From trusted data to AI systems that run.
BluePi builds the data foundations, models, integrations, and controls that let teams use AI inside daily work. Our engineers work with your team from first use case to measurable result.
Built alongside your team, backed by cloud and data delivery since 2012.
Cloud first. Data next. AI inside the workflow now.
Systems that change an operating decision.
Each engagement starts with a business workflow and a measurable result. BluePi builds the data path, intelligence, integration, and controls required for live use.
Demand and inventory systems
Orders
Demand signals
Inventory
Lead times
Create orders
Review exceptions
Update plans
Measure outcomes
Computer vision systems
Detect objects, defects, and events inside operational workflows.
→03Document and workflow systems
Extract, validate, route, and act on information from business documents.
→04Customer intelligence systems
Unify governed customer data and support decisions across service and commerce.
→05Operational data systems
Move trusted data into business workflows with the required latency and controls.
→Reliable AI starts with a reliable data path.
BluePi connects source systems, quality controls, governed data products, models, workflows, monitoring, and human review through one owned path.
- 01Source systems and events
- 02Ingestion and change data capture
- 03Transformation and quality controls
- 04Governed data products and features
- 05Models and decision logic
- 06Workflow integration
- 07Monitoring and human review
From one workflow to a running system.
A forward-deployed engineer builds with your team, on real data, inside the real workflow, with evidence at each release gate.
- 01
Frame
Define the decision, owner, baseline, data path, and system boundary.
Delivery artifactMetric definition - 02
Build
Create the first working slice on real data and test it with users.
Delivery artifactData map and evaluation - 03
Integrate
Connect live systems, monitoring, review, and failure paths.
Delivery artifactIntegration and test record - 04
Operate
Release, document, train, transfer, and establish the next cycle.
Delivery artifactRunbook and handover
Where BluePi delivers today
Current Google Cloud work, grounded in data engineering.
Recent BluePi projects include BigQuery and Dataproc migrations, Dataflow pipelines, change data capture, model migration, governance controls, and operational reporting.
Google Cloud Partner: Select tier · Data & Analytics competency
Review Google Cloud engineering- BigQuery migration
- Inventory, convert, validate, and operate analytical workloads
- Change data capture
- Capture source changes with replay and reconciliation
- Dataflow pipelines
- Build streaming and batch paths with observable delivery
- Model migration
- Port, evaluate, deploy, and monitor live models
- Governance controls
- Apply ownership, access, quality, lineage, and evidence
What changed when the system went live?
Each case study records the operating context, system boundary, engineering decisions, controls, and measured results.
Featured customer work
A national lifestyle retailer connected SKU-store forecasts to inventory decisions
BluePi built a recurring forecasting and recommendation cycle for ordering, reordering, distribution, and redistribution. In one category, same-store sales grew 11 percent, the safety-stock factor moved from 2.5 to 1.5, and raw-material planning extended to nine months.
Read the case study
Hourly forecasts helped restaurant teams prepare before demand arrived
Quick-service restaurants · AWS forecasting and store planning application
A logistics platform moved operational changes into BigQuery in near real time
Logistics · Google Cloud, Debezium, and BigQuery
A FASTag data platform connected toll transactions, vehicle-class detection, and violation workflows
Banking and mobility · AWS data platform, workflow automation, and computer vision
What customer teams say about the work.
Operating and technology leaders describe what it is like to build and run these systems with BluePi.
“BluePi has been a valuable partner in our Digital Technology journey. They understand our business well and work as an extended arm of our company. We have been very impressed with their technological capability and knowledge.”
“This project has been one of the most challenging and ambitious ones for Delhivery. Given the multifaceted challenges and complexity involved, Bluepi team took up the task enthusiastically and executed it all through with same perseverance and rigor. Being given the kind of reassurance and ongoing support combined with its innovative and collaborative style adds further reason for us to appreciate them.”
Start with a workflow your business already measures.
Industry pages name the operating conditions, decision flows, and evidence relevant to each setting.
Retail and consumer
Retail systems must work across product, location, channel, promotion, inventory, and customer data.
Explore workflows →02Manufacturing
BluePi builds governed data platforms and decision systems across enterprise applications, plants, partners, equipment, and reporting.
Explore workflows →03Logistics
Logistics decisions depend on fresh events from many operational systems and a reliable path through delays, duplicates, and replay.
Explore workflows →04Quick-service restaurants
Quick-service restaurant (QSR) operations combine short planning cycles, local demand, promotions, menu availability, store differences, and sharp peaks.
Explore workflows →05Banking, financial services, and insurance
Financial-service systems need clear ownership, traceable data, controlled access, measurable models, and reliable exception handling.
Explore workflows →06HR technology and SaaS
HR technology platforms need fast reporting, strict tenant isolation, resilient pipelines, and governed access across sensitive workforce and payroll data.
Explore workflows →Field notes from running systems.
Technical articles explain architecture, decisions, failure modes, evaluation, and operations in plain language.
Demand forecasting methods and the decisions they support
Compare baselines, statistical methods, machine learning, hierarchy, evaluation, and operating fit.
Read the guide →Google Cloud · 02Event-driven ingestion on Google Cloud
Design a data path for change capture, schema handling, replay, reconciliation, and observable delivery.
Read the guide →Data governance · 03A data-governance operating model for India’s DPDP Act
Translate the Digital Personal Data Protection (DPDP) Act’s requirements for privacy, ownership, purpose, access, retention, and evidence into deployed controls.
Read the guide →Data migration · 04Migration economics and cutover risk
Estimate migration work through inventory, uncertainty, validation, parallel run, cutover, and stable operations.
Read the guide →