Designing for
the Unspoken

Tacit Knowledge

Michael Polanyi (1966) defined tacit knowledge as understanding that is "inherently difficult to articulate," summarized in his aphorism:

"We can know more than we can tell."

It encompasses know-how, mental models, and intuitions gained through action and experience.

Situated Learning

Brown, Collins & Duguid (1989) established that knowledge is inseparable from the activity, context, and culture in which it develops.

The "Master-Apprentice" model depends on legitimate peripheral participation: novices learn by working alongside experts in authentic contexts.

Information Overload

Miller (1956) established that human working memory can hold approximately 7±2 items simultaneously.

When data volume exceeds this capacity, processing degrades. Modern intelligence environments routinely exceed these limits, creating the "cognitive bottleneck."

Deep Smarts (Leonard, 2005)

Expert intuition built through years of experience: pattern recognition, judgment under uncertainty, and contextual awareness.

Unlike explicit knowledge in manuals, deep smarts are transferred through guided experience and Socratic dialogue.

Inheritance Imperative

Hang & Zhang (2024) argue that when experts retire, organizations face a "knowledge inheritance crisis."

Departing expert knowledge is organizational inheritance that must be actively claimed through structured protocols.

Recognition-Primed Decision

Klein (1993) showed that experts do not deliberate options; they recognize patterns.

TacitFlow preserves these pattern libraries by capturing the cues experts notice.

Workforce Crisis

• OECD (2025): 40% of civil servants report burnout; 13% intend to leave within 12 months.
• Europol (2025): Urgent need for "state-of-the-art analytical competence" to address capability gaps.

Methodology

Arksey & O'Malley (2005) established the framework for scoping reviews, distinguishing them from systematic reviews by their broader exploratory purpose. Scoping reviews map key concepts, evidence types, and research gaps rather than synthesizing effect sizes.

PRISMA-ScR

Tricco et al. (2018) extended PRISMA reporting guidelines specifically for scoping reviews, adding items on rationale for review type selection and deviation from protocol. This enhances transparency and reproducibility in exploratory evidence synthesis.

The Externalization Gap

The SECI model assumes externalization is straightforward. The review found the opposite: converting tacit knowledge to explicit form is the primary bottleneck in knowledge transfer.

Key Insight

A gap exists between how people share knowledge in practice (socially) and how technology supports it (structurally).

Detailed Findings

Most workplaces rely on mentoring, storytelling, and job shadowing. AI or digital tools for experience sharing remain rare. While socialization methods are common, they rarely succeed in making tacit knowledge explicit.

Thematic Analysis

Braun & Clarke (2006) established thematic analysis as a qualitative method. Their six-phase process (familiarization, coding, theme generation, review, definition, write-up) enables systematic pattern identification while remaining accessible. Unlike grounded theory, it permits both inductive and deductive coding.

From Interviews to EASCI

This study revealed that tacit-to-explicit conversion does not happen in one leap (as SECI suggests). Interview data showed knowledge transfer requires multiple deliberate stages: initial recognition of experience, tentative articulation, structured formalization, social validation, and adaptive refinement.

Participating Organizations

• EASS: Estonian Academy of Security Sciences
• KRA: Defence Resources Agency
• MoD: Ministry of Defence
• PBGB: Police & Border Guard Board
• RIA: Information System Authority
• SMIT: IT & Development Centre

"Sticky" Tacit Knowledge

Von Hippel (1994) coined "sticky information" to describe knowledge costly to transfer. Tacit knowledge is deeply embedded in context, making it resistant to codification.

Current Mechanisms

Transfer relies on ad-hoc mentoring ("watch me do this"). This breaks down during rapid turnover or when key personnel leave unexpectedly.

Practitioner Interest

Participants expressed strong preference for tools making sharing easier. Voice-based AI assistants resonated well for quick, natural capture.

SECI Model Context

Nonaka & Takeuchi (1995) describe four knowledge conversion modes. While organizations excel at Socialization (mentoring), they struggle with Externalization (articulating concepts).

