Data managementConsolidationData platformsSemantic layerAI on dataIoT
Engineering data into decisions
Resiper Labs is a specialist data engineering company. We consolidate data from scattered systems and machines, build the platforms that hold it, give it one business meaning, and put analytics and AI to work on top of it.
Data is everywhere. Usable data is not.
In most organisations, data is spread across ERPs, CRMs, spreadsheets, cloud applications and the machines on the floor. Each system has its own formats, and its own names for the same things.
So reports disagree, simple questions take days to answer, and AI tools give confident answers built on inconsistent data. The problem is rarely a lack of data. It is the missing layer that makes data usable.
Familiar symptoms
- The same question gets three different answers
- Reports are rebuilt by hand every month
- Machine data never leaves the plant
- AI pilots nobody trusts enough to use
Six specialisms. One discipline: data.
Engage us for one of them, or for several. Open a capability for its scope.
Full capability detail01 Data Management
Data architectureGovernanceData qualityMaster dataLineage
Data that is governed, documented and fit to use.
Architecture
- Data architecture and modelling
- Data standards and naming conventions
Governance
- Data ownership and stewardship
- Access rules and handling of sensitive data
- Lineage: where data comes from and how it changes
Quality
- Data quality rules and monitoring
- Master data management
02 Data Consolidation
Data pipelinesETL / ELTIntegrationReconciliationMigration
Scattered data, brought into one coherent environment.
Ingestion
- Batch and real-time data pipelines (ETL / ELT)
- Connections to ERP, CRM, files and cloud applications
Reconciliation
- Matching and de-duplicating records across systems
- Consolidation into a single, query-ready model
Migration
- Moving on-premises data estates to the cloud
- Consolidating legacy databases and reporting stores
03 Data Platforms
Data lakeLakehouseFabric Data WarehouseCloud data platformsData engineering
The right home for data: data lake, lakehouse or Fabric Data Warehouse.
Data lake & lakehouse
- Data lake and lakehouse architecture
- Raw, processed and archive data zones
Fabric Data Warehouse
- Microsoft Fabric Data Warehouse design and build
- Migration of existing warehouses and Power BI estates to Microsoft Fabric
Cloud data platforms
- Data platforms on AWS and Databricks
- Cloud infrastructure for data workloads
Data engineering
- Orchestration and scheduling
- Performance and cost tuning
In the data journey
Technologies
Microsoft Fabric · Fabric Data Warehouse · Databricks · AWS
04 Semantic Layer & Analytics
Business definitionsSemantic modelsPower BIMicrosoft FabricAnalytics
One business meaning for every number.
Semantic layer
- Business definitions and metrics
- Semantic models
- Consistent hierarchies, calculations and access rules
Analytics
- Power BI reports and dashboards
- Power BI and Microsoft Fabric architecture
- Self-service analytics on governed data
Decision support
- Forecasting and trend analysis
- Anomaly detection
05 AI on Data
Conversational dataApplied AIMachine learningAI agentsData-driven automation
AI that works on your data, not around it.
Conversational data
- Ask questions of your data in plain language
- Answers grounded in governed data and business definitions
Applied AI
- Machine learning for forecasting, classification and anomaly detection
- AI-assisted analysis and summarisation
- Understanding documents and unstructured text
Agents & automation
- AI agents where the task and the data justify them
- Automation triggered by trusted data
- Human review designed into automated steps
Assurance
- Evaluation for accuracy, cost and latency
- Deployment in your environment or through enterprise APIs
06 IoT & Real-Time Data
Device dataMachine dataReal-time ingestionReal-time analyticsAlerts
Machine and device data, connected to the systems that need it.
Device & machine data
- Sensor and device data collection
- Integration with PLCs, historians and plant systems
Real-time
- Streaming ingestion, from the edge to the cloud
- Real-time monitoring and operational analytics
- Alerts when readings move out of range
Connected to the business
- Combining machine data with ERP and maintenance data
- History kept for analysis and AI
The data journey.
