Predictive Analytics & LLM Integration
Forecasts your team can act on, and AI that actually knows your business.
We build the AI layer most businesses talk about but few actually ship: predictive models - demand forecasting, churn prediction, lead scoring - surfaced in dashboards your team opens, not buried in a spreadsheet; and large language models integrated into your product or internal tools, fine-tuned on your own data with guardrails so answers stay accurate and on-brand instead of generic.
Why it matters
What changes for you
Decisions backed by data instead of a gut call
Spot churn and demand shifts before they show up in revenue
AI that knows your business, not a generic chatbot answer
Your proprietary data stays private - never used to train public models
Models retrained on a schedule, so accuracy does not quietly decay
Controlled, monitored cost - not an open-ended API bill
What you get
Deliverables included
- Data audit and source mapping
- Predictive model design, training, and accuracy monitoring
- LLM integration (OpenAI, Anthropic, and others) for your product or tools
- RAG pipeline over your own documents
- Fine-tuning on proprietary data with safety guardrails
- Custom dashboards and alerting
- Retraining schedule and cost monitoring
How it works
Your Predictive Analytics & LLM Integration roadmap
- 01
Start with the decision
We define the forecast, score, alert, or answer your team needs and the action it should trigger, then assess whether your data can support it.
- 02
Prepare and prove the data
We map sources, clean records, establish a baseline, and test model approaches before committing to an integration your team will depend on.
- 03
Put intelligence inside the workflow
We build the model or RAG pipeline, add privacy and accuracy guardrails, and surface outputs in the product, dashboard, or tool your team already uses.
- 04
Monitor accuracy and cost
We track drift, weak answers, latency, and API spend, then set retraining and review rules so performance stays useful after launch.
FAQ
Common questions
How much data do we need for this to work?
It depends on the model. Lead scoring can work with hundreds of records; demand forecasting needs more historical data. We assess feasibility in discovery.
Can this integrate with our existing BI tools?
Yes. We export to Power BI, Looker, Google Sheets, or build standalone dashboards.
Is our data used to train public models?
No. We use enterprise API tiers and private fine-tuning so your proprietary data is never exposed to public training.
OpenAI, Claude, or open-source?
We pick based on your needs - accuracy, cost, latency, and privacy requirements. Often a hybrid approach works best.
Do we need a data science team in-house?
No. We handle the modeling and integration - you need clear business questions and access to the data and people who know your process.
Next Step
Ready to get started with Predictive Analytics & LLM Integration?
Bring the goal and the constraints. In 30 minutes we scope the work, answer your questions, and map the fastest path to launch.