AI and ML models, put to work inside your systems
We prepare the data, deploy models into the applications and workflows your teams already use, and watch how they perform over time.
What we take on
- Use-case and data checkWe confirm the problem suits a model and that your data can support it.
- Data preparationFeatures and training data built from your systems by our data engineering team.
- Model deploymentModels served as APIs or batch jobs and connected to your applications.
- Monitoring and retrainingAccuracy and data drift tracked, with a plan for when and how models are retrained.
- Existing models to productionModels from your data scientists or a vendor taken from notebook to production.
Models built on pipelines and data our team already looks after.
Where this fits in your stack
AI & ML Model Integration builds on our BI Analytics work and is done by the same team that covers the rest of the path.
When companies call us about AI and ML
You notice: Our data scientists built a model, but it never left the notebook.
What we do: We package it, deploy it behind an API and connect it to the system that needs it.
Discuss thisYou notice: We want to forecast demand, but we are not sure our data is good enough.
What we do: We check the history you have and tell you plainly what a model can and cannot do with it.
Discuss thisYou notice: Our model worked at launch, and nobody knows how it performs now.
What we do: We add monitoring for accuracy and drift, and set a retraining routine.
Discuss this
Where we do AI and ML, by industry
What this service does in each sector, from that sector's page. Open yours to see the full picture.
All 3 AI and ML use cases- Manufacturing & RetailDemand forecasts built on your own sales history, once that history is clean.
- TelecomChurn and anomaly models put to work inside your existing systems.
- Media & EntertainmentRecommendation and churn models plugged into your apps once the data is ready.
- Consumer GoodsDemand forecasts built on clean sales history and plugged into planning.
How an engagement starts
You see a written plan with scope, timeline and team before any work begins.
Step 1: Talk
A call with our engineers about your systems, your data and the decisions you need it for.
Step 2: Assess
We review your current setup and send a written plan with scope, timeline and team.
For AI and ML work, that review covers the decision the model supports, the data available, any existing models and where predictions need to appear.
Step 3: Build and run
We deliver the work with documentation, and can stay on to run and support it.
Common questions
Something else on your mind? Email info@analyca.in or book a consultation.
Do you build models or integrate existing ones?
What if our data is not ready?
How do we know the model is working?
How do we start working together on AI and ML?
What you can hold us to
Companies we have delivered projects for:
A written plan before any work starts
After the first call we review your setup and send scope, timeline and team in writing.
Documentation with everything we deliver
Pipelines, databases, dashboards and deployments are handed over with documentation your own team can follow.
One team accountable for the result
The same engineers work across your databases, pipelines, dashboards and releases.
Book a consultation
Tell us what you are working on. An engineer from our team will get back to you to set up a call.