Ziggy-class research
Collects and verifies structured evidence through repeatable research cycles, regulatory and operator materials, industry datasets, and source registers.
How ASL intelligence works
An engineered intelligence system.
AllStreet Labs combines specialized agents, structured evidence, analytical frameworks, persistent operational memory, and human review. The goal is not to publish what “the AI said.” It is to produce repeatable, auditable intelligence whose inputs, transformations, uncertainty, and approval state remain visible.
This public methodology describes the operating model and current data-center implementation. Sector-specific methods evolve with each intelligence layer; production claims remain subject to human approval.
01 / Architecture
Distributed by function, bounded by design.
Purpose-built agents operate in defined lanes on isolated infrastructure. Written procedures govern research, synthesis, output structure, review criteria, authority, and safety boundaries. No single chat or model acts as the company’s memory or unreviewed publishing authority.
Seren-class strategy
Frames problems and applies OODA loops, Wardley mapping, Cynefin classification, scenario planning, and operating-risk analysis.
Codex-class build
Turns reviewed intelligence into validated datasets, reports, dashboards, publishing systems, and durable technical controls.
Nova-class execution
Translates approved intelligence into targeted business-development programs while preserving sender, approval, and outreach boundaries.
02 / Evidence flow
From collection to public artifact.
- ScopeDefine the decision, audience, geography, sector, thresholds, and exclusions.
- CollectPrefer primary regulatory, government, utility, operator, filing, and structured-data sources.
- QualifySeparate announced facts from estimates, inference, conflict, and unknown values.
- SynthesizeConnect facts to constraints, market structure, scenarios, and decision implications.
- ValidateCheck schemas, provenance, totals, geographic precision, privacy, and unsafe claims.
- ReviewA human reviews evidence, material changes, uncertainty, and publication recommendation.
- PublishOnly reviewed content enters an immutable release; candidate material remains visibly gated.
- CorrectCorrections preserve source history, dataset versions, verification dates, and a durable change record.
03 / Coordination and memory
State is tracked outside the model.
AgenticHQ routes tasks and handoffs, tracks approvals and work reports, records operating state, and surfaces improvement signals. Shared continuity files and product repositories preserve durable human-readable and implementation truth. Public products do not depend on these internal systems at runtime, and private memory or credentials never enter public datasets.
04 / Analytical standards
Evidence, inference, and recommendation remain distinct.
- Every material public fact must be traceable to a source.
- Source publication and access dates are preserved.
- Confidence and geographic precision are explicit.
- Unknown values remain null rather than becoming zero.
- Estimates and inferred values are never presented as announced facts.
- Conflicting evidence is retained when decision-relevant.
- Research agents cannot publish directly to production.
- Humans approve consequential and production-facing output.
05 / Current data-center layer
Sector-specific implementation.
The North America candidate dataset generally includes developments with at least 20 MW of disclosed or credibly estimated capacity, at least USD $250 million of investment when MW is unknown, or a documented strategic rationale. It distinguishes announced, planned, construction, operational, and cancelled phases; qualifies capacity and investment; prevents double counting; and excludes unknown MW and cancelled projects from active totals.
Coordinates are labeled exact, site, city, metro, or region. Coverage is representative rather than exhaustive, and several markets require further review before production approval.
06 / Limitations and corrections
Intelligence is versioned, not declared finished.
Sources change, projects evolve, and methods improve. Each public layer should state freshness, limitations, confidence, and approval status. Corrections should identify the affected record, disputed field, and supporting public evidence.