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Autonomous Partnerships

An agent built for partner performance economics, not general chat

Every recommendation PartnerOz makes about your budget or your partners runs through a small model fine-tuned for partner management, inside a cloud dedicated to your program. Never a shared, general purpose model trained on the open web.

How we're built

An SLM fine-tuned for partnerships, not an LLM wrapper

Most agents in this space are a thin prompt wrapped around a frontier chat model, pointed at your data. We built the opposite: a small language model tuned specifically to partnership language, wired to a deterministic statistics engine it cannot override.

  • Fine-tuned on funnel setup, segment summaries and partner correspondence, not general purpose web text
  • Runs in a cloud environment dedicated to your program, so nothing you send it trains a shared or public model
  • Scoped to language tasks only: parsing setup, summarizing findings, drafting partner copy
Where each part of the system runsArchitecture
Deterministic engine, your data never leaves codeFunnel math, segment mining, budget optimization: conventional, testable code. No model inference, no hallucination risk.
Fine-tuned SLM, dedicated cloudParses your funnel setup, writes findings and partner drafts. Purpose-built, so it is cheaper and faster than a generalist.
Human approvalBudget shifts and partner-facing messages wait for your team's sign-off before anything leaves the system.

What a general purpose LLM would cost you at scale

When a frontier, general purpose model has to reason over raw lead-level data directly, you pay frontier rates for statistical work a few hundred lines of code can do for a fraction of the cost.

General purpose LLM doing everything

~$2,400/month
  • Re-sends the full lead-level context on every reasoning step
  • Pays frontier per-token rates for statistical work, not just language
  • Cost scales linearly with lead volume and partner count
  • Harder to trace why a specific number was produced

PartnerOz SLM

~90% less
  • Statistics run as deterministic code, not model calls
  • The fine-tuned model only handles language tasks, a few hundred calls a month
  • Cost is driven by proposal volume, not lead volume
  • Every number is traceable back to the rule and data that produced it

Illustrative example for a program processing around 20,000 conversions and 6 active partners a month, based on typical published API rates for general purpose frontier models versus a fine-tuned small language model. Actual figures depend on your data volume and cadence. We'll model yours in a demo.

Build around questions that matter to your partnerships team

Will our credit-tier and financial data end up training someone else's model?

No. PartnerOz runs in a cloud environment dedicated to your program. Nothing you send it is used to train any shared or public model, and raw credit or financial attributes never leave that environment. Even the summaries we draft show aggregate, segment-level detail, never raw fields.

What can it actually change without asking us?

Nothing partner-facing. PartnerOz can query data, run analysis and draft proposals on its own, but every budget change above your set thresholds and every partner communication waits for a human approval.

How do we know why a recommendation was made?

Every proposal carries a trail back to the data and the rule that produced it: the segment, the confidence interval, the constraint it was checked against. Nothing arrives as an unexplained number.

What about fraud and low-quality traffic skewing the analysis?

Segment mining is only trustworthy on clean data, so filtering bad traffic is step one of the pipeline, not an afterthought.

What if we just build this ourselves?

If you already run a data science function, you can. But the tooling alone for fraud detection and fraud-aware attribution on a fintech-grade partner program typically runs $7,500 to $12,000 a month before headcount, and that is before building the segment-mining and budget-optimization layer on top of it. For most partner teams the real choice is not build versus buy. It is move now with an agent already tuned to this problem, or watch a competitor's CAC advantage compound while your program still runs on manual spreadsheet reviews.

Fraud & traffic quality

Segment mining only works on clean data

PartnerOz treats fraud and traffic-quality filtering as a first step, not an afterthought. The idea is not one clever tool, it is layered defense: pre-attribution blocking at the platform level, click-level filtering, and post-attribution analysis for the patterns simple rules miss, so a winning segment is a real one.

30 to 43%
typical fraud share of fintech partner traffic, industry-wide
<3%
achievable with layered, pre-attribution fraud defense
50+
deterministic detection signals available before attribution even occurs
~80%
of fraud management that runs on automation, with human review on the rest

See PartnerOz reasoning about your own funnel

Bring your conversion data and your CAC targets. We'll show you the segments and the budget move it would propose.

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