Enterprise data warehouse
The data warehouse for hard-to-build datasets.
Rillor collects fragmented source data, turns it into structured and traceable records, and maintains it for enterprise analytics, AI systems, monitoring, and focused applications.
- Collect
- Label
- Normalize
- Maintain
Dataset registry
Data built around the evidence.
Each Rillor dataset declares what it contains, where it comes from, how it is structured, what it can support, and what remains limited. Access is confirmed by request, against current scope, source rights, evidence, freshness, and permitted delivery.
Selected Brazilian administrative authority materials, source metadata, authority identity, document structure, and relationships
Source-linked research, authority retrieval, citation analysis, and legal-data infrastructure
GPU Systems, Pricing & Market Data
GPU system specifications, normalized configurations, listings, quotes, indicative prices, reported sales, verified transactions when available, and index-methodology research
Procurement comparison, market research, valuation and depreciation analysis, and governed index development
Federal Procurement Intelligence
Selected procurement, award, supplier, item, connector, and retrieval records
Supplier analysis, identifier resolution, sourcing research, and procurement-market mapping
Public Communications & Market Response
Selected communications, entities, classifications, timestamps, and time-aligned market observations
Event datasets, communications research, historical comparison, and methodology evaluation
Four current programs. Rillor’s scope is not limited to them.
The hard part
Source data rarely arrives ready for enterprise use.
Sources change. Records disagree. Identities drift. Formats break. Rights and provenance disappear. Rillor turns that unstable material into a dataset a downstream system can inspect and use.
Fragmented sources
The required records sit across many publishers, portals, filings, and formats with no shared index.
Inconsistent records and identities
The same entity appears under different names, identifiers, units, and field conventions.
Changing formats and content
Structures are revised and material is withdrawn, so one-time extracts quietly go stale.
Rights and provenance constraints
Origin context and source rights are lost during collection, leaving records unusable downstream.
Incomplete or conflicting observations
Observations disagree, and nothing records which reading was kept or why.
Custom datasets
Need a dataset that does not exist yet?
Define the sources, entities, fields, history, geography, and refresh cadence you need. Rillor can scope, build, and maintain the dataset around that requirement.
What you specify
Source universe
Which publishers, portals, filings, feeds, or first-party systems count as in scope.
Entities
The record and entity types that must be resolvable and joinable.
Fields
Required attributes, relationships, and the labels your systems expect.
History
How far back the record set must reach, where sources permit it.
Geography
Jurisdictions and markets the dataset has to cover.
Cadence
How current the data must stay, and what a change should trigger.
Delivery
How the finished records should reach the system that will consume them.
Custom work is subject to source feasibility, rights, scope, timeline, and commercial agreement.
How Rillor builds data
Collect. Label. Normalize. Maintain.
Every stage preserves the evidence the next stage needs: source context, identity decisions, transformations, quality review, versions, and known limitations.
Collect
Acquire and preserve source material with origin metadata and applicable rights information.
- retained source artifact
- capture context
Label
Identify records, entities, attributes, relationships, and evidence classes.
- entity assignment
- evidence class
Normalize
Reconcile structures, identities, units, terminology, and time without erasing the source record.
- conformed schema
- transformation lineage
Maintain
Version datasets, track changes, review quality, reconcile conflicts, and publish limitations.
- dataset version
- change and conflict record
Governance and verification apply across every stage — they are not a fifth step.
The full warehouse methodManaged delivery
Built for the system that will use it.
Delivery may be scoped as an authenticated API, scheduled export, or another agreed structured format. Schema, cadence, versions, rights, and support are defined with the engagement.
API access is a delivery method, not a standalone product. There is no anonymous or self-service public endpoint.
Authenticated API
Scoped endpoints with defined coverage, schema, authentication, rate limits, and versions.
Scheduled export
Recurring delivery on an agreed cadence, with version and change notes.
Structured file
An agreed structured format matching the receiving system's expectations.
Other managed arrangement
Another delivery arrangement suitable for your environment and controls.
Dataset standard
Every dataset should explain itself.
Rillor records provenance, rights posture, identity and schema decisions, lineage, evidence classes, freshness, quality, versions, and limitations. Every dataset record is written against that standard, so what a dataset holds and how it was built can be read off the record itself.
Source provenance
Where each record came from and under what capture context.
Source rights posture
The recorded rights position for the source material.
Record and entity identity
How records and entities are identified and resolved.
Schema and labels
The declared structure, field definitions, and label vocabulary.
Transformation lineage
What was changed between source record and dataset record.
Evidence classification
What kind of observation a value actually is.
Freshness and versioning
Declared refresh objective, dataset versions, and change history.
Quality and reconciliation
Review steps and how conflicting observations are handled.
Explicit limitations
What the dataset does not cover and should not be used for.
Tell us what data you need, how it will be used, and how current it must stay.
Request dataNo account required · Text-only inquiry · Reviewed by the dataset team