How data becomes useful, from the systems where it starts to the decisions and actions it supports. Most clients need part of this journey, not all of it.
- SourcesERP · CRM · files · cloud apps
- ConsolidatePipelines · reconciliationCapability 02
- FoundationData lake · Fabric Data WarehouseCapability 03
- Semantic layerOne business meaningCapability 04
- Analytics & AIReports · AI on dataCapability 04 · 05
- ActionDecisions and alerts
06 · Second streamIoT & real-time dataDevices · sensors · machines, ingested in real time into the foundation, or straight to alerts.
01 · Every stageData managementArchitecture · governance · quality, applied across the whole journey.
AI is only as good as the data under it.
Analytics and AI inherit every inconsistency in the data they read. That is why we start with the data, and build analytics and AI on top of it.
| Without a data foundation | With a data foundation |
|---|---|
| Each system defines 'customer' differently | One definition, used by every report and tool |
| AI answers vary with the source it happens to read | AI answers come from governed, consistent data |
| Machine data stays on the shop floor | Machine data sits alongside business data |
| Every new question starts a new data project | New questions use data that is already prepared |
What it looks like in practice.
Three illustrative examples of the kind of work we do. They show the shape of the work, not results from a specific client.
- Illustrative example 01
Reporting everyone agrees on
- Fragmented data in ERP, CRM and spreadsheets
- Consolidated into one data environment
- Semantic model with agreed definitions
- Business reporting in Power BI
Capabilities02 Data Consolidation03 Data Platforms04 Semantic Layer & Analytics
- Illustrative example 02
Machine data that raises an alert
- Machine and sensor data
- Real-time ingestion
- Real-time analytics
- Operational alert to the maintenance team
Capabilities06 IoT & Real-Time Data04 Semantic Layer & Analytics
- Illustrative example 03
Asking questions of business data
- Business data from core systems
- Governed data foundation
- Semantic layer
- AI query and analysis in plain language
Capabilities01 Data Management03 Data Platforms04 Semantic Layer & Analytics05 AI on Data
Four steps, whatever the size of the job.
The same four steps apply to a data consolidation, a Fabric Data Warehouse or an AI-on-data pilot. Each step ends with something you can review before the next one begins.
- 01
Understand
We start with your business: the decisions that matter, the data behind them today, and how we will both know it worked.
OutputAgreed goals and success measures
- 02
Architect
We look at your systems and data as they are and design the target architecture around them, reusing what works.
OutputA design and a plan you can review
- 03
Build
We build pipelines, platforms, models and AI in stages you can see and test, not in one big reveal at the end.
OutputWorking data systems in your environment
- 04
Measure
We check the result against the goals agreed at the start, then decide with you what comes next.
OutputEvidence for the next decision
Start where you are.
Five ways to work with us. Start at whichever stage fits: many clients begin with a discovery workshop; others arrive with a design already agreed, or need support for a data platform that is already running.
| Stage | Engagement | Purpose | You bring | You leave with |
|---|---|---|---|---|
| 01 | Discovery Workshop | Align on the business goals, the data involved and the priorities. | The people who own the process and the data | A prioritised set of opportunities and a shared view of success |
| 02 | Architecture Assessment | Review your data estate as it is today and pinpoint the gaps that matter. | Access to systems, documentation and data owners | A current-state map, the gaps that matter, and a recommended target architecture |
| 03 | Proof of Value | Prove an outcome on your own data before committing to scale. | A defined use case and access to the data it needs | A working result and the evidence to decide whether to proceed |
| 04 | Design & Delivery | Design and build pipelines, platforms, models and AI: the implementation itself. | A decision to proceed and a named owner on your side | Working, documented data systems in your environment |
| 05 | Operate & Support | Run, monitor and support the data platform and what depends on it. | Access and an agreed way to raise issues | Pipelines, models and reports that stay maintained and supported |
Scope, format and timing are agreed with you before each engagement begins.
Client
- Apollo Tyres
Tell us about your data.
Where it lives, what you need from it, and what is getting in the way. We will tell you honestly whether we can help, and where we would start.