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Challenge-led scoping
Programs open with an intelligence problem, named ownership, and success metrics agreed with transition partners before research execution begins.
Research programs
Structured research for intelligence advantage.
Stratir conducts high-payoff research programs across all-source intelligence and AI systems: challenge-led scope, operational evaluation, and transition when methods prove repeatable under field scrutiny.
Mission
To advance intelligence advantage through high-payoff research programs that withstand operational evaluation.
Stratir is an all-source intelligence research laboratory. We invest in challenge problems where breakthrough methods would materially change how teams collect, analyze, evaluate, and act. Programs are scoped for measurable payoff, executed as testable hypotheses, and held to field evaluation before transition.
Working with transition partners and operator communities, we pursue research in collection systems, knowledge representation, agentic automation, and review discipline. Methods that meet evaluation criteria move to publication and adoption. Methods that do not remain documented for scrutiny.
Laboratory model
Research organized for evaluation and transition.
Stratir runs programs the way advanced research agencies do: define the challenge, execute under review, benchmark against operational reality, and transition what the evidence supports. Outputs include software, analytical briefs, and operating practice designed for adoption without concealed assumptions about sources, confidence, or human accountability.
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Inspectable research
Architecture, provenance, and agent behavior remain visible to operator and peer review throughout the program lifecycle.
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Operational evaluation
Methods are assessed against live operational work and independent review, not against demo completion or narrative acceptance.
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Transition on evidence
Publication, release, and tradecraft transfer occur when evaluation criteria are satisfied, not when a timeline expires.
Program stack
Five layers that structure every program.
Individual programs may emphasize different layers. The sequence provides a common architecture so research compounds across mission areas rather than restarting with each engagement.
Define the intelligence problem before the method
Each program begins by naming the decision under pressure: who owns it, what evidence will satisfy review, and what operational change success must produce.
Unify the sources the mission depends on
Registries, internal records, live signals, and collection outputs converge in one inspectable layer so analysts work from evidence, not reconciliation.
Represent the operating environment with rigor
People, organizations, documents, events, and findings become durable objects with relationships, state, and lineage that reviewers and agents can reason over together.
Apply automation within defined bounds
Agents accelerate collection, enrichment, and synthesis under mission scope, with audit trails and human review treated as structural requirements, not optional controls.
Transition what evaluation confirms
Programs produce software, briefs, and operating practice that transition partners can adopt with full visibility into assumptions, provenance, and review discipline.
Research disciplines
Active research across six disciplines.
These disciplines represent areas of ongoing investigation and release within the laboratory. Mission requirements determine which disciplines a given program emphasizes and in what sequence.
Systems engineering
Intelligence systems and interfaces
Research into complete operational systems: interfaces, APIs, workflows, and integrations shaped by field requirements, evaluation criteria, and the review standards operators must meet.
Public releases including Feynman, Limitless OSINT, and Signal Canvas, with design-partner programs across entity research, aggregation, and review surfaces.
Program outputs
- Production interfaces and APIs
- Workflow integration paths
- Tenant-ready deployment
- Operator onboarding and handoff
Operational application
- Intelligence platforms requiring review surfaces, entity research, and source-attached outputs.
- Investigation environments where case context must persist across shifts and reviewers.
- Training systems where curriculum, practice loops, and progression must reflect live tradecraft.
Participation
Paths into active research.
Organizations participate through challenge programs, design partner engagements, and transition partnerships. Evaluation criteria and field assessment are co-defined before execution; adoption follows demonstrated performance.
Challenge program
A scoped research effort with defined objectives, evaluation benchmarks, and a transition partner accountable for field assessment.
System release
A complete operational system: interface, data layer, agent workflows, and integrations validated against program criteria.
Evidence infrastructure
A shared intelligence layer where search, entity resolution, enrichment, and review remain bound to source provenance.
Agent-assisted analysis
Bounded agent workflows for collection, enrichment, and synthesis, with accountable humans retaining judgment on material findings.
Design partner engagement
Joint research with your organization from challenge definition through field evaluation, release, and tradecraft transfer.
Test and evaluation
Transition is earned through operational proof.
No program advances to field adoption without meeting defined evaluation standards: source discipline verified, agent behavior inspectable, and outputs benchmarked against the challenge the program was chartered to address.
- 01Success metrics and evaluation benchmarks established before research execution
- 02Independent assessment of outputs against live operational requirements
- 03Provenance, confidence labeling, and lineage preserved across the stack
- 04Transition partner concurrence prior to field adoption or publication
Program fit
For organizations accountable to evidence, not assertion.
- 01Intelligence organizations requiring operational releases backed by evaluation, not presentation.
- 02Programs where provenance, confidence labeling, and review discipline are mission requirements.
- 03Collaborations with a named decision owner, shared evidence standard, and transition partner participation.
- 04Investigation, fraud, and compliance units rebuilding analytical context at the start of every cycle.
- 05Training leaders seeking to scale tradecraft beyond individual expert instruction.
- 06Operators determining where automation belongs in the workflow, and where it does not.