CODA Requirement Intelligence Engine

CODA Requirement Intelligence

CODA Requirement Intelligence Engine

Turning operational signals into governed software execution.

CODA Requirement Intelligence turns operational signals into governed software execution.

From logs, grievances, policies, documents, requirements, and codebase evidence into classified control gaps, correlated candidates, implementation reality checks, and human-confirmed engineering decisions.

1.7M+Logs observed

177Requirements indexed

5Source reservoirs

25Pilot candidates

0Automatic mutations

YesCorrelation enabled

Current environment snapshot — includes live requirement and candidate batch counts from this environment. · File-backed candidate batches — not production authorization. · Safe fallback metrics; no full log scan on page load.

Operational signals become invisible engineering debt

Operational complexity and compliance pressure are rising while engineering capacity is flat. Organizations that cannot reconcile signals, requirements, and code will accumulate invisible control debt — until audit or incident exposes it.

Logs pile up

Millions of operational events hide recurring control and workflow failures.

Grievances reveal control failures

HR and compliance issues describe system gaps — not language problems to sanitize.

Policies drift from code

Policy obligations rarely map cleanly into engineering backlogs.

Documents stay outside delivery

Procedures and finance docs hold controls that never become testable requirements.

Requirements get dirty

Backlog tables go stale; weak acceptance criteria look planning-ready.

Codebase truth is hard

Partial implementation is invisible if you only read the requirement row.

Why normal requirement systems fail

Before CODA

Signals scattered across logs, HR, policy, and docs
Requirements table trusted as implementation proof
Humans manually invent control recommendations
No correlation across sources

After CODA

Universal SourceSignal with classification and risk
Codebase is implementation truth
Automated diagnosis with guided confirmation
Evidence-backed audit and reconciliation

Ticket tools capture tasks, not operational truth across sources.
Requirement tables become stale the moment code moves faster than documentation.
Incidents, grievances, and policies rarely map cleanly into engineering work items.
Code may already implement controls the requirement table never describes.
Humans cannot manually inspect millions of signals for control patterns.

CODA breakthrough: one operating intelligence layer

CODA's moat is a universal operating intelligence layer: new sources become adapters, not new engines. Cross-source correlation and codebase reality checking sit behind an evidence-only governance posture.

Sources→ SourceSignal→ Classification + Risk→ Cross-source Correlation→ Control Gap Diagnosis→ Requirement Match→ Codebase Reality Check→ Guided Human Confirmation→ Evidence / Audit / Reconciliation

Example: “Employee may have approved payment improperly” → independent approval workflow, separation of duties, audit logging, exception review — not “employees should not steal money.”

Proof of scale

Current environment snapshot — safe fallbacks when live counts are unavailable.

1.7M+Logs observed

177Requirements indexed

5Source reservoirs

25Pilot candidates

0Automatic mutations

YesCorrelation enabled

Current environment snapshot — includes live requirement and candidate batch counts from this environment.
File-backed candidate batches — not production authorization.
Safe fallback metrics; no full log scan on page load.

Source reservoirs

New sources become adapters, not new intelligence engines.

Logs Live

Functional and operational patterns at scale.

Grievances Live

Control failures → system-control recommendations.

Policies Live

Obligations and approval language.

Documents Live

Procedure and requirement-like content.

Requirements Live

Indexed backlog — intent, not proof of implementation.

Google Drive / Box Future

Future adapters only — not a new intelligence engine.

SourceSignal architecture

Source

Origin of the signal — log, grievance, policy, document, requirement.

Classification

Interpretation — control gap type, risk domain, sensitivity.

Monitoring

Pattern tracking across time and routes.

Correlation

Relationships across sources — one cluster, one control story.

Decision

Confidence-scored recommendation — not approval.

Human review

Confirmation of high-impact decisions — not manual re-classification.

Multi-source correlation

Related signals across reservoirs converge on one control story — not duplicate tickets.

Policy Reimbursement requires approval

Grievance Payment control concern

Log Approval route fails repeatedly

Requirement Weak acceptance criteria

Document Finance procedure — manual approval

Codebase Partial workflow exists

Financial approval control gap

One cluster · one diagnosis surface

Control gap diagnosis

Automated diagnosis complete — humans confirm high-impact decisions.