Convergent Validity

Literature and field data independently identify the same failure point, strengthening confidence in this finding.

Documented Loss

Cho et al. (2020) documented this in Security Operations Centers: tacit expertise remains "trapped in heads" until personnel depart, then vanishes.

Why Voice?

• Natural: Experts articulate experience through storytelling.
• Focus: Speaking allows capture without breaking concentration.
• Context: Voice preserves narrative structure and reasoning.

Software as Hypothesis

TacitFlow is a research instrument (Leinonen et al., 2008). It operationalizes concepts from knowledge management theory to allow empirical validation. The study tests: Can AI mediate the tacit-to-explicit conversion?

Workflow Integration

TacitFlow requires no separate login or forms. It integrates into existing work activities, capturing knowledge through passive listening or brief voice prompts.

Why SECI?

Nonaka and Takeuchi's The Knowledge-Creating Company (1995) is among the most cited works in management science. SECI has shaped knowledge management practice for three decades.

The Spiral Concept

SECI operates as a continuous spiral rather than a linear sequence. Knowledge cycles through all four modes repeatedly.

Academic Critiques

• Gourlay (2006): Externalization lacks operational definition. It's a metaphor without a mechanism.
• Tsoukas (2003): Tacit knowledge cannot be fully "converted". It remains context-dependent.
• Snowden (2002): SECI oversimplifies context, assuming universal applicability.

"We know more than we can tell."

— Michael Polanyi (1966)

Collins' Taxonomy (2010)

• Relational: Concealed by social dynamics.
• Somatic: Physically embodied skill.
• Collective: Distributed across a team.

Theoretical Limits

• Gourlay (2006): "Externalization" lacks operational definition.
• Tsoukas (2003): Context is inseparable from tacit knowledge.
• Von Hippel (1994): "Sticky information" resists transfer.

Abductive Reasoning

Peirce (1903): The process of forming explanatory hypotheses. Unlike deduction (proving) or induction (generalizing), abduction infers the best explanation for a specific situation.

Intuition Pumps

Dennett (2013): Thought experiments designed to elicit intuitions. They bypass the Polanyi Paradox by asking "what would you do if...?" rather than "what do you know?".

Key References

• Polanyi (1966): The Tacit Dimension.
• Gourlay (2006): "Conceptualizing Knowledge Creation."
• Busch (2008): "Tacit Knowledge in Organizational Learning."

The Socratic Method

Named after Socrates (470-399 BCE), the method uses guided questioning rather than direct instruction. The teacher asks probing questions that expose contradictions, forcing the learner to refine their understanding. TacitFlow's AI probes follow this tradition: asking questions that help experts articulate what they know.

Iterative Refinement

DBR's iterative cycles distinguish it from traditional experiments. Each TacitFlow iteration generates usage data that informs the next design phase, embodying DBR's commitment to theory-practice integration.

Cognitive Load Theory

Sweller (1988): Working memory has strict limits. TacitFlow's voice interface minimizes extraneous load (typing/navigating) to preserve germane load (reflection/articulation).

Recognition-Primed Decision

Klein (1993): Experienced practitioners recognize patterns rather than deliberating options. TacitFlow preserves these pattern libraries by capturing the cues experts notice.

Complex Thinking

Morin (2008): Complex phenomena require multiple perspectives. TacitFlow synthesizes pragmatism, process philosophy, organizational learning, and cognitive science.

Deep Smarts

Leonard (2005): Expert intuition built through years of experience. Unlike explicit knowledge in manuals, deep smarts are transferred through guided experience and Socratic dialogue.

Psychological Safety

Edmondson (1999): Essential for sharing. Experts must feel safe revealing their "tricks" without fear of judgment. TacitFlow addresses this through anonymous draft submission and mentor-mediated validation.

Mental Models

Internal representations (e.g., "suspicious behavior"). TacitFlow captures mental models through structured articulation and makes them visible for peer validation.

Avoiding Competency Traps

March (1991): Organizations that over-exploit existing knowledge fall into "competency traps." EASCI balances exploitation (Consolidation) with exploration (Innovation).

