Rebuilding the Information Rails of Capital Markets

Evidence-First Knowledge Systems for the Age of AI

Executive Summary

Modern financial research operates atop brittle information plumbing. Analysts possess unprecedented access to data yet remain unable to recall, verify, or connect what they already know. The problem is not scarcity of information but the absence of a computable memory — a substrate where evidence, context, and time coexist in a single, auditable fabric.

This paper argues for a new foundation: evidence-first knowledge systems. These systems unite the precision of search with the interpretive depth of language models, governed by immutable provenance and contractual data control. They do not replace human judgment; they amplify it by ensuring that every claim can be traced, every fact is temporally indexed, and every synthesis remains reproducible.

We outline five design primitives essential to this transformation:

1. Persistent research memory that remembers everything a firm has read.
2. Verifiable citation graphs that link facts to their exact sources.
3. Temporal intelligence that knows what is current, historical, or superseded.
4. Authority-weighted reasoning that respects the hierarchy of disclosures.
5. Governed interoperability that keeps private data sovereign yet portable.

Together, these elements describe the information rails of capital markets — a shared, evidence-anchored graph that connects analysts, investors, and companies without compromising privacy or trust. ValuWiki represents one early instantiation of this philosophy: a working model of how memory, reasoning, and governance can coexist inside a unified research substrate.

The long-term vision extends beyond any single product. When every insight carries its own evidence trail, knowledge itself becomes infrastructure — transparent, compounding, and built for a more auditable market future.

I. The Epistemic Crisis of Financial Research

The productivity of financial analysts is constrained not by a lack of information, but by the inability to recall and audit it. Three structural frictions define the present state:

1. Discovery Friction

Crucial facts are buried in filings, transcripts, and reports. Search tools built for text retrieval — not conceptual reasoning — return keyword noise. The result: 30% of research time lost to rediscovery.

2. Accountability Friction

Outputs that lack traceable provenance cannot be defended or audited. Narratives float free from source, introducing compliance risk and eroding confidence in AI-assisted workflows.

3. Insight Decay

Knowledge is scattered across PDFs, emails, notes, and LLM sessions. Insights fade because there is no persistent, queryable memory linking what has been read, concluded, and superseded.

Each friction compounds the others. Without precision recall, analysts re-derive insights; without provenance, they cannot defend them; without memory, the cycle repeats.

II. The Case for Evidence-First AI

Large language models have expanded analytic fluency but severed the link between generation and grounding. They produce plausible text rather than provable reasoning. In high-stakes domains such as finance, this epistemic gap is unacceptable.

An evidence-first system reverses the logic of conventional AI. Instead of starting from language and hoping to attach evidence later, it starts from verified evidence and builds language as a view layer over structured knowledge.

Core Principles

1. Evidence Fidelity — Every assertion must trace to a primary source, preserving quote, context, and location.
2. Temporal Intelligence — Systems must understand what is current, what is historical, and what has been superseded.
3. Authority Hierarchy — Disclosures in audited filings outweigh analyst commentary or media speculation.
4. Multi-Modal Understanding — Tables, charts, and text are interdependent signals; comprehension requires synthesis across them.
5. Explainability — Each output must show why it was returned, not merely what was returned.

These principles define the design ethics of AI in financial research: fidelity over fluency, auditability over allure.

III. Design Primitives for Next-Generation Research Systems

From first principles, an evidence-first research system must integrate five capabilities:

1. Persistent Research Memory

A continuously expanding corpus that remembers every document, note, and excerpt a researcher has processed. Unlike session-based LLMs, this memory is long-lived, queryable, and owned by the user.

2. Verifiable Citation Graph

Every fact becomes a node; every citation, an edge. This transforms static text into a graph of evidence — where reasoning paths can be traversed, audited, and replicated.

3. Temporal Indexing

Facts carry timestamps and lineage. The system knows when guidance becomes actuals, when numbers are restated, and how narratives evolve.

4. Authority-Weighted Synthesis

When conflicts arise, precedence rules apply:

10-K filings → Transcripts → Analyst reports → News → Personal notes.

Interpretation always begins with the most credible source.

5. Multi-Modal Comprehension

Financial reasoning depends on integration of numbers, visuals, and prose. The system must treat tables and charts as equal citizens in semantic space — extracting quantitative facts and linking them to textual claims.

Together, these primitives form the epistemic substrate for compounding knowledge.

IV. Architecture of the Evidence Graph

The envisioned infrastructure can be expressed as a layered stack — an evidence graph that mirrors how humans reason about markets.

1. Input Layer — Capture

Filings, transcripts, web captures, research notes, and datasets enter through direct upload, API sync, or browser integration. Each input is parsed into structured fragments while preserving source integrity.

