AdOps AI Assistant for White Label Ad Exchange Platforms

Your platform data. Answers in ChatGPT.

Vima’s AdOps AI Assistant connects reporting data from your white label ad exchange platform to ChatGPT, allowing AdOps teams to investigate performance, errors, traffic changes and optimization opportunities conversationally.

Let’s talk See examples

From reports To answer

Adelion data case in practice

Three ways to put it to work.

This working example uses Adelion data connected to ChatGPT through Vima’s AdOps AI Assistant. It shows what the assistant can do today; during discovery, we adapt it around your AdTech team’s own platform, bottlenecks, reporting and operating rules. Names and figures are sample data.

DAILY REVIEW / 01

Run your daily check

Review the last completed day against a 7-day baseline, from profit and revenue to requests, bids and impressions.

The assistant brings together partner performance and relevant error reports, so your team starts with a prioritized list of changes to investigate.

ChatGPT

OpenAI

Adelion data· connected via Vima Media Lab

Run yesterday’s daily check against the previous 7 days. What needs attention?

Used Adelion data DSP performance · SSP performance · Errors

ChatGPT with Adelion data

Two changes deserve a closer look.

Profit−12%

Requests+3%

Bid rate−18%

1. Demand: Buyer A accounts for most of the profit decline. Requests held steady, but bids fell.

2. Delivery: Timeouts increased on one SSP → DSP route.

Next: separate the demand change from the route’s technical issue.

Continue the conversation

Is the drop still visible today?

ChatGPT

Today is a partial UTC day. I’ll check the current pace and label the comparison separately from yesterday’s completed-day review.

Illustrative conversation · sample data

TROUBLESHOOTING / 02

Investigate performance drops

Trace the funnel from requests to impressions to find where performance changed across DSPs, SSPs and GEOs.

Check errors, timeouts, targeting and IVT alongside those changes. Findings separate confirmed observations from possible explanations, giving your team evidence for the next check or partner discussion.

ChatGPT

OpenAI

Adelion data· connected via Vima Media Lab

Buyer A is buying less. Is this a technical issue or a demand change?

Used Adelion data DSP performance · Errors · Targeting

ChatGPT with Adelion data

The decline is concentrated in two GEOs.

Illustrative breakdown
GEO	Requests	Bids
DE	Stable	Down
FR	Stable	Down

Observed: No matching increase in timeouts or DNS errors in the checked reports.

Possible explanation: A demand-side change. The reports do not confirm the buyer’s campaign settings.

Next: send the partner the affected routes and dates.

Continue the conversation

What should we ask the partner?

ChatGPT

Ask whether campaign coverage or bid filters changed for DE and FR during the affected period. Include the request and bid trend for each route.

Illustrative conversation · sample data

TRAFFIC EFFICIENCY / 03

Find inefficient traffic

Compare request volume, bid response and profit across SSP × DSP routes, GEOs and app bundles where reporting supports it.

Build a shortlist of high-volume, low-return traffic for review. After your team adjusts filters, compare before-and-after results to see whether efficiency improved without overlooking profitable traffic.

ChatGPT

OpenAI

Adelion data· connected via Vima Media Lab

Which routes use a lot of QPS but generate little profit for this DSP?

Used Adelion data Routes · GEOs · App bundles

ChatGPT with Adelion data

Here are two routes to review.

Illustrative route comparison
Route	Requests	Profit / 1M
SSP A · GB	High	$0.01
SSP B · DE	Medium	$0.34

SSP A · GB is a candidate for a bundle-level review before changing filters.

Review bid response and profitable exceptions before limiting traffic. No settings have been changed.

Continue the conversation

Did our last filtering change help?

ChatGPT

Compare the periods before and after the change: requests, bid rate, impressions and profit. Lower request volume alone does not prove an improvement.

Illustrative conversation · sample data

Also available: reporting exports and partner report comparisons. Request a filtered breakdown by GEO or app bundle, prepare a spreadsheet for review, or compare a supplied partner report with platform data to investigate discrepancies.

How it works

From your team’s problem to a working assistant.

We handle the infrastructure and integrations behind the scenes. Your team gets a simple way to ask questions about its data.

Discover where your team loses time

We start with the problems, not a fixed feature list. Together, we review recurring tasks, reporting gaps and investigations, then choose the first use case where an assistant can make a measurable difference.

Build your MCP server and assistant infrastructure

We deploy and configure Vima’s AdOps assistant infrastructure, connect reporting data from your white label ad exchange platform and turn your operating rules into repeatable AI assistant workflows. The technical setup stays behind the scenes.

Connect the conversation interface

Once the assistant is ready, we connect it to your company’s compatible ChatGPT account or workspace. If your team prefers Claude Code or another compatible interface, we assess that option during discovery.

Validate and expand with your team

We check answers against real reports and investigations, refine the workflow and measure whether it saves time. Then we add the next use cases around the problems that matter most to your team.

Data access is read-only by default, and your team decides what to change. A compatible ChatGPT plan and enabled custom integrations are required for the ChatGPT setup. Vima’s product and service are billed separately from your AI subscription.

About Vima

Engineering experience. Everyday AdOps problems.

FounderViktor Kocherha16+ years in IT

Vima Media Lab was founded by Viktor Kocherha, bringing together an IT background and hands-on work with programmatic operations. That combination shapes our approach: understand the operational question first, then build the tools and workflows needed to answer it.

Vima’s AdOps AI Assistant grew out of recurring programmatic workflows: daily health checks, DSP and SSP investigations, traffic filtering reviews, partner report discrepancies and follow-up after optimization. One working implementation uses Adelion data, while the product and its workflows can be adapted to another white label ad exchange platform, its data and the operating rules of its AdTech team.

We connect platform reporting to an AI assistant and define how each investigation should run. Working with your team, we check the findings against real reports and refine the workflow so the output provides evidence you can review and practical next steps.

Built from operational work. Designed to help your team.

Viktor Kocherha, founder of Vima Media Lab

Start with the problem your team wants to solve.

Tell us where reporting or investigation slows your team down. We’ll run a discovery session, show the Adelion data example and outline how Vima’s AdOps AI Assistant could work with your white label ad exchange platform and process.

Request a call

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