Process Philosophy

Whitehead: Actual Entities are "drops of experience." Concrescence is the integration of data into a new unity. In TacitFlow, each Knowledge Object is a concrescence of prior data, perspective, and AI structuring.

Collins' Taxonomy (2010)

Relational: Concealed by social dynamics.
Somatic: Embodied.
Collective: Social.
TacitFlow targets relational and somatic knowledge through conversational elicitation.

Experiential Learning

Kolb (1984): Concrete Experience → Reflective Observation → Abstract Conceptualization → Active Experimentation. EASCI maps directly onto this cycle.

Miller's Law (7±2)

Working memory holds ~7 chunks. EASCI's structured stages respect this limit by processing knowledge incrementally.

Curse of Expertise

Hinds (1999): Experts cannot "unlearn" expertise to imagine a beginner's perspective. TacitFlow's AI probes address this by asking the questions novices would ask.

Theoretical Backbone

This framework is the theoretical backbone that TacitFlow operationalizes. Each stage maps to a typed artifact, a quality gate, and a deliberately bounded AI role.

Seven Foundations

The framework maps grounded cognition, cognitive load theory, abductive reasoning, sensemaking, transactive memory, process philosophy, and organizational learning to specific stages.

Field Example

Pragmatist Standard

EASCI does not claim a perfect translation of tacit states. It asks whether an artifact is practically adequate for organizational use, with adequacy judged socially at Consolidation.

Machine-Checked Invariants

The Lean report formalizes structural properties such as provenance acyclicity, gate monotonicity, series-system composition, and CCVE taxonomy embeddings, while explicitly tracking remaining proof debt.

The Pragmatist Bypass

EASCI bypasses the “can tacit knowledge be fully converted?” impasse by asking whether a partial artifact is good enough to guide future inquiry and action.

Tsoukas Still Matters

Tsoukas's objection survives: some subsidiary awareness cannot become focal without changing its character. EASCI responds by limiting the claim, not by denying the problem.

How the New Governance Changes This

Quality gates, counter-hypothesis records, and PLE make “adequacy” inspectable rather than rhetorical. The artifact's warrant is procedural and social.

CCV and CCVE

The Lean report represents Collins-style knowledge composition as a simplex. CCVE adds the explicit fraction produced by successful externalization.

Why Formalize a Taxonomy?

Formalization prevents hand-waving across categories. If a mapping preserves simplex structure, the framework can compare knowledge-type mixtures without pretending they are identical.

Design Consequence

TacitFlow should ask “what kind of tacit knowledge is this?” before selecting prompts, gates, reviewers, or reuse boundaries.

Micro-Ba

Nonaka's Ba assumes sustained shared context. EASCI's micro-Ba is a short, prompted reflective space created in the field when co-presence is unavailable.

Why Voice Survives the Update

The newer ECCE framing strengthens the voice argument: speech reduces dual-task load and preserves paralinguistic cues for Consolidation.

Gate Implication

G1 is not a bureaucratic form. It protects the context needed for later Articulation, Structuring, and reuse.

Psychological Safety

The interview findings still matter: if practitioners fear blame, surveillance, or loss of credit, they will not contribute useful tacit knowledge.

Trust Barrier

Attribution through PROV-O helps address a social dilemma: contributors can see how their knowledge is credited, reused, and reviewed.

Regulatory Link

Social review and dissent records support contestability: reviewers can see which alternatives were considered and why one interpretation was accepted.

Zombie Facts

A validated KO can become harmful when conditions change. Innovation prevents outdated knowledge from retaining authority through retrieval alone.

PLE-5

Evolution records how knowledge changes: review triggers, retirement events, and supersession links become part of the explanation.

Lean Connection

The formal model treats lifecycle governance as structure, not metaphor: provenance ordering, acyclicity, and gate monotonicity are properties to preserve.

Feedback Architecture

The ECCE paper specifies eleven feedback paths. Single-loop paths correct execution parameters; double-loop paths revise organizational norms; deep paths propagate Innovation-stage insights.