2. Processing Layer — Comprehension

Natural-language, tabular, and visual inputs are harmonized into unified representations. Entities, relationships, and metrics are extracted, timestamped, and linked to document context.

3. Knowledge Graph Layer — Memory

Facts become addressable nodes within a versioned, temporal graph. The graph records what was known, when it was known, and from where.

4. Query Layer — Reasoning

Users or AI agents pose questions. Retrieval operates across the graph, assembling citations, comparing time periods, and ranking sources by authority and freshness.

5. Governance Layer — Trust

Access, deletion, export, and provenance verification are enforced at this layer. Tenancy isolation guarantees that evidence scoped to one institution never contaminates another. Opt-in model training and auditable data flows preserve contractual control.

Together, these layers describe a computable memory for markets — a substrate where evidence itself becomes infrastructure.

V. Governance as Design Ethic

In evidence-first systems, governance is not an afterthought; it is a computational property. Trust emerges not from promises, but from design constraints.

Key Commitments

• Tenant Isolation: Data confined to its originator's domain.
• No Training Without Consent: Documents never used to improve models unless explicitly permitted.
• Full Portability: Users can export, audit, or delete their data at any time.
• Provenance Immutability: Source trails cannot be retroactively altered.
• Private by Default: All processing occurs within secure tenancy boundaries.

These commitments make evidence-first AI legally compliant by architecture, not by policy.

V-A. The Technical Foundation: Implementing Evidence-First Systems

Evidence-first architectures require specialized infrastructure beyond traditional RAG pipelines:

Knowledge Graphs as Core Intelligence Layer

Generic vector stores cannot answer "Which companies are affected by chip shortage?"—no single document contains this answer. A knowledge graph connecting entities (Apple → TSMC → Taiwan → Geopolitical Risk) enables:

• Cross-document pattern discovery
• Temporal fact tracking (guidance → actuals)
• Proactive alert generation when new filings affect existing relationships

This is not theoretical: production systems using graph databases now handle billions of entity relationships, enabling real-time pattern detection across tens of thousands of documents.

Web-Scale Research Infrastructure

Modern equity research spans SEC filings, earnings transcripts, news, and live market data. Recent advances in AI-native web search have made this tractable:

• Deep research APIs: Purpose-built search tools for AI agents now deliver 47% accuracy vs GPT-4's native browsing capability, with predictable per-query pricing¹
• Structured financial data: Normalized APIs provide programmatic access to regulatory filings, earnings data, and corporate disclosures
• Continuous learning pipelines: Asynchronous processing ensures every new document updates the knowledge graph without blocking user queries

This infrastructure allows systems to answer questions requiring synthesis across thousands of sources—without the latency or cost of loading them all in real-time.

VI. From Research Memory to Market Infrastructure

When each firm maintains its own verifiable research memory, and those memories can interoperate through standardized provenance protocols, a new infrastructure emerges: an interconnected evidence graph spanning the sell-side, buy-side, and corporates.

Such a network would:

• Collapse redundant discovery across institutions,
• Enable regulators to audit claims without data transfer, and
• Allow AI agents to reason transparently over shared, cited knowledge.

This is the Information Rail of Capital Markets — a system where insight moves with evidence attached, where research compounds instead of decaying, and where AI participates as a governed interpreter, not a hallucinating oracle.

Interoperability Through Standard Infrastructure

The evidence graph becomes truly powerful when integrated with:

• Public company data: Standardized APIs for regulatory filings and financial disclosures
• Real-time web intelligence: Deep research APIs for market-moving events and external validation
• Cross-institutional knowledge graphs: Standardized provenance protocols enabling interoperability

This allows a sell-side analyst's research memory to cite the same authoritative source as buy-side fund managers—reducing redundant discovery and enabling regulators to audit claims without data transfer. This isn't centralization—it's standardized provenance protocols allowing decentralized research memories to interoperate while maintaining tenant isolation and data sovereignty.

VII. Why This Matters

Every industrial revolution begins with a new substrate for knowledge. Accounting created the ledger. The internet created the hyperlink. The next leap will come from computable evidence — machine-readable, human-auditable, universally referenceable.

Building that substrate is both a technical and civic challenge. It requires aligning incentives across institutions, codifying provenance as data, and designing systems that can remember responsibly.

Conclusion

Financial knowledge compounds only when evidence persists. By formalizing how information is captured, contextualized, and cited, we can transform research from a sequence of documents into a continuum of understanding.

Evidence-first knowledge systems are the foundation of that future. They turn ephemeral analysis into enduring infrastructure — a living graph of what the market knows, believes, and can prove.

ValuWiki represents one early instantiation of this paradigm: a proof-of-concept for how memory, reasoning, and governance can coexist inside a single research substrate. The broader project, however, belongs to the industry as a whole — to those committed to building the information rails of capital markets.

References