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.
Clients includeFractalSolventisOmniXM
AI agent · Renewal risk brief
Trigger
Northline Pay payment volume is down 23%
- Pulled 14 recordsFrom CRM, billing, and support
- Scored churn risk: 78%Based on 3 warning signs
- Hid sensitive detailsPrivacy rules applied
- Drafted a renewal briefCites 4 sources
Waiting for the account owner
Nothing is sent without approval
Illustrative example · 3 systems
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.
Three ways to put your data to work.
Automate a task, predict an outcome, or bring every number into one place. Start with one and add the rest when it pays off.
Works with HubSpot, Salesforce, QuickBooks, Xero, Stripe, Google Analytics, and most other business systems.
Explore the live demo(opens in a new tab)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.
- 01 / 04
Data cleaning and engineering
Where most of the work happens
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
- 02 / 04
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
- 03 / 04
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
- 04 / 04
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
Built this way
- Account intelligence assistant
- Document intelligence
- AI agents for follow-through
- Lead research and qualification
- Support triage with account context
- Risk and anomaly monitoring
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.
- 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.
- Churn and retention
- Customer lifetime value
- Demand forecasting
- Revenue forecasting
- Credit and payment risk
- Lead conversion
Health score · 8-week forecast
Illustrative
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
In the client's words
Nearly 100 client engagements delivered since 2023. Fiverr Pro and Top Rated.
Blinko delivered exceptional work on a highly complex problem. Deep expertise in data science, machine learning, and business domain knowledge – exceeded all expectations.
Start small. Go as far as it pays off.
Set up the tools you already have, build what they cannot do, or have us run and improve it all after launch. Most clients start with one and add the others over time.
Set up
Implementation
Get more from the tools you already pay for. We configure them, connect them, and train your team.
Best when: Your tools can do the job but are not set up or connected.
Build
Custom development
First version live in 4–8 weeks
We build what your existing tools cannot handle: AI agents, predictive models, and analytics apps.
Best when: Repetitive, data-heavy work that no off-the-shelf tool covers.
Run
Managed operations
Monthly
We keep integrations, agents, and models running after launch, improve them, and report the results.
Best when: You want the results without hiring a data and AI team.
Send us one process you want automated.
Tell us what your team repeats every week. We will reply honestly about whether AI can take it on and what it would take.
No pitch, just an honest answer.