Quality Gates

G1-G3 certify epistemic quality; G4-G5 certify social validation and lifecycle governance. A validated artifact has passed the whole series, not just a model confidence threshold.

Process-Level Explainability

PLE records how an artifact was produced, challenged, validated, reused, and updated. This complements model-level XAI by explaining organizational knowledge production.

Formal Status

The Lean 4 report mechanizes key structural properties and uses scaled-integer arithmetic for decidable checks, while distinguishing completed kernel-checked proofs from tracked proof obligations.

Why Five Stages?

Three stages decompose SECI externalization; Experience and Innovation supply the contextual and temporal governance SECI leaves implicit.

Situated Learning

Brown & Duguid (1991) argue that canonical descriptions of work often diverge from actual practice. Real learning happens through legitimate peripheral participation within communities of practice.

Embodied Cognition

Merleau-Ponty (1945) argued knowledge is grounded in bodily experience. TacitFlow's Experience stage captures pre-reflective patterns through behavioral observation.

Maurice Merleau-Ponty (1908-1961)

French phenomenologist who established the body as the primary site of knowing. Against Cartesian dualism, he argued that perception is active embodied engagement. TacitFlow captures this through contextual metadata.

Phenomenology

Study of conscious experience from a first-person perspective (Husserl). It treats subjective experience as valid data, not bias to be eliminated.

Cartesian Dualism

Descartes' separation of mind and body. Merleau-Ponty's critique grounds tacit knowledge theory: knowing involves the whole embodied person.

Abductive Mapping

Peirce distinguished abduction (guessing best explanation) from deduction and induction. Structuring uses abduction to generate hypotheses from sparse evidence.

Exploration vs. Exploitation

March (1991): Organizations must balance efficiency (exploitation) with innovation (exploration). EASCI structures this tension.

Capture Phase in Practice

Communities of Practice

Lave & Wenger (1991): Knowledge transfer occurs through "legitimate peripheral participation". TacitFlow uses mentor-novice pairing.

Grounded Cognition

Barsalou (2008): Conceptual knowledge involves modal simulations. Expert judgment relies on "feeling" if a situation matches prior patterns.

Cognitive Apprenticeship

Collins et al. (1989): Modeling, coaching, scaffolding, and fading. AI probes function as scaffolding that fades as reasoning improves.

Reflective Practice Feedback Loop

Schön (1983): "Reflection-in-action". Innovation reveals gaps, prompting refinement of Articulation methods.

Pattern Recognition Feedback Loop

Simon (1996): Experts recognize patterns. Innovation helps identify recurring structures, improving Structuring.

Legitimate Peripheral Participation

Newcomers learn by participating at the edges of practice. TacitFlow operationalizes this through guided observation.

Bounded Rationality

Simon (1957): Humans use heuristics due to limited processing power. KOs provide pre-organized knowledge to reduce search costs.

Smart Forgetting Protocol

Innovation requires unlearning. This protocol ensures outdated intelligence (e.g., disproven hypotheses) is actively pruned to prevent "zombie facts."

Social Verification as Quality Gate

Von Krogh et al. (2000): Knowledge creation requires "enabling conditions" like trust and empathy. TacitFlow's mentor circles operationalize this, surfacing tacit disagreements to prevent hallucination loops.

Double-Loop Learning

Argyris & Schön (1978): Unlike single-loop learning (correcting errors), double-loop learning questions governing assumptions. It supports revising protocols when standard methods fail.

Social Learning Theory

Bandura (1977): Learning occurs through observation and modeling. TacitFlow's mentor circles create observational learning opportunities.

Observational Learning

Learning by watching others. Components: attention, retention, reproduction, motivation. TacitFlow makes validated expert KOs visible to learners.

Validate Phase in Practice

Embodied Knowledge Feedback Loop

Merleau-Ponty (1945): Structuring creates cognitive schemas that become the pre-reflective lens for future Experience.

Organizational Memory Feedback Loop

Walsh & Ungson (1991): Consolidated KOs become the institutional vocabulary that shapes how practitioners articulate new experiences.

