AUTOSINT

See what changed. Understand what supports it. Know what to watch next.

DAGR turns public information into reviewed intelligence for mission teams. It shows what changed, what the evidence supports, and what to watch next.

Built to complement enterprise data platforms and mission systems-not replace them.

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Scattered observations,
hidden change

Public-source observations arrive across disconnected feeds, formats, and time horizons. The changes that matter are easy to lose in the flow.

Evidence and context live in different places, so an analyst has to show not only what happened but what supports it.

Decision support that leaders can rely on requires source traceability, explicit limitations, and human review.

DAGR system workflow schematic Abstract example schematic, not operational data. Scattered observations are collected; changed observations are detected against baselines; related observations are correlated into supporting evidence; a traceable assessment cue is developed; watch conditions are defined; and outputs remain drafts until human review is satisfied, then are briefed or integrated. An evidence trail carries source references alongside every step. Observations Rights-approved public sources Evidence trail Detect change Against versioned baselines Correlate evidence Linked across sources and time Assessment cue Traceable, with limitations Watch condition Explicit follow-up conditions Human review Drafts until review is satisfied Brief or integrate
  1. Observations Rights-approved public sources
  2. Detect change Against versioned baselines
  3. Correlate evidence Linked across sources and time
  4. Assessment cue Traceable, with limitations
  5. Watch condition Explicit follow-up conditions
  6. Human review Drafts until review is satisfied
  7. Brief or integrate Reviewed outputs only

One scoped question.
Evidence challenged.
Human release.

DAGR turns a mission question into a reviewable output through a bounded, governed workflow. Unsupported conclusions are not forced through.

  1. Scope the question

    Define the mission question, time window, source boundary, and release requirement.

  2. Assemble the evidence

    Collect approved public observations and preserve source identity and context.

  3. Challenge the answer

    Separate support from contradiction, identify gaps, and reject unsupported conclusions.

  4. Review and release

    A person reviews the evidence, limitations, and assessment before release.

Explore the full governed workflow.

Real events.
Reviewed evidence.

Reconstructed from official records and retained historical observations.

Conflict and critical infrastructure · Ukraine

Russia-Ukraine War: Energy Infrastructure Under Sustained Attack

December 1, 2025-May 31, 2026

UN monitoring documented at least 423 attacks on electricity facilities, prolonged outages, and widespread disruption to essential services.

High confidence · 2 public sources

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Maritime security and energy · Strait of Hormuz

Strait of Hormuz: Shipping Risk and Oil-Flow Disruption

Fourth quarter 2025-second quarter 2026

EIA estimated Hormuz oil flows fell from 21.6 million b/d in late 2025 to 4.9 million b/d in 2Q26 while MARAD kept commercial-shipping risk high.

Moderate confidence · 3 public sources

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Prediction-market signals · United States

Federal Reserve Decision: What Prediction Markets Signaled

June 17, 2026, 17:06-21:12 UTC

Retained trading was consistent with no 25-basis-point move; only the official FOMC statement established the unchanged rate.

Moderate confidence · 5 public sources

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Explore all historical cases →

Built to verify
before it briefs

Evidence stays attached

Every observation retains its source identity, collection timestamps, and raw-content lineage, so cues and assessments stay traceable to their origins.

Challenges stay visible

Contradicting evidence is preserved, weighted, and surfaced rather than discarded. "Insufficient evidence" is a valid outcome: the system abstains rather than forcing an unsupported conclusion.

People release the output

Machine outputs remain drafts until required human review is satisfied. DAGR AUTOSINT is human-reviewed decision support, not an autonomous decision authority.

How evidence is handled.

Veteran-owned,
SBA-certified

DAGR Group LLC is a veteran-owned U.S. small business, certified by the SBA as an SDVOSB and VOSB, and registered in SAM.gov.

  • Academic Advisory

  • Agentic AI 26-2 Participant

  • Member

  • Program Member

  • Member

  • JIFX 26-2 Participant

  • NASA SEWP Via Carahsoft

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