Skip to main content

AI Analysis

Last updated: 2026-10-23

Overview​

Inqura uses AI to analyze evidence and generate findings based on professional investigation standards. The AI applies a rigorous evidence evaluation framework derived from CIGIE (Council of the Inspectors General on Integrity and Efficiency), GAO (Government Accountability Office), and other cross-sector standards.

Analysis Types​

Question Analysis​

Analyzes evidence to answer a specific investigation question.

Output includes:

  • Direct answer with confidence level
  • Evidence cited with relevance assessment
  • Alternative explanations considered
  • Evidence gaps identified
  • Recommended follow-up actions

Best for:

  • Answering specific factual questions
  • Evaluating allegations
  • Determining compliance with criteria

Topic Analysis (Topic Synthesis)​

Synthesizes findings across all questions in a topic using a dedicated topic-synthesis output structure.

Output includes:

  • Source analysis summaries with provenance (which question analyses fed this synthesis)
  • Cross-question patterns with convergence/divergence indicators
  • Inter-question contradictions (where analyses disagree)
  • Emergent gaps (gaps only visible when looking across questions)
  • Cited findings with evidence references
  • Topic-level conclusions

Best for:

  • Understanding a subject area holistically
  • Identifying patterns and contradictions across questions
  • Discovering gaps that individual analyses miss
  • Preparing topic sections for reports

Gap Analysis​

Identifies missing evidence and unanswered questions.

Output includes:

  • Questions with insufficient evidence
  • Evidence types that would strengthen findings
  • Recommended evidence sources
  • Priority gaps to address

Best for:

  • Planning additional evidence collection
  • Assessing readiness for conclusions
  • Identifying risks to findings

Overall Summary​

High-level synthesis of the entire investigation.

Output includes:

  • Executive-level summary
  • Key findings across all topics
  • Overall evidence assessment
  • Conclusions and recommendations

Best for:

  • Executive briefings
  • Report introductions
  • Stakeholder communications

Generating Analysis​

From the Analysis Page​

  1. Navigate to Analysis in the sidebar
  2. Click "Generate Analysis" to open the dialog
  3. Select analysis type (Question, Topic, Gap, Summary)
  4. Choose specific questions or topics (if applicable)
  5. Optionally add investigator direction
  6. Click "Generate Analysis"

Async generation: The dialog closes immediately after firing the request. A blue "Generating..." banner appears in the analysis list. When generation completes, a toast notification appears and the status updates to "Complete" or "Failed." You can navigate freely while generation runs in the background.

Batch generation: Select multiple questions and generate analyses for all of them at once. Progress shows "Submitting 1/N..." during request phase, then "Generating analysis 1 of N..." during processing.

Gray-out of analyzed questions: Questions that already have a completed analysis are disabled (grayed out) in the Generate Analysis dialog with an "analyzed — vN" version indicator. "Select All" only selects unanalyzed questions. Questions with failed analyses remain selectable.

Investigator Direction​

Provide specific guidance to focus the AI:

  • "Focus on timeline inconsistencies"
  • "Evaluate against the procurement policy Section 3.2"
  • "Consider the vendor's explanation in Exhibit 5"

Direction is included in the AI prompt but doesn't override the evaluation framework.

Regeneration via Request Revision​

Once an analysis is complete, the only path to a new version is through Request Revision on the existing analysis:

  1. Open the existing analysis
  2. Click "Request Revision"
  3. Select a reason from the dropdown (e.g., new evidence, different direction, quality concerns)
  4. Add feedback text explaining what to change
  5. Click "Regenerate"

This preserves audit trail integrity — every new version has a documented reason.

Admin/test workflow: Admins can alternatively delete an existing analysis entirely, which re-enables the question in the Generate Analysis dialog for a clean regeneration.

Quality Metrics​

Every analysis includes quality metrics to help you assess reliability.