Double-Loop Learning Feedback Loop

Innovation back to Experience reframes the entire knowledge capture process, creating fundamental shifts in practice.

Stable Tacit Practices

Practices are not static but actively performed. Consolidation creates "relative stability" where knowledge is routinized yet subject to re-enactment.

Destabilization Events

External shocks or anomalies force a shift from unreflective habit to active sensemaking (Dewey's "problematic situation").

Metacognition

Flavell (1979): "Thinking about thinking." TacitFlow's reflective probes develop awareness of one's own cognitive processes.

Lures for Feeling

Whitehead: Validated KOs become "lures" that shape how practitioners perceive future situations.

Resilience Engineering

Hollnagel (2011): Innovation enables adaptive capacity by updating mental models based on accumulated experience.

Self-Determination Theory

Deci & Ryan (2000): Intrinsic motivation depends on autonomy, competence, and relatedness. TacitFlow's voice-first design supports personal agency (choosing what to share) rather than forcing structured input.

Self-Efficacy

Bandura (1977): Belief in one's ability to succeed. TacitFlow builds efficacy through structured success experiences and visible attribution.

Paralinguistic Information

Voice captures hesitation, emphasis, and tone: metadata for tacit judgment. Polanyi (1966) noted experts often "feel" rightness before they can explain it. Audio preserves these signals; text discards them.

Contextual Metadata

Data about data capturing creation circumstances. TacitFlow captures:

• Temporal: Timestamp, duration.
• Spatial: GPS, zone.
• Social: Presence, supervisor.
• Operational: Case #, incident type.
• Physiological: Heart rate (stress).

Situated Interaction

Brown, Collins & Duguid (1989): Knowledge is inseparable from the activity and context in which it develops.

Subtle Cues

Audio captures confidence or uncertainty. Experts often "feel" rightness before they can explain it.

Knowledge Object Structure

Each KO contains:

1. Claim: the asserted knowledge statement
2. Evidence: supporting sources and observations
3. Confidence: weighted score based on evidence quality
4. Provenance: full derivation chain per W3C PROV-O
5. Metadata: timestamps, classification labels, contributor IDs
6. Relations: typed links to other KOs in the graph

PROV-O Vocabulary

PROV-O (W3C, 2013) is the OWL representation of PROV-DM. TacitFlow serializes all KO provenance as PROV-O-compliant RDF, enabling interoperability with any PROV-aware system.

From "Sticky" to Traceable

Von Hippel (1994) identified tacit knowledge as "sticky": costly to transfer because context is lost. PROV-DM embeds context (situational metadata, derivation history) directly into the knowledge structure.

Addressing the Trust Barrier

Reluctance to share often stems from lack of attribution. PROV-DM's wasAttributedTo ensures contributors receive explicit credit, transforming sharing into a documented contribution.

Ontology (Information Science)

A formal specification of a conceptualization. Unlike a taxonomy, ontologies capture complex relationships (e.g., "an officer can supervise multiple incidents").

Taxonomy vs. Ontology

Taxonomy: Tree structure (is-a). Dog is an animal.
Ontology: Graph structure (arbitrary relations). Dog is owned-by Person.

The Provenance Chain

Creation Trail (who/when) → Derivation Trail (reasoning logic) → Access Trail (retrieval). All trails are W3C PROV-O compliant.

Audit Trail Architecture

TacitFlow maintains three audit layers ensuring full accountability and traceability for every piece of knowledge in the system.

The Tacit Knowledge Gap

The framework is designed for experience-dependent, partially ineffable expertise where peer review is feasible and knowledge holders are accessible while reasoning is still fresh.

Series-System Logic

The formal report models gate composition as a series system: the artifact advances only if every required gate succeeds.

Gate Diagnostics

A failed gate reports which condition failed and what remediation is possible, turning governance into a cognitive scaffold rather than an opaque rejection.

Why Gates Matter

Without gates, GenAI can turn unvalidated fragments into authoritative-looking organizational memory. EASCI blocks that escalation by design.

