the existing Agent and commerce stack through the native controls available for that merchant.

    Result: production behavior changes, not just recommendations.

  2. Merchant-Approved Shopping Agent Playbook

    Defines how the Agent should handle important product-selection, qualification, recommendation, ranking, claim, exception, and later-turn situations for the merchant and category.

    Result: one approved definition of how important buying journeys should work.

  3. Category-Specific Evaluation Coverage

    Tests those approved behaviors across realistic shopper situations, including missing information, competing requirements, product comparisons, cards, exceptions, and later-turn changes.

    Result: evaluation that checks the actual shopping behavior, not just whether the response sounds good.

  4. Ongoing Quality & Regression Coverage

    Important approved behaviors and previously fixed failures are retained and rerun as products, policies, models, and shopping journeys change.

    Result: the merchant can detect regressions and keep important journeys working over time.

START WITH ONE JOURNEY

Start where the agent materially influences what a customer chooses or does next.

Share the agent or platform, one category or product family, and one important shopping journey.

KETUPA will determine the optimization opportunity, required inputs, implementation path, and focused first engagement.

ONE AGENTONE PRIORITY CATEGORYONE IMPORTANT JOURNEYONE COMPLETE OPTIMIZATION CYCLE

Review a Shopping Journey