87% Guided confirmation Financial-control confirmation required No strong requirement match Partial codebase implementation

Problem categoryPayment control concern

Likely control gapFinancial approval control gap

Confidence band87% — Guided confirmation

Criteria matched

Payment/control pattern detected
Grievance source with financial-control relevance
No strong requirement match found
Partial finance workflow in codebase
Missing separation-of-duties and audit-trail evidence

Suggested controls

Independent approval workflow
Maker-checker / separation of duties
Approval history log
Exception monitoring
Policy acknowledgment / reminders

Requirement matchNo strong match — closest weak matches listed for staff.

Codebase realityPartial implementation — approval view found; SoD tests missing.

Missing controls

Separation of duties enforcement
Audit trail tests
Exception monitoring

Human confirmation

System recommendation needs confirmation because this is a financial-control signal.

Codebase reality check

Requirements describe intent. The codebase reveals reality.

Code evidence is not human acceptance. The requirement row is not final proof of implementation.

Requirement intent

Independent payment approval should exist with separation of duties.

Codebase evidence found

finance payment model
approval view
reimbursement route

Missing evidence

Separation-of-duties enforcement
Audit trail test
Exception monitoring

Intent layer

What the requirement says should exist.

Implementation evidence

Models, views, services, templates, tests in repo.

Reality status

Partial / likely implemented / missing controls / needs tests.

Missing controls

Suggested controls not evidenced in code.

Next action

Improve requirement, tighten, attach evidence, or audit.

Confidence and decision thresholds

Score	Band	Meaning
90–100	Auto-ready recommendation	Still not approval. Low/medium risk only.
80–89	Guided confirmation	Strong diagnosis; sensitive domains still need explicit confirmation.
60–79	Human review required	Ambiguous requirement or codebase evidence.
Below 60	Defer / insufficient evidence	Gather more context before backlog action.

Financial, HR/grievance, security, and compliance signals always require confirmation before persistence or draft creation — even at high confidence.

Guided human confirmation

CODA performs the diagnosis. Staff confirm, link, defer, dismiss, or use explicit promote for drafts.

Human review means confirmation of the system's diagnosis, not manual re-classification.

Confirm recommendation

Accept the system recommendation after reviewing evidence.

Link existing requirement

Connect the signal to an existing requirement when the match is clear.

Attach evidence

Persist source evidence without changing requirement status.

Create draft requirement

Only through explicit promote flow — never silent creation.

Defer to owner

Send to the correct owner when evidence is incomplete.

Dismiss not actionable

Mark as not actionable without deleting source data.

Governance and safety posture

No silent automation. No automatic approvals. High-impact decisions stay human-confirmed with a full audit trail.

Evidence-only by default

RequirementPrompt artifacts — no automatic field mutation.

No automatic approval

GQI, Prompt Packs, and Control Cycle do not authorize production.

Production pilot guarded

Feature flags and staff-only routes — not autonomous rollout.

Sensitive source redaction

Pilot settings can redact HR/grievance text in reports.

Audit trail preserved

Evidence history and reconciliation decisions are retained.

governance_state_change: forbidden production_activation: blocked approval_gate: NOT_IMPLEMENTED

Core platform capabilities

Source Observatory

SourceSignal Layer

Multi-source Classification

Cross-source Correlation

Control Gap Diagnosis

Codebase Reality Check

Grouped Candidate Review

Evidence History

Implementation Audit

Reconciliation

GQI / Prompt Pack

Control Cycle

Live evidence gallery

Primary evidence is the working route — screenshots are optional for PDF/offline export.

Requirements Intelligence Hub

Command center for backlog health, intelligence links, and AI_SYSTEM tooling.

Open requirements hub /requirements/

What to look for

Hub cards linking to candidate review, observatory outputs, and staff tools.

Executive: One front door for requirement intelligence — not scattered admin pages.

Governance: Review and evidence actions do not approve requirements or authorize production.

Technical: Read-only hub; no automatic requirement field mutation.

Candidate Review Queue

File-backed candidate batches from Source Observatory and correlation.

Open candidate review /ai_services/source-candidates/

What to look for

Batch list, grouped view toggle, unresolved vs attached counts.

Executive: Staff see patterns grouped by topic — not hundreds of duplicate rows.

Governance: Review and evidence actions do not approve requirements or authorize production.

Technical: Batches stored under var/ai_system/source_candidates; review_state only.

Current Pilot Batch

Active staff pilot batch when available; otherwise start from the queue.

Open candidate review queue /ai_services/source-candidates/

What to look for

Grouped review cards, human-readable actions, pilot guardrails banner.

Executive: Controlled pilot scope — not autonomous production rollout.

