The AI/ML toolkit for high-risk operators
Priori connects your data into one AI-consumable model, answers questions asked in plain language, and runs machine learning against the problems of this vertical: dynamic pricing and offers, MID risk, churn win-backs, anomaly detection.
De-risk. Optimise. Automate.
How AI is being underutilised
Every click, message, purchase, and chargeback is already in your systems. AI stalls on it for predictable reasons, and each one maps to one of our products.
Priori Ontology + Lineage
Context Management
The challenge: Data lives disconnected across silos, as rows without meaning. Nothing in it knows what a MID or a sub-ID is, so an AI guessing at the rows gives answers you cannot trust.
What it does: Every source lands in one place, portals and spreadsheets included, and becomes a knowledge graph of the objects you actually run on, with the domain built in. Every answer traces back to defined objects.
ExplorePriori Lens
Business Intelligence
The challenge: An analyst writes the query, checks the joins, and ships the chart three days later. You made the call on day one.
What it does: BI behind a natural-language interface that can actually be trusted. Business users pull their own analysis in plain language, no ticket to the data team, and every number traces to the context layer.
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Priori Lab
Machine Learning
The challenge: AI can write machine learning: LTV, churn, anomalies, disputes. Today it stays in the back office, never in the operational loop where prices and traffic move.
What it does: Set the objective, say chargeback risk or lifetime value. An agent builds, ships, retrains, and grades the model, then serves it live inside the operational loop.
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Priori Runner
Agents
The challenge: No AI today reads the A/B test, drafts the change, opens the PR, and watches what ships. And none of it is proactive: it waits for a prompt.
What it does: A proactive teammate that watches on its own and runs the work end to end: drafting features, making dashboards, handling support, automating workflows. Every action lands in the audit log, and nothing ships without your sign-off.
ExplorePriori Sentry
Anomaly Detection
The challenge: Churn shows up in the monthly review, long after the users left. The cause hides in high-cardinality data: one sub-affiliate at 8% buried in a 0.6% average.
What it does: Always-on watch over every object and every slice. It learns each metric's normal shape and alerts while the problem is still forming, with the on-call paged when it matters.
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And unlike generic tools trained on everyone's traffic, all of it runs on your data and your context layer. The models learn your affiliates, your offers, your dispute history, and they stay yours.
What operators run on it
Generic tools are not built on your data and never learn your patterns. These are the jobs that only work when the context layer is yours.
Optimise
De-risk
Automate
Optimise
Dynamic pricing and offers
Prices, trials, upsells, and discount depth set by predicted value and price sensitivity, not one flat price for everyone.
offer by predicted value
Concrete example
A visitor whose first clicks match a high-LTV cohort sees the annual plan first. A price-sensitive one sees the entry tier with a trial.
How it fits together
Your systems feed one context layer. Everything intelligent reads it, and every action passes your sign-off.
Human in the loop
Your team
nothing acts without your sign-off
Interfaces & agents
Priori Lens
BI & dashboards
Priori Sentry
Anomaly detection
Priori Lab
ML models in production
real-time inference
Priori Runner
Proactive agents
your sign-off
Tools
via MCP
One shared context
The context layer
one live model of your operation, in a form AI can reason over
Semantics & ontology
Knowledge graph
Wiki
Agent skills & workflows
Business systems
Systems of record
Systems of data
Systems of knowledge
Systems of engagement
outcomes feed back into the context layer, so it keeps learning
The context layer
A live model of your business, kept current. Every source lands in one place, then gets modelled as the objects you actually run on, affiliates, users, purchases, chargebacks, MIDs, joined the way they connect in reality. It is the context every chart, model, and agent reads, so anything built on it is reading the business as it is right now.
Everything you already have
Databases and APIs, but also the portals and spreadsheets that were never built to hand data over. It all lands in one place, joined and kept current.
Your business as real objects
Affiliates, users, payments, chargebacks. Each one a real thing on one map, not a row in a table.
Joined the way they connect in reality: this user came from that campaign, paid, earned the payout, then charged back six weeks later. The map holds the whole chain.
A context the AI understands. This is the context layer: models and agents reason over things they actually know, the objects and how they relate.
Everything downstream reads the context layer
Analytics, machine learning, agents, and anomaly detection share the same definitions, so every answer agrees.
How engagements work
An end-to-end, white-glove service. We do the building.
01
Pick one measurable problem
We start with a specific, easily measurable problem in your business. It falls into one of three categories:
- Optimise, measured in LTV, conversion rate, and ROAS going up.
- De-risk, measured in chargeback ratio going down and anomalies caught.
- Automate, measured in hours saved and outsourced spend saved.
02
Solve it together
Our engineers embed with your team and do the work: they map your data into the context layer, build the agents and models for you, and stay responsible for the AI end to end, on your servers. Your people cannot run the business and debug AI infrastructure at the same time, so we carry that part. You get the result, not a toolkit and a manual.
03
Move to the next one
When the number moves, we pick the next problem and go again, if you want to. The work compounds: the data is already mapped, the context layer is already built, and the agents already know your operation, so problem two starts where problem one finished. You keep everything that was built.
Built for operators
On your servers
Deployed in your infrastructure. Your data never leaves, and what the model learns about your business stays yours.
Open, not another silo
Speaks MCP and connects to 300+ tools, so it plugs into the stack you already run instead of replacing it.
Built with you
A four-week pilot on your data. Our engineers do the building, embedded with your team. No data science hires required.
Pick the one issue. We will build it.
Start with the problem that is costing you the most this quarter.