Data & intelligence platforms

What it is

The data layer: collection, ingestion, modelling and the reporting surface leadership uses, with AI or analytics applied where they earn their place.

Why businesses need it

Most companies already have the data. What they lack is a trustworthy number, defined once, that everyone is looking at the same screen.

What MIWEBX delivers

• Event and product analytics instrumentation
• ETL and streaming pipelines from source systems
• Warehouse modelling and metric definitions
• Executive, operations and customer-facing dashboards
• Forecasting, scoring and anomaly detection where useful

Core features

• One documented metric definition per number
• Incremental pipelines with backfill and replay
• Data quality checks that fail loudly and early
• Row and column level access control
• Self-serve exploration alongside fixed reports
• Model outputs explained with the inputs that drove them

Technical capabilities

• Ingestion built as incremental jobs with watermarks, so a re-run backfills instead of duplicating
• Metric definitions written once in a modelled layer and reused by every report
• Source-to-target lineage recorded so any figure can be traced back to its table
• Freshness and null-rate checks that halt a load and alert rather than publish a wrong number
• Access enforced at row and column level inside the query layer
• Warehouse tables keyed deliberately, with change history kept for records that move
• Large-table reporting kept off the transactional database and sized by measured query cost

How we build it

• Name the decisions the data has to serve before choosing a source.
• Instrument events and connect systems with an owner for each feed.
• Model the warehouse and agree every metric definition in writing.
• Build pipelines with backfill and replay paths from the first one.
• Publish dashboards with permissions mapped to roles.
• Set refresh schedules, alerting and the review cadence.

Technology stack

• Python
• PostgreSQL
• SQL
• Redis
• React
• TypeScript
• Node.js

What you receive

• Deployed pipelines with source-to-dashboard lineage
• A metric dictionary the whole company agrees on
• Dashboards with permissions mapped to roles
• Scheduled refresh, alerting and failure notification
• Documentation for extending the model with new sources

Business benefits

• Decisions stop arguing about which number is right
• Problems surface on a dashboard, not in a monthly review
• New sources plug into an existing model
• Analyst time moves from extraction to interpretation