Custom AI Solutions
Custom AI solutions built on your data
Recommendation engines, demand forecasting, computer vision, document intelligence and fine-tuned language models — designed for your data and deployed into your product or operations.
- Timeline
- 6–12 weeks
Overview
Ideal for
- Companies with valuable historical data
- Products that need personalisation
- Operations that rely on manual inspection
When off-the-shelf AI is not enough, we build models around your data: predicting demand or churn, recommending products, detecting defects in images, classifying documents or adapting a language model to your industry.
We follow a disciplined machine-learning process: understand the decision you want to improve, check the data, build a baseline, measure accuracy honestly and only then deploy — as an API, inside your app or as a dashboard your team uses every day.
Models are monitored after launch for accuracy and drift and retrained as your data grows, so performance holds up in the real world.
What we deliver
Custom AI Solutions, end to end.
- 01
Predictive analytics
Forecast demand, sales, churn or risk from your historical data.
- 02
Recommendation engines
Personalised product and content recommendations that lift engagement.
- 03
Computer vision
Image and video recognition for quality checks, counting, OCR and moderation.
- 04
Natural language processing
Classification, entity extraction, sentiment and search across your text.
- 05
Fine-tuned & private LLMs
Language models adapted to your domain, hosted privately if required.
- 06
MLOps & monitoring
Deployment, monitoring, drift detection and retraining pipelines.
Included
What’s included
- Feasibility study and data audit
- Baseline model and success metrics
- Model development and evaluation report
- Deployment as an API or in-app feature
- Results dashboard
- Monitoring and retraining plan
- Documentation and knowledge transfer
- Data-privacy and security review
Tools & stack
Built with the best tools
- Python
- PyTorch
- scikit-learn
- Hugging Face
- OpenAI
- Vertex AI
- AWS SageMaker
- FastAPI
- PostgreSQL
- Docker
Process
How we deliver custom ai solutions
- 01
Frame
Define the decision to improve and how success will be measured.
- 02
Data
Audit, clean and prepare the data; build a simple baseline first.
- 03
Model
Train, evaluate and explain results honestly before deployment.
- 04
Deploy & monitor
Ship as an API or feature, monitor accuracy and retrain over time.
Solutions we build
Start from a working demo.
- Custom AI & MLLive demoCustomer Churn Prediction“We find out a customer has left only when they don’t renew.”At cancellation → Up to 60 days ahead, with reasons
- Custom AI & MLLive demoReview Sentiment Analysis“We have thousands of Google reviews and no idea what they say overall.”A few read by hand → All sorted by topic and outlet
- Custom AI & MLLive demoNumber Plate Recognition (ANPR)“The guard writes vehicle numbers in a register at the gate.”Stop and sign the register → Barrier opens on its own
- Custom AI & MLLive demoPeople Counting AI“We know how many people buy — but not how many walk in.”Not measurable → By hour, every day
- Custom AI & MLLive demoFraud & Anomaly Detection“Odd transactions surface at the audit, months after the money has gone.”At the audit → Held as it happens
- Custom AI & MLLive demoSales & Demand Forecasting“We buy for the festive season on gut feel — and get it wrong both ways.”Last year’s totals → Weekly forecast with a range
- Custom AI & MLLive demoAI Defect Detection“Inspectors miss small defects at the end of a long shift.”By eye, when attention allows → Every part, the same way
- Custom AI & MLLive demoAsk Your Data (AI)“I wait two days for a report to answer one question about my own sales.”A report request → 1 typed question
FAQ
Custom AI Solutions FAQ
How much data do we need for custom AI?
It depends on the problem. Some projects work with a few thousand records or images; others need more. Our feasibility study checks your data before you invest in building.
Should we fine-tune a model or use RAG?
Most business-knowledge problems are best solved with retrieval (RAG) on top of a strong model. Fine-tuning helps when you need a specific style, format or domain behaviour. We recommend based on tests, not trends.
Can the AI run on our own servers?
Yes. We can deploy open-source models in your cloud or on-premise when privacy or compliance requires it.
Ready for Custom AI Solutions?
Tell us what you are building. You get a reply within 24 hours and a free, no-pressure strategy call.
or email contact@arcadealliancenetwork.com