The Check Badge​

BadgeMeaning
Check: passedClaims traced to evidence, question addressed, validation passed
Check: review neededUntraced claims, coverage gaps or a failed validation; review before relying
Check: not yet runThe background checks have not finished yet, or could not complete

The check measures how the analysis was produced. It is not the confidence level of the conclusion (see Confidence Levels below), and the two can disagree. Thresholds are in the Quality Metrics guide.

Faithfulness Score​

Measures whether AI claims are grounded in evidence:

  • 95-100%: Nearly all claims verified
  • 85-94%: Most claims verified
  • 70-84%: Notable unsupported claims
  • <70%: Many unverified claims

Coverage Score​

Measures whether the analysis addresses all aspects:

  • 95-100%: All aspects addressed
  • 85-94%: Minor gaps only
  • 70-84%: Notable gaps
  • <70%: Major gaps

Retrieval Quality​

Measures how relevant the retrieved evidence was:

  • Strong: Average similarity > 0.35
  • Moderate: Average similarity 0.15-0.35
  • Weak: Average similarity < 0.15

See AI Quality Metrics for detailed information.

How AI Analysis Works​

1. Evidence Retrieval (Three-Stage Hybrid Pipeline)​

The system uses a three-stage hybrid retrieval approach:

Stage 1 — Keyword Search: PostgreSQL tsvector/tsquery finds evidence containing the same terms as the question. Catches exact-match evidence (case numbers, names, policy codes) that semantic search misses.

Stage 2 — Semantic Search (Vector Embeddings): Amazon Titan Text Embeddings V2 via pgvector converts questions and evidence into meaning vectors. Catches evidence that's relevant even when it uses different words.

Stage 3 — Reranking: Cohere Rerank 3.5 re-evaluates merged results from Stages 1 and 2 by reading question and evidence together. Promotes keyword-surfaced results that semantic search missed; demotes false positives.

Results are combined using team-draft interleaving (alternating picks from vector and keyword ranked lists) before reranking. Query-adaptive weighting automatically boosts keyword weight for entity-heavy queries containing proper nouns, dates, and identifiers.

2. Document Processing​

For file attachments:

  • PDFs have their text extracted. A scanned (image-only) PDF has no extractable text, so it reaches analyses by its title only
  • Word documents have text extracted
  • Excel files are converted to CSV format
  • Images are not read by the AI: they are stored and viewable, and reach analyses by their title only. Analyses use extracted text, not the file itself

3. Analysis Generation​

The AI receives:

  • System prompt with evaluation framework
  • Investigation context (title, focus, work type)
  • Relevant evidence chunks
  • Full document files
  • Background documents (if configured)
  • Framework documents (if configured)
  • User direction (if provided)

4. Citation Repair​

The AI sometimes generates valid cited findings with correct evidence titles but hallucinated UUIDs instead of real evidence IDs. A citation repair step runs between AI output and the sanitizer, matching hallucinated IDs to real ones by title. The sanitizer remains as a safety net.

5. Quality Checking​

After generation:

  • Faithfulness checker verifies claims against evidence
  • Coverage checker identifies gaps in question coverage
  • The Check result is calculated
  • Results are stored with analysis
  • Retrieval quality is logged (retrieved vs. cited evidence) for continuous monitoring

Quality checks work correctly for all analysis types including gap analysis and error check, with proper text extraction for non-question output schemas.

Evidence Considered Panel​

Each analysis includes an expandable "Evidence Considered" panel showing which evidence passages were retrieved and how:

  • Plain-language retrieval story: "Found by: Keyword + Vector | Rerank confirmed" per passage, replacing raw pipeline diagnostics
  • Header: Shows total evidence passages used
  • Raw scores: Available behind an info toggle for advanced users
  • No misleading labels: Removed the old "Low / Below threshold" similarity labels that confused users despite the system working correctly

Evidence Considered Panel: Gap Impact Badges​

Gap analysis outputs include impact badges (Critical / Significant / Minor) with tooltip definitions explaining what each level means operationally.

Evidence Evaluation Framework​

The AI uses 10 quality criteria from CIGIE/GAO standards as a conceptual framework when assessing evidence — they shape what the AI looks for, but they're no longer scored individually per item. The active output is a simpler 2-field assessment (confidence + reasoning) rolled up across all 10 dimensions.

