Blinko
Churn and revenue risk · AI agents · Analytics apps

Find the revenue you are about to lose.

We build churn and revenue-risk models and AI agents on your CRM, payments, and support data, so account teams know who is at risk, why, and what to do this week. Tested before launch, measured after.

See how we build AI

Agent run · Renewal risk brief

Running

Trigger

Northline Pay payment volume is down 23%

  1. Retrieved 14 records

    CRM · Billing · Support

  2. Scored churn risk: 78%

    Churn model v3 · 3 drivers

  3. Masked account details

    Policy check passed

  4. Drafted renewal brief

    Cites 4 sources

Waiting for the account owner

Nothing is sent without approval

Grounded in 3 systems · Eval pass 96% · Illustrative example

Start here

Revenue Risk Scan

2 weeks

Two weeks on your own data. We find the accounts most likely to churn or shrink, put a dollar value on the risk, and show what is driving it.

Recurring-revenue businesses: payments and merchant services, B2B SaaS, and lenders.

What you get

  • A ranked list of at-risk accounts, with the revenue behind each one
  • The main drivers of churn in your business, in plain language
  • A check of whether your data can support an ongoing model
  • A written plan and estimate for an early-warning system, if it is worth building

Read-only access to your CRM, billing or payments, and support data. NDA signed first.

What we do

What we build.

AI engineering

AI that does the repetitive work.

Agents that read documents, research leads, and draft the next step, with a person approving.

See AI solutions

Predictive analytics

Know what happens next.

Churn, lifetime value, forecasting, and risk models that explain every prediction.

See predictive solutions

Analytics app

One place for your numbers.

Blinko Central connects your systems into one analytics app for leadership and every team.

See the analytics app

Works with HubSpot, Salesforce, QuickBooks, Xero, Stripe, Google Analytics, and most other business systems.

How we build AI

From messy data to alerts your team acts on.

Four steps, in order. Each one is built on the step before it, so we start where most AI projects go wrong: the data.

Step 1Where most of the work happens

Data cleaning and engineering

AI is only as good as the data under it. We connect your systems, fix what is broken, and build pipelines that keep it clean every day.

  • Match the same customer across CRM, billing and support
  • Fix formats, fill gaps and remove duplicates
  • Automated quality checks on every refresh
  • ETL / ELT pipelines
  • Entity resolution
  • dbt
  • Data quality tests
  • Snowflake · BigQuery · Postgres

Four systems, one clean record

Example

Before

  • CRMDuplicateNorthline Pay
  • BillingName mismatchNORTHLINE PAY INC.
  • SupportNo account IDnorthline-pay
  • SheetStaleN. Pay (old)

Pipeline

  1. 1Match
  2. 2Standardize
  3. 3Fill gaps
  4. 4Validate

After

Account
Northline Pay
Annual revenue
$240K
Payments, 30 days
−23%
Open tickets
4

42 of 42 quality checks passed

Step 2

RAG and AI agents

Agents answer from your own records using retrieval-augmented generation (RAG), and use your tools to do real work, with sources for every answer.

  • Search across documents, tickets and call notes
  • Agents that look things up and draft the next step
  • Respects who is allowed to see what
  • RAG
  • Vector search
  • LLM agents
  • Tool calling
  • MCP

How an agent answers a question

Example

Question

Why has Northline Pay gone quiet?

RAG: search your knowledge

Contracts · Tickets · Call notes

AI agentPlans, looks up, drafts

Tools it can use

CRM · Billing · Calendar

Answer with sources

Payments fell 23% after a failed integration [1], and 3 tickets are still open [2].

Step 3

Modeling and churn prediction

Machine learning models score every customer for churn risk and explain why, tested on your past data before anyone relies on them.

  • A risk score for every account, refreshed daily
  • The top reasons behind each score
  • Backtested on your own history
  • Gradient boosting
  • Survival models
  • SHAP explanations
  • Backtesting

Churn risk, scored daily

Example

  • Northline Pay78%
  • Harbor Freight Co.54%
  • Kestrel Labs21%
  • Juniper Health8%

Why Northline Pay is at risk

  • Payment volume down 23%

  • 3 unresolved tickets

  • No logins for 21 days

Backtested on two years of past accounts.

Step 4

Alerts and notifications

When a risk crosses your threshold, the right person hears about it in the tools they already use, with a suggested next step.

  • Slack, Teams, email or a CRM task
  • Rules you set: who, when and how urgent
  • A person approves anything sent to a customer
  • Slack · Teams · email
  • CRM tasks
  • Human approval
  • Monitoring

The right person hears first

Example

Your rule

If churn risk is over 70%, tell the account owner.

  • Slack
  • Email
  • CRM task

Churn alert · Northline Pay

9:02

Risk rose to 78%. Payments are down 23% and 3 tickets are open. Suggested next step: a renewal call this week.

Approve draft emailBook a call
Predictive analytics

See what is likely to happen, and why.

We build churn, lifetime value, demand, and risk models on your history, then explain each prediction so teams know what to do about it.

  • Churn and retention
  • Customer lifetime value
  • Demand forecasting
  • Revenue forecasting
  • Credit and payment risk
  • Lead conversion
Explainable
Every score comes with the drivers behind it.
Tested on your history
We backtest on past periods before anyone relies on a prediction.
Built into daily work
Scores reach the CRM, the app, or an alert, not a slide deck.

~$500K+

Annual revenue protected (estimate) · Fractal

Read how we build LTV prediction systems

Health score · 8-week forecast

Illustrative

at risk below 50actualforecasttoday 41

Northline Pay · health score

41 / 100

Down from 82 · What is moving the score

  • No contact for 21 days−18
  • Payment volume down 23%−14
  • Support tickets up 3×−9
  • Renewal due, no meeting booked−6
  • Customer for 4 years+7
Proof

Results from client work.

Nearly 100 client engagements delivered since 2023. Fiverr Pro and Top Rated.

All case studies
$100M+

in yearly transactions tracked in the system we built

We connected their systems into one live view for every role, with alerts that explain themselves in plain English.

Fractal · Construction fintech
~50%

of financial review work automated

We built an AI assessment engine that reads financial documents and sorts transactions, so advisors review one clear summary instead of a pile of paperwork.

Solventis · Consumer fintech
~2,400

hours a year, estimated at handover (about 1.2 full-time roles)

We built an AI system that cleans and matches property records automatically, so agents spend their time on clients instead of spreadsheets.

Real estate agency · Real estate
“Blinko delivered exceptional work on a highly complex problem. Deep expertise in data science, machine learning, and business domain knowledge - exceeded all expectations.”
Steve ConnAnalytics Director, Fractal & Sky Systems

Tell us what you are trying to fix.

We can look at the systems involved and suggest a sensible place to start.

A straightforward 30-minute conversation.