AGENTIC COMMERCE SOLUTIONS
Get ready for agent-led commerce, from product information to the cart.
Let your products be understood correctly. Let price, stock and delivery terms stay consistent. Let a shopping request turn into the right product and the right cart.
Brantial’s agentic commerce vision brings together finding the problems along that path, proposing corrections with a known source, and verifying the result of every improvement.
THE PROBLEM
Shopping with agents runs on steps that depend on each other.
This request needs product attributes, variants, price, stock and delivery terms to be assessed together. Getting the chosen product into the cart correctly is part of the same process.
Our approach examines each of those steps, so it becomes clear which problem comes from which piece of data or which transaction.
“Find me a product within my budget, in my size, that can be delivered by Friday.”
SOLUTION AREAS
Six solution areas, one shared goal: shopping tasks completed correctly.
- 01
Make product information trustworthy.
When a product’s material, dimensions, use case or compatibility is unclear, choosing correctly gets harder. That uncertainty grows when the page, the catalogue and the data feed carry different information about the same product.
- Solution focus
- Identifying missing attributes, contradictory descriptions and source mismatches. Grounding every proposed correction in verifiable product information, and marking fields that cannot be verified as missing.
- Intended outcome
- A prioritised work list, with its sources, showing which product information needs correcting and why.
- 02
Assess price, stock and delivery terms together.
The variant someone wants of a suitable-looking product may be out of stock. The price may have changed, a campaign may have ended, or the delivery terms may not meet the user’s need.
- Solution focus
- Consistency of price, stock, campaign and delivery information at product and variant level, with the source of each and the time it was checked kept visible.
- Intended outcome
- A clear account of the stale, missing or contradictory commercial terms that could affect a purchase decision.
- 03
Test whether agents can pick the right product and variant.
Meeting a shopping request involves more than finding a product name. Size, colour, budget, intended use and compatibility all have to be satisfied together.
- Solution focus
- Comparing the product selection against the user’s conditions across defined shopping scenarios, including how the task stops when no suitable product exists or required information is missing.
- Intended outcome
- Test results showing which requests were met correctly, which ended in the wrong selection, and which cases needed an explanation.
- 04
Verify that the chosen product reaches the cart correctly.
Choosing the right product and building the right cart are separate checkpoints. The variant, quantity or price in the cart can drift from what the user asked for.
- Solution focus
- Examining the steps from product selection to cart creation in authorised test environments, and reading the resulting cart back from the connected commerce system to compare it with the expected result.
- Intended outcome
- Records showing at which step the hand-off to the cart breaks down, and the difference between the expected cart and the actual one.
- 05
Turn corrections into sourced, approved steps.
Once a problem is found, it should be clear which information changes, where the correct information comes from, and which system the change is applied to.
- Solution focus
- Presenting the current value, the proposed value, the evidence, the target system and the approval it needs together, with the scope of application limited by the connection’s permissions.
- Intended outcome
- Correction proposals an authorised person can review, with explicit reasoning and scope, and an approved application flow on supported connections.
- 06
Measure the result of the improvement again.
Updating a record does not by itself show that the shopping problem is solved. The change has to reach the target system, and the same task has to be assessed again.
- Solution focus
- Verifying the updated data from the connected system, re-running the relevant tests and comparing them with the previous result, then watching whether the same problem returns after later catalogue or system changes.
- Intended outcome
- A verification history showing which problem was resolved, which one persists, and which change created a new one.
THE APPROACH
Tie every finding to an improvement process you can follow.
- 01 Understand
Define the product information, the commercial terms and the expected shopping result.
- 02 Test
Run the defined task; check the selection and the transaction result.
- 03 Correct
Locate the source of the problem; put the evidenced change in front of the person authorised to approve it.
- 04 Verify
Check that the change was applied, and assess the task again.
Through one example
- Situation
- A user is looking for an accessory compatible with a particular model. The approved manufacturer document carries the compatibility information; the field is empty in the store’s catalogue.
- Test
- The missing field makes the correct selection impossible.
- Correction
- The proposal is tied to the information in the manufacturer document and submitted for review.
