AI & Machine Learning
From demand forecasting to document understanding to intelligent assistants, we build practical AI that plugs into your operations and pays for itself. No science projects — every model we ship answers a business question you already have.
What you get
- Turn historical data into forward-looking predictions
- Automate judgments that used to need a human reading every case
- Keep improving — models retrain as your business generates new data
Typical timeline: Pilot model typically in 6–8 weeks
What We Do
AI & Machine Learning, in four capabilities
Machine Learning
Classification, regression, and anomaly-detection models trained on your data: which invoices look fraudulent, which customers are about to churn, which machines need maintenance before they fail.
Natural Language Processing
Systems that read and write human language — extracting terms from contracts, triaging support tickets, summarizing reports, and answering questions over your own knowledge base.
Computer Vision
Visual inspection, document recognition, and image-based quality control. Cameras and scanned documents become structured, queryable data.
Predictive Analytics
Forecast demand, cash flow, risk, and workload. Predictions feed directly into dashboards and planning processes, with confidence ranges your teams can act on.
Deliverables
Exactly what lands on your desk
No vague outcomes. Every engagement ships concrete artifacts your team can own, run, and extend.
- Use-case scoping with feasibility and expected business value
- Data audit — quality, gaps, and preparation plan
- Trained and validated models with documented accuracy
- Integration into your workflows, dashboards, or bots
- Retraining pipeline and model monitoring
- Handover documentation and team enablement
Typical toolset
Indicative — we choose tools to fit your environment.
- Python
- TensorFlow / PyTorch
- scikit-learn
- LLM APIs & RAG pipelines
- Azure AI / AWS SageMaker
Want to know if your systems are covered? Ask us directly
Proof
This service in the field
Results delivered with the capabilities described above.
Snap a receipt — AI reads the rest
RITA: AI receipt analyzer mobile application on iOS and Android
Biscovery’s own mobile product
RITA (Receipt Intelligent Analyzer) is Biscovery’s own iOS app — the receipt AI we deploy for enterprises, rebuilt for everyone. Snap or upload a receipt and multimodal AI extracts the merchant, amount, date, category, payment method, and line items. Records organize themselves into list and calendar views, and a monthly AI summary shows where the money went — with export files emailed on schedule for reporting and claims.
Read case studySearch past projects in plain language
Biscovery RAG: an internal workspace for project knowledge
Biscovery's own product
Biscovery RAG is our workspace for delivery knowledge. Teams sign in with a one-time email code, upload proposals, PDDs, and project files, and the portal indexes them for search. Sales and pre-sales can ask in English or Chinese and get answers drawn from that library.
Read case studyCaptcha-solving bot checks intermediary licences automatically
Insurer automates regulatory licence checks on the public register
Staff were manually logging into the Insurance Authority's public register to confirm that every broker and agent they worked with held an active licence. We built a bot that runs the whole check — including the captcha and one-time password — and files the results automatically.
Read case studyCVs read, scored, and ranked — automatically
Recruitment team automates CV screening with AI scoring
Recruiters were posting to each job portal by hand and reading every CV one by one. We built them an internal Talent Intelligence Workspace that posts everywhere at once, reads every CV with AI, scores candidates against weights the recruiters control, and keeps every profile searchable for the next opening.
Read case studyFAQ
Common questions
Our data is messy. Can we still use AI?
Usually yes. The first phase of every engagement is a data audit: we assess what you have, what’s usable, and what needs cleaning. Many high-value models start from data that lives in spreadsheets and legacy exports.
How do you make sure the AI is actually accurate?
Every model is validated against held-out real-world data before it touches production, with metrics agreed upfront. Where the model is uncertain, it routes the case to a human instead of guessing — accuracy you can defend to auditors.
Should we start with AI or RPA?
They solve different problems: RPA automates rule-based tasks, AI handles judgment and prediction. Many clients start with RPA for quick wins and add AI where a human still has to read, judge, or estimate.
Can we use large language models with our internal documents?
Yes. We build retrieval-augmented (RAG) assistants that answer questions strictly from your own documents, keeping data within your tenant and control. Typical uses: policy lookup, contract review, and internal knowledge search.
Ready to put ai & machine learning to work?
Tell us about the process that’s costing you the most time. We’ll come back with an honest assessment of what automation can and can’t do for it.