Capability 01
AI consulting & roadmap
Use-case selection, feasibility, model & deployment patterns, board-ready business case. Often the entry point for first-time AI buyers.
Custom AI engineering
AI · IoT · Big Data — the full custom AI engineering track.
BAP's AI/IoT service covers AI consulting, computer vision, deep learning, data science, big data, cloud deployment and custom AI application development. It is the foundation used for Smart MES, remote health monitoring and Adaptive Learning — BAP's live vertical platforms.
Capability band · one team
From sensor to dashboard — one delivery team.
Most AI/IoT programmes fail at the seam between data engineering, modelling and operations. BAP composes all six disciplines under a single accountable team.
Use-case selection, feasibility, model & deployment patterns.
Inspection, defect detection, AI cameras for line and public safety.
Forecasting, anomaly detection, scoring, churn, recommendation.
Ingestion, transforms, batch + streaming pipelines.
AWS · Azure · GCP, MLOps, monitoring, run-cost optimisation.
Telegram/Voice bots, enterprise AI products, vertical SaaS.
Capabilities · 6 disciplines in depth
What each of the six disciplines actually delivers.
Capability 02
Computer vision systems
Quality inspection (e.g. wines), defect detection, AI camera for factory lines and public safety — including edge inference on constrained devices.
Capability 03
Data science & ML pipelines
Forecasting, anomaly detection, recommendation, churn, scoring. Feature stores, eval harnesses, retraining playbooks included by default.
Capability 04
Big data engineering
Batch and streaming ingestion, schema evolution, lineage, governance. Spark, Flink, Kafka and modern lakehouse patterns.
Capability 05
Cloud deployment & MLOps
AWS / Azure / GCP deployment, autoscaling, GPU economics, monitoring, model registry, blue-green rollout.
Capability 06
Custom AI applications
Telegram/Voice bots, enterprise AI products, vertical SaaS components. Built-in to fit the customer's existing IAM and audit posture.
Same engine, different verticals
BAP's AI/IoT engine today runs five verticals.
Each vertical has a published reference and an in-production BAP product feeding learnings back into the consulting track.
Factory
Smart Factory — defect detection, scheduling
Vision-based defect detection on production lines, AI-driven dispatch and OEE optimisation. Wired into BAP Smart MES.
Elderly care
IoT monitoring, fall & vital detection
Remote monitoring, fall detection, vital-sign anomaly detection and alerting — for elderly-care facilities and home-care programmes. Not hospital-side software modernisation.
Clinical AI
Hospital-side AI
Predictive diagnosis, imaging, lab and EHR decision support — a distinct line aimed at hospitals, kept separate from eldercare offerings.
Education
Adaptive Learning & teacher tooling
Personalised AI tutor, quiz generation, learning analytics. Powers the Adaptive Learning product.
Energy
ENEBAP — energy forecasting
AI-based load forecasting and energy optimisation for industrial facilities, retail chains and campuses.
How we deliver · 5 phases
Five phases — pilot to scaled production.
Phase 1
Use-case discovery
1–2 weeks
Identify 2–3 candidate use cases, baseline data, expected ROI, sensor inventory.
Phase 2
Data & feasibility
2–3 weeks
Audit data sources, prove feasibility on small dataset, agree baseline metrics.
Phase 3
Pilot build
4–8 weeks
First sensor-to-dashboard pipeline live, with one trained model serving a single use case.
Phase 4
Production launch
4–12 weeks
Scale sensor fleet, deploy monitoring & drift detection, integrate with MES / EMR / LMS.
Phase 5
Subscription run
Ongoing
AMS subscription — retraining cadence, run-cost optimisation, periodic accuracy reviews.
Stack · four layers, BAP-daily
BAP's standard AI/IoT reference stack.
Each layer lists the tools BAP uses daily. Customers can substitute freely — these are starting points, not constraints.
Edge / IoT
Raspberry Pi / NVIDIA Jetson
ESP32 sensor nodes
Industrial PLCs
BAP AI camera series
Compute & training
PyTorch · TensorFlow
JAX / HuggingFace
AWS SageMaker · Azure ML
Vertex AI
Data layer
Kafka · Pulsar
Spark · Flink
Delta Lake / Iceberg
Postgres + Timescale
Serving & ops
Kubernetes · Istio
Triton / TF Serving
Prometheus · Grafana
OpenTelemetry
FAQ
Real questions enterprise buyers ask.
1. What makes BAP's AI/IoT offering different?+
BAP blends AI consulting, computer vision, deep learning, data science, big data engineering and cloud deployment under one delivery team. The same engine powers BAP live products, so the consulting track is informed by production learnings.
2. Does BAP build both the AI and the IoT device layer?+
Yes. BAP ships edge AI cameras, IoT gateways, MES terminals and the corresponding cloud pipelines. Customers see one accountable engineering team from sensor to dashboard.
3. Which verticals have live deployments?+
Factory (defect detection, scheduling), Elderly care (fall detection, vitals, behaviour analytics), Clinical AI (imaging, EHR support), Education (Adaptive Learning), and Energy (ENEBAP forecasting).
4. How are projects priced?+
Outcome-priced when possible (energy reduction, defect rate, lead-time) or fixed-scope depending on risk appetite. Pilots run 4–8 weeks before scaling.
5. What about data residency?+
BAP deploys on the customer's Azure tenant or on-prem K8s for regulated clients. No raw data leaves the customer's perimeter, and audit logs are co-owned with the customer's IAM team.