- Application
- The approved change is applied over a supported connection, and the catalogue is read back.
- Verification
- The same selection task is run again and the result is assessed alongside the new test record.
Had the source carried no compatibility information, the field would have stayed as missing information awaiting verification.
TEAMS
Bring your teams around the same commercial goal.
| Team | The question they hold | Intended working outcome |
|---|---|---|
| E-commerce and commercial operations | Which problem blocks the right product selection or the cart? | Prioritised task and offer problems |
| Catalogue and product content | Which information is missing, contradictory or unverified? | A sourced correction list |
| Product and engineering | Where does the transaction break, and can we reproduce the same failure? | Scenarios, expected results and transaction records |
| Management and operations | What did we improve, and is the problem recurring? | Verification history and progress indicators |
MEASUREMENT
Judge progress against explicit criteria.
The first and later tests should be compared using the same scenarios, conditions and success criteria. Every result should be read together with the scope tested and the time it was checked.
The share of shopping tasks that correctly meet the defined conditions.
Whether the selection matches the attributes that were asked for.
Whether product, variant, quantity and price match the expected result.
Whether the transaction stops where it should when information is insufficient, no suitable product exists, or authority is missing.
Problems shown to be resolved by re-testing after a correction.
Whether a problem reappears, and how long it takes to resolve.
These are definitions. On the agentic commerce side we publish no score, success rate or improvement chart until real test data exists; the sector figures below belong to the SI visibility product that is in use today.
TODAY’S FOUNDATION
You start with your industry’s questions, not from zero.
The agentic commerce direction is built on this foundation. In the SI visibility product in use today, every vertical arrives with a ready prompt universe, its own critical query types and a weekly benchmark average. You do not type prompts into an empty box at setup; you pick your vertical.
- 9
- Ready vertical packs, each with a benchmark average
- 14,000+
- Turkish prompt templates across the verticals
- 6
- Answer engines, including AI Overviews
- 24 hrs
- From setup to the first benchmark score
| SECTOR | PROMPT UNIVERSE | CRITICAL QUERY TYPE | CATEGORY AVG. |
|---|---|---|---|
| Finance & Banking | 2,400 | Trust: “is it safe, what are the fees” | 34% |
| E-commerce & Retail | 2,100 | Comparison: “best X”, “X vs Y” | 41% |
| Health & Health Tourism | 1,800 | Multilingual treatment and clinic choice | 28% |
| B2B SaaS | 1,600 | Alternative: “X alternative”, “X pricing” | 38% |
| Automotive | 1,400 | Model comparison and cost of ownership | 36% |
| Education | 1,200 | Programme and institution choice | 31% |
| Energy & Manufacturing | 1,100 | Supplier evaluation | 24% |
| Legal & Consulting | 900 | Service and expert selection | 22% |
| Real Estate & Construction | 850 | Location and project queries | 26% |
Category average: mean visibility of the vertical’s top 20 brands over the last 30 days.
Read the measured case studiesFREQUENTLY ASKED QUESTIONS
Frequently asked questions
What is agentic commerce optimisation?
It is the work of improving data and transaction flows so SI agents can correctly complete commercial tasks such as finding products, comparing options, picking the right variant and building a cart. Brantial’s starting focus is the path from product information to the cart.
Where does SI visibility sit in this approach?
How a brand and its products appear in SI answers tells you about the discovery stage. Our agentic commerce approach adds product accuracy, commercial terms and the outcome of shopping tasks to that picture.
Which agents and commerce systems can be assessed?
The scope is set by the data you grant access to and the connections supported. Tests are designed to run over observable tasks and transactions. The read, test and change permissions each connection supports should be stated separately.
Do we have to change our whole infrastructure to start?
Our starting approach is a bounded product group and a few shopping tasks on your existing infrastructure. The connections and permissions needed for wider scope are decided after that first assessment.
Turn your readiness for agent-led commerce into a concrete starting point.
The first step is to define your product group and the shopping task you want completed correctly. Let us look together at Brantial’s agentic commerce direction and the scope that could fit you.