Nonaka's SECI Model

EASCI keeps SECI's insight that externalization matters, but replaces the single phase with staged artifacts, boundaries, gates, and feedback paths.

Smart Forgetting

Innovation requires not just learning, but unlearning. Kluge & Gronau (2018) showed that organizational knowledge must be actively pruned. "Smart Forgetting" invalidates outdated KOs to prevent "zombie facts."

Smart Forgetting & Creative Advance

Whitehead's (1929) "creative advance into novelty" reframes this: the settled past (validated KOs) becomes the platform for future innovation. The Innovation stage retires obsolete knowledge while generating new experiential context.

Complexity at the Edge

Stacey's Complex Responsive Processes (2000) and Kauffman's At Home in the Universe (1995) explain why the feedback loops produce non-linear, emergent outcomes. The compounding effect emerges from self-organizing interactions across EASCI stages.

Edge of Chaos

A metaphor from complexity science describing the boundary zone between order and randomness where complex systems exhibit maximum adaptability and creativity. Effective organizations operate at this productive boundary.

Transactive Memory System

Wegner (1995): Organizations remember collectively through a distributed cognitive network where members know "who knows what." TacitFlow operationalizes TMS by explicitly mapping expertise networks.

GDPR Compliance

GDPR Art. 17: The Innovation phase includes data retirement protocols. When KOs are superseded, the system maintains audit trails while pruning operational data per Art. 17 (Right to Erasure) requirements.

Micro vs Macro Loop

Micro-Loop

Session-level inquiry: evidence foraging, hypothesis challenge, provenance export.

Macro-Loop

Strategic knowledge governance (EASCI).

GraphRAG (Edge et al., 2024)

Connecting LLMs to knowledge graphs produces more accurate, traceable responses than document-based RAG alone. The graph structure enables reasoning over relationships, not just retrieving text chunks.

Devil's Advocate Pattern

The agent argues against the investigator's current hypothesis using evidence retrieved from the graph, then requires a documented disposition rather than passive acceptance.

Bayesian Weights

The ICAIL scenario keeps H1 and H2 weights visible as evidence arrives, showing non-monotonic movement rather than a one-way march to confirmation.

Socratic Challenge

Questions target missing evidence, unexamined assumptions, and internal contradictions. The investigator must articulate why one hypothesis should survive.

Complexity & Emergence

Kauffman (1995) showed complex systems self-organize at the "edge of chaos." The Micro Loop's cyclic process mirrors this: emergent intelligence arises from constrained iteration, not linear prediction.

Heuristics

Heuristics (availability, representativeness) enable fast decisions but introduce bias. TacitFlow's structured KOs provide reliable anchors to correct these biases (Kahneman, 2011).

Memory Architecture

LLMs have ephemeral context windows (128k–200k tokens). TacitFlow's Knowledge Graph persists indefinitely, transforming the assistant from a "stateless oracle" to a "learning partner."

Token Prediction vs. Reasoning

Standard LLMs predict the next token based on statistics. TacitFlow forces a different process: retrieve evidence, reason over it, then validate.

Multi-Hop Reasoning

Answers questions requiring multiple connected facts (e.g., "Who trained the officer who handled Case X?"). Vector search cannot traverse these relational chains; GraphRAG can.

Semantic vs. Keyword Search

Keyword: exact matches. Semantic: conceptual similarity ("routine patrol" $\approx$ "standard rounds"). TacitFlow combines both.

Dense Retrieval

Uses neural networks to encode queries and documents into dense vectors. Similarity is computed via cosine distance, capturing semantic relationships unlike sparse methods (BM25).

Theoretical Foundations

• Dewey: Knowledge is dynamic transformation ("situated inquiry").
• Dennett: "Intuition pumps" force externalization of tacit assumptions.
• Peirce: Abduction generates hypotheses from sparse evidence.
• Weick: Knowledge is "enacted" through social narrative.
• Whitehead: "Creative advance" - past KOs become data for new models.

Workplace Learning

Eraut (2004): Most learning is informal. The "Experience" stage captures authentic practice rather than formal documentation.