The 10 framework dimensions:

  1. Relevance: Does it address the question?
  2. Reliability: Is the source credible?
  3. Sufficiency: Is there enough evidence?
  4. Validity: Does it prove what it claims?
  5. Competence: Is the source qualified?
  6. Completeness: Are there gaps?
  7. Timeliness: Is it current?
  8. Objectivity: Fact-based or opinion?
  9. Authenticity: Is it genuine?
  10. Consistency: Does it align with other evidence?

The historical complete framework (the original 10-criteria checklist) is an internal reference document, not published on this site; the current 2-field model is described above and in the product-facts register.

Confidence Levels​

The AI assigns confidence to conclusions:

LevelMeaning
EstablishedStrong, sufficient, convergent evidence
ProbableModerate evidence or strong with minor gaps
PossibleSome evidence but significant gaps or weaknesses
InsufficientEvidence too weak or incomplete to support reliable conclusions

These four levels apply to legacy-path analyses. On a cited-path analysis, Inqura instead proposes an answer (Yes / Partly / No / Cannot be determined) and, when the answer is definitive, a High or Moderate confidence. See the Interpreting Analysis guide, Chapter 4.

Finding Status​

After reviewing analysis, record your determination:

StatusWhen to Use
PendingNot yet reviewed
SubstantiatedEvidence supports the allegation
Partly substantiatedEvidence supports part of it; you state which part
Not substantiatedEvidence does not support the allegation
Unable to determineEvidence is insufficient to determine

The determination can be recorded when you sign off an analysis or on the Organize page. Where you depart from Inqura's proposal, you give a reason, and it is recorded with the determination.

Background Documents​

Context documents that inform the AI:

  • Charge letters
  • Organizational charts
  • Scope memos
  • Relevant policies

Toggle "Include in Analysis" to control which are sent to AI.

Framework Documents​

Evaluation criteria documents:

  • Policies and procedures
  • Regulations and standards
  • Industry frameworks
  • Audit criteria

The AI uses these as the basis for evaluating compliance.

Usage and Quotas​

AI Generation Limits​

PlanAllowance
Trial15 total (does not reset)
CoreGenerous monthly allowance — your exact figure is on Billing > Usage
ProGenerous monthly allowance — your exact figure is on Billing > Usage

AI generations include:

  • Question/topic/gap/summary analysis
  • Report section generation
  • AI Agent chat messages

Monitoring Usage​

View usage in Settings → Billing:

  • Current period usage
  • Limit and remaining
  • Usage history

Best Practices​

Before Generating​

  • Ensure evidence is uploaded and linked
  • Check that embeddings are processed
  • Configure background/framework documents
  • Write a clear focus statement

Providing Direction​

  • Be specific about what to focus on
  • Reference specific documents or policies
  • Ask for particular aspects to be addressed
  • Note any concerns to investigate

Reviewing Output​

  • Check quality metrics first
  • Verify key claims against evidence
  • Note any unsupported claims
  • Review coverage gaps
  • Assess if regeneration is needed

Iterating​

  • Add missing evidence before regenerating
  • Provide more specific direction
  • Split complex questions into parts
  • Try different analysis types

Troubleshooting​

Low Faithfulness Score​

  • Evidence may not directly support AI's phrasing
  • Add more explicit evidence
  • Check if relevant evidence was retrieved

Low Coverage Score​

  • Question may be too complex
  • Evidence may not address all aspects
  • Provide direction to focus on gaps

Weak Retrieval​

  • Add more evidence related to the topic
  • Ensure evidence has been processed for embeddings
  • Rephrase question to match evidence terminology

Validation Failed​

  • AI produced malformed output
  • Try regenerating
  • Contact support if persistent
  • AI Quality Metrics - Detailed metrics explanation
  • Evidence Evaluation Framework — internal reference (full historical framework text), available on request
  • Investigation Workflow - Analysis in context