Predict what happens next — and act on it before it does.
We build forecasting, scoring and recommendation models on your historical data and put their outputs into the systems where decisions are made.
From data to predicted action
NaviCS- Historical dataTransactions, behavior
- Predictive modelTrained and validated
- Forecast / scoreWho, what, when
- Business actionDelivered to CRM & teams
The challenge
Business problems we solve
Where organizations typically get stuck before engaging NaviCS for Data Science.
01
Decisions based on hindsight
Reports explain last month, but teams need to know which customers, orders or risks to act on now.
02
Models stuck in notebooks
Promising analysis never reaches the people or systems that could use it.
03
No way to prioritize
Sales and service teams treat every lead or account the same because there is no reliable score.
What we do
Data Science capabilities
Scope we deliver end to end, or alongside your own team.
Forecasting
Demand, sales and volume forecasts at the level your planning needs.
Scoring models
Lead, propensity, churn and risk scores that rank where to focus effort.
Segmentation
Group customers or products by behavior to tailor offers and service.
Recommendations
Next-best-action and product recommendations based on history and context.
Model operations
Deployment, monitoring and retraining so models stay reliable over time.
Use cases
Where it makes a difference
Concrete business applications we design and deliver.
Lead scoring
Rank new leads so sales teams call the most promising prospects first.
Churn early warning
Identify accounts showing signs of leaving while there is still time to act.
Demand forecasting
Plan inventory, staffing and capacity against expected volumes.
Collections prioritization
Focus outreach on accounts where contact is most likely to help.
How we deliver
From Business Challenge to Business Outcome
How NaviCS takes Data Science from business challenge to business outcome.
Discover
Understand the business problem, the people involved, the data and systems in place, and what success looks like.
Design
Define the solution, architecture, data flows and guardrails — and agree on how results will be measured.
Build
Deliver in short, reviewable increments so stakeholders see working software early and often.
Integrate
Connect to your CRM, data platforms, APIs and business applications so the solution fits existing workflows.
Deploy
Release to production with monitoring, access controls, documentation and team enablement.
Improve
Review usage and outcomes, tune models and workflows, and extend what works to new teams.
Technology
Technologies we work with
We design around the platforms you already use wherever possible.
- Python
- SQL / PostgreSQL
AI agent integration
Connected to how your business runs
Predictions are most valuable when something happens next. NaviCS agents can use scores to decide who to call, what to offer and when to escalate.
See how NaviCS agents act across systemsReads context · follows your rules · takes approved actions · logs every step
Outcomes
What you can expect
The business results each engagement is designed around. Specific targets are agreed with you during discovery.
- Forward-looking signals alongside historical reporting
- Teams focusing effort where it is most likely to pay off
- Model outputs delivered into CRM and daily workflows
- Models monitored and retrained as the business changes
Case studies
Case study — coming soon
We are preparing published Data Science case studies. Ask us for a walkthrough of relevant work during your demo.
FAQ
Data Science questions, answered
How much historical data do we need?
It depends on the problem. A discovery phase checks data volume, quality and history before any model is promised.
How do you measure whether a model is working?
We agree on evaluation metrics and a baseline up front, test on held-out data, and track business outcomes after deployment.
Can non-technical users understand the predictions?
Yes. We present scores with the main drivers behind them and deliver them in familiar tools such as CRM and dashboards.
Who maintains the models after launch?
We set up monitoring and retraining, and can either operate the models or hand over to your team with documentation.
Related services
Often combined with
Data Analytics
Dashboards and reporting that give every team trusted numbers they can act on.
Learn moreData Engineering
Reliable pipelines, lakehouses and warehouses that analytics and AI can depend on.
Learn moreCRM Automation
Automate lead capture, qualification and follow-up across your CRM.
Learn more