Source Discovery
Identify source classes, official records, documents and other evidence channels appropriate to the research objective.
Research architectureRaven Mind Studios
Research / Evidence / Intelligence
Research, Data Acquisition & Intelligence Systems
Turn scattered source material into structured evidence your team can actually use.
Raven Mind Studios designs research and data systems that preserve the path from source material to usable intelligence. We connect source discovery, acquisition, provenance, verification, normalization, canonical records, indexing and research outputs so important evidence does not disappear into disconnected tabs, spreadsheets and one-off files.
Evidence, connected.
Web sources
Documents
Records
From source to structure
Keep the sourcein sight.Origins, review decisions and records remain connected.
- Identity
- Source identifiers retained
- Evidence
- Original material preserved
- Review
- Verification state attached
- Use
- Search, datasets and research
SOURCE / VERIFY / STRUCTURE / INDEX / USE
Provenance retained through the pipelineEvidence lineage
Source URLRetained
Source IDRetained
TimestampRetained
Verification StateAttached
Canonical LinkTraceable
Evidence Intelligence Pipeline
SOURCE / VERIFY / STRUCTURE / INDEX / USE
Explore all five stages
Evidence Intelligence Pipeline
SOURCE / VERIFY / STRUCTURE / INDEX / USE
Sources
Source Registry
Known source locations, classes and identifiers.
Web Sources
Public pages, records and structured online evidence.
Documents
Files, reports and source materials requiring extraction.
Acquisition
Raw Evidence
Original material retained before normalization.
Source Metadata
URL, source ID, timestamps and acquisition context.
Research Queue
Records waiting for processing or further review.
Verification
Validation Rules
Required fields, format checks and record conditions.
Corroboration
Additional source evidence where confirmation matters.
Human Review
Escalation path for ambiguity or conflicting evidence.
Structure
Canonical Record
Stable structured representation of the entity or fact.
Relationships
Connections between normalized records.
Provenance Links
Evidence remains attached to what it supports.
Intelligence
Search Index
Structured retrieval across evidence and records.
Datasets
Reusable structured outputs for operational work.
Research Outputs
Reports, dashboards or other evidence-backed views.
Research system capabilities
The useful output is not just more data. It is data with structure, evidence and context.
Research becomes substantially more useful when the system preserves where records came from, how they were evaluated and how they relate to a canonical structure. We design the workflow around that chain of evidence.
Data Acquisition
Collect source records through appropriate retrieval, import or structured research workflows while preserving their original context.
Source collectionProvenance Tracking
Keep source URLs, timestamps, source identifiers and lineage attached to the records and claims they support.
Evidence lineageVerification Workflows
Route records through validation, contradiction checks, corroboration and human review where source quality matters.
Quality controlNormalization & Canonicalization
Transform inconsistent source material into a stable schema with identifiers, relationships and controlled fields.
Canonical dataSearchable Intelligence
Index structured evidence so teams can retrieve records, compare sources and generate research outputs efficiently.
Evidence retrievalEvidence architecture
The system should be able to answer where a record came from and what happened to it afterward.
A useful intelligence platform does not flatten source material into an anonymous database. Source records, verification states, canonical entities and outputs remain connected so evidence can be followed through the system.
The connected evidence record
Open each layer to see how the original material, review and final outputs stay connected.
01 / Source layer
Retain the origin and original material.
Inspect layer
Source Registry
Known origins, source types and acquisition targets.
Raw Evidence
Original source material before normalization.
Source Metadata
Identifiers, URLs, timestamps and acquisition context.
02 / Review layer
Make uncertainty and decisions visible.
Inspect layer
Verification Queue
Records requiring review, corroboration or validation.
Conflict State
Contradictory or incomplete evidence remains visible.
Review Decision
Human or rule-based resolution before canonicalization.
03 / Canonical layer
Structure records without detaching their evidence.
Inspect layer
Canonical Records
Stable structured representation of resolved entities or records.
Relationships
Controlled links between records and entities.
Provenance
Source lineage remains attached to canonical outputs.
04 / Intelligence layer
Find, compare and use the research.
Inspect layer
Search Index
Fast retrieval across normalized evidence.
Research Views
Structured comparison, review and analysis interfaces.
Research Outputs
Datasets, reports, dashboards or downstream workflows.
Evidence Intelligence Core
Source material moves through acquisition, verification and canonicalization without losing the evidence trail that supports the final record.
AcquireVerifyNormalizeCanonicalizeIndexUse
Verification system
Not every acquired record should become a canonical record automatically.
Source quality, missing fields, contradiction and uncertainty can require different handling. The verification layer makes those conditions explicit instead of quietly converting every source into the same level of confidence.
Follow the evidence behind a field.
Choose a comparison, then select a candidate field. Inspect the original values, the normalization rule and the evidence attached to the result.
Interactive exampleTraining-room research
The field-to-source trail
Capacity
EX-S01 / ORIGINAL
Room inventory
Inventory document
Original value / Capacity
Comparable value:Inspect original source fields
EX-S02 / ORIGINAL
Schedule record
Scheduling export
Original value / Capacity
Comparable value:Inspect original source fields
:
The rule for this sample field
Candidate field
Choose a comparison to explore its evidence.
Four parts of a reviewable record.
Validate
Check whether the record meets structural and source requirements before deeper processing.
- Required fields
- Expected format
- Source identity
Compare
Identify duplicate, conflicting or corroborating evidence that changes how the source should be interpreted.
- Duplicate detection
- Contradiction checks
- Corroborating sources
Resolve
Apply review rules or human judgment where the available evidence cannot be safely normalized automatically.
- Manual review
- Source prioritization
- Resolution state
Record
Carry the result forward with the provenance and review state needed to understand how the record was established.
- Canonical identifier
- Source lineage
- Verification status
How the engagement moves
Each stage reduces the distance between raw source material and usable intelligence.
The exact research depth changes by project, but the system follows a consistent progression from research objective through acquisition, verification, structure and practical use.
Research objectives, source evidence and practical use stay connected.
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Discover
Define the research objective and identify the source classes that can support it.
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Acquire
Collect source evidence while preserving identifiers, timestamps and original context.
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Verify
Evaluate records against rules, corroboration and human review requirements appropriate to the use case.
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Structure
Normalize fields, resolve entities and create canonical records without discarding provenance.
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Use
Expose structured evidence through search, datasets, dashboards or intelligence workflows designed for the team.
Intelligence outputs
One evidence system can support multiple ways of using the research.
Once records are structured and indexed, the same evidence foundation can support searchable research, reusable datasets and evidence-backed operational outputs without rebuilding the source collection process for every use case.
Searchable
Research Index
Search structured source and canonical records without losing the ability to trace results back to their evidence.
- Canonical record search
- Source comparison
- Evidence retrieval
- Relationship discovery
Reusable
Structured Datasets
Produce normalized records that can be reused by applications, internal teams or downstream research workflows.
- Canonical identifiers
- Normalized fields
- Relationships
- Source lineage
Operational
Research Outputs
Turn structured evidence into dashboards, reports, review queues or other interfaces built around the research task.
- Research dashboards
- Review interfaces
- Evidence reports
- Operational outputs
Build the evidence system deliberately
Bring us the research process that is too important to live in disconnected tabs and spreadsheets.
Raven Mind Studios can help define the source model, acquisition workflow, provenance structure, verification process, canonical schema, search architecture and research outputs needed to turn recurring research into a maintainable intelligence system.