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Engineering-grade diagnostic intelligence

Your spend just dropped.
Your bidder says it's fine.

We connect to your existing data sources—whatever they are—and apply AI-driven causal reasoning to find the exact failure hiding between your systems.

The 3am scenarios

You've seen these. You know the feeling.

Revenue collapsed at 14:00. No alert fired.

Your dashboards show healthy QPS. Your logs look normal. But spend is down 60% and nobody can explain why. The gap is between your systems — and that's exactly where standard monitoring goes blind.

Win rate cratered. Floors haven't changed.

You're bidding, the SSP is responding, but bids aren't winning. You've ruled out floors, targeting, and budget. The failure is somewhere in the handshake — a silent protocol-level mismatch invisible to both sides.

SSP says it's your side. Your team says it's theirs.

Blame ping-pong between partners eats days. Without a neutral, cross-system view of the full bid lifecycle, both sides are arguing from partial data. You need the ground truth, not another opinion.

You fixed it. But you don't know what "it" was.

A rollback recovered spend. But you never confirmed the root cause, so the same failure pattern will recur — next time in a slightly different configuration. Unknown bugs don't stay fixed.

The Product

Causal reasoning across your entire bid lifecycle

We deploy an Agentic Reasoning Layer that connects to the data sources you already have — bidstream logs, SSP response feeds, server-side tracking, or custom event pipelines. No vendor lock-in. No rip-and-replace.

The system correlates signals across sources simultaneously, tests competing hypotheses automatically, and surfaces the precise anomaly with enough evidence to act on — not just an alert to investigate.

Works with your existing data — logs, APIs, streaming pipelines, custom formats

Correlates across DSP, SSP, and tracking layers in a single analysis pass

Returns a fix, not just a finding — with confidence, evidence, and replication steps

Incident · 2026-04-17 14:07 UTC
— Connecting to data sources (3 detected) ...
— Ingesting bid-request logs [SSP_A, SSP_B] ...
— Cross-referencing tracking events ...
— Testing 24 hypotheses ...
✗ Floor shift    hypothesis rejected (p=0.91)
✗ Budget cap    hypothesis rejected (p=0.88)
✗ Targeting     hypothesis rejected (p=0.82)
✓ Latency spike   hypothesis confirmed (p=0.03)
Root cause identified
Middleware update at 13:58 UTC introduced +44ms overhead in bid-request parsing. Causes timeout on 17% of auctions at SSP_B (timeout threshold: 80ms). SSP_A unaffected — longer default timeout.
→ Suggested fix: Revert middleware v2.1 or negotiate SSP_B timeout to 130ms.
→ Estimated spend recovery: ~$12k/day.

Process

From incident to answer in hours

01

Connect your sources

We work with whatever you have — raw logs, streaming data, API endpoints, or exported files. No proprietary agent to install, no pipeline migration.

02

AI tests every hypothesis

The reasoning layer autonomously correlates signals across systems, testing scenarios in parallel that would take a human analyst days to work through sequentially.

03

Receive a confirmed fix

You get a root cause with evidence, a specific remediation, and a predicted recovery value — not a shortlist of things to investigate next.

10+

Years building programmatic infrastructure

IPONWEB

Core bidding engine experience

Protocol

Deep knowledge of RTB, OpenRTB, and proprietary handshakes

We spent a decade building the engines that run programmatic. We debug these systems the way surgeons read scans — not by guessing, but by knowing exactly where failures hide.

Stop losing money
to bugs you can't see.

Direct access to engineers who understand your stack at the protocol level. Powered by AI that reads your data, not a playbook.

Log Forensics  //  Cross-System Correlation  //  Causal Reasoning