Social Knowledge

Tacit transfer depends on trust (Leonard & Sensiper). Mentor circles and verification address sharing barriers (Cabrera & Cabrera).

Integration of Theory

Integrates three theoretical components: (1) EASCI stages as macro-level knowledge lifecycle, (2) micro-loop inference cycle (Boyd, 1987), and (3) W3C PROV-O for provenance tracking.

Demo Instructions

Click "Run Simulation" to see a knowledge capture scenario. Use step controls (⏮ ⏭) for pedagogical walk-through. The visualization shows stage transitions, KO validation, and feedback loops.

Simulation Scenario

A practitioner notices an anomaly. The system captures context, elicits a voice fragment, structures it into a provisional KO with a counter-hypothesis, seeks peer validation, and records lifecycle triggers.

Production Telemetry

TacitFlow tracks retrieval latency, confidence indicators, counter-hypothesis state, and provenance node count. Three trace panels show GraphRAG data flow, challenge state, and PROV-O chains.

Retrieval Latency

Time from query to retrieval. Must be <200ms for voice. Optimized via ANN indexing and caching. High latency indicates graph growth or query complexity.

Confidence Scores

Based on embedding similarity, provenance completeness, peer validation, and recency. <70% triggers retrieval; <50% triggers expert review.

Real-Time Confidence

Confidence scores update live. Low confidence (<70%) triggers additional evidence retrieval. Very low confidence (<50%) flags the KO for manual expert review. This prevents premature consolidation of uncertain knowledge.

Real-Time Telemetry

The telemetry panel shows: PROV nodes (provenance chain length), KO count (Knowledge Objects referenced), Confidence % (weighted evidence score), Token count (LLM resource usage).

Production Metrics

Tracks Capture latency (time from observation to KO creation), Articulation completeness (AI probe iteration count), Validation rates (peer acceptance %), and Decay ratios (KOs archived vs. promoted).

Why Visualization Matters

Tufte (2001) emphasizes that complex processes become comprehensible when represented visually with appropriate detail. This simulation shows data at multiple levels simultaneously: individual reasoning steps (micro), stage transitions (macro), and system-wide metrics (telemetry).

Small Multiples

The three trace panels (GraphRAG, challenge, PROV-O) follow Tufte's "small multiples" principle (Tufte, 2001): identical visual structures showing different data streams allow direct comparison and pattern recognition across the inference stages.

Software as Hypothesis

Leinonen et al. (2008): The prototype is a research instrument for testing theoretical assumptions, not a commercial product. Features are hypotheses about what enables tacit knowledge transfer.

Cognitive Apprenticeship

Collins et al. (1989): Modeling, coaching, scaffolding, fading. TacitFlow's AI probes function as scaffolding that "fades" as analysts internalize reasoning patterns.

The Prototype

Unlike chatbots that predict tokens, TacitFlow structures claims with evidence. Every output follows W3C PROV-O provenance standards, making reasoning auditable.

KO Example (Simplified JSON-LD)

{
  "@context": "https://w3id.org/ko/v1",
  "@type": "KnowledgeObject",
  "id": "ko:uuid-...",
  "claim": "Suspect A linked to Van B",
  "evidence": ["ev:log-001", "ev:cam-02"],
  "confidence": 0.85,
  "author": "agent:lepik"
}

KOs encode claims with evidence following W3C PROV-O for legal chain-of-custody.

EU AI Act Compliance

The source papers map EASCI controls to EU AI Act human oversight, logging, risk management, and contestability obligations, plus LED fact/assessment separation and logging duties.

Human Agency

The system supports, not replaces, human judgment. AI suggestions are candidates; practitioners author claims and reviewers validate them.

Bias Mitigation

The key mitigation is not simply RAG. The counter-hypothesis forcing function makes premature closure harder than adversarial scrutiny.

Data Sovereignty

Air-Gapped Deployment: To ensure absolute data sovereignty and prevent leakage, the architecture is designed for on-premise operation, disconnected from public cloud providers.