Governance: Pilot feature flags; production_activation remains blocked.

Technical: Dynamic batch link when review store has batches.

Grouped Review & Control Diagnosis

Automated control gap diagnosis on group and candidate detail.

Open grouped review (select a batch) /ai_services/source-candidates/?view=groups

What to look for

Automated Control Diagnosis block, suggested system controls, live route to codebase reality.

Executive: System completes diagnosis; humans confirm — not manual classification.

Governance: Diagnosis is not proof; recommendation is not approval.

Technical: control_gap_diagnosis service; optional RequirementPrompt evidence on attach.

Implementation Audit & Reconciliation

Compare requirement intent to codebase evidence; staff reconciliation queue.

Open reconciliation queue /ai_services/requirements/implementation-audit/reconciliation/

What to look for

Audit status, matched paths, missing controls, reconciliation decisions.

Executive: Closes the loop between backlog claims and code reality.

Governance: Reconciliation records decisions — does not auto-change spec status.

Technical: implementation_audit scanner + optional RequirementPrompt persistence.

Requirement Detail & Evidence

Open a requirement to view evidence history, GQI, and prompt packs.

Sample requirement CODA0008296 /requirements/8296/

What to look for

Evidence history list, AI_SYSTEM artifacts, unchanged requirement fields after evidence attach.

Executive: Evidence-backed requirements — not documentation theater.

Governance: GQI and Prompt Packs score only; they do not approve governance.

Technical: RequirementPrompt evidence types include control gap diagnosis when attached.

Staff Presentation Center

AI-generated pitch center for staff (separate from this flagship page).

Open presentation center /portfolio/requirements/

What to look for

Pitch generation form — login required; does not mutate requirements.

Executive: Optional staff tool for generated narratives.

Governance: Review and evidence actions do not approve requirements or authorize production.

Technical: Preserves /portfolio/requirements/ from PR 19A.

This Flagship Presentation

Curated demo command center with live links (you are here).

Demo mode deck /portfolio/requirement-intelligence-platform/demo/

What to look for

Live evidence cards, presenter notes, demo path — links over screenshots.

Executive: Proves the system is alive without waiting for PNG captures.

Governance: Presentation does not run pipelines or authorize production.

Technical: Offline-safe curated content + optional live metrics.

These working routes become the controlled staff pilot path: every step produces evidence, but no step silently approves, mutates, or activates production behavior.

Staff production pilot workflow

Controlled staff pilot — not autonomous production.

1

Report-only Source Observatory

Reads selected source reservoirs; produces JSON/report.

No database mutation on requirements or logs.

2

Source Correlation

Clusters related signals across sources.

Evidence optional; no auto-promote.

3

Grouped Candidate Review

Staff review grouped by requirement, topic, or control pattern.

Defer/dismiss updates review_state only.

4

Control Gap Diagnosis

Automated diagnosis with suggested system controls.

Diagnosis is not proof or approval.

5

Selective Evidence Attachment

Attach to existing requirements via RequirementPrompt.

Does not mutate Requirement fields.

6

Implementation Audit

Compare requirement text to codebase paths.

Code evidence is not human acceptance.

7

Reconciliation

Staff queue for audit vs backlog decisions.

Records decision — not production authorization.

8

Findings Report

Pilot metrics and findings for staff learning.

Report-only; production_activation blocked.

Technical architecture

Source adapters

SourceSignal taxonomy

Classification + risk

Correlation clusters

Control gap diagnosis

Requirement matcher

Codebase reality checker

Candidate review UI

Evidence store (RequirementPrompt)

Reconciliation

Business and investor value

Investors should see defensible operating intelligence — faster conversion of incidents and policies into governed engineering work, with backlog discipline and compliance defensibility.

Backlog discipline

Fewer orphan signals and duplicate requirements.

Operational learning loop

Incidents, policies, and logs become governed engineering work.

Engineering accountability

Codebase reality is checked before commitment.

Scalable intelligence layer

New sources are adapters, not new engines.

Defensible compliance posture

Evidence-backed, human-confirmed decisions.

Faster conversion

Grievances and operational failures become system controls, not vague tickets.

Live demo path

Open working pages in order — each link is read-only or staff-governed review.

Requirements hub

Active requirements list and detail entry points.

/requirements/ Open

Requirement intelligence

Hub linking observatory, candidates, and staff tools.

/requirements/intelligence/ Open

Candidate review

Grouped candidate review and control gap diagnosis.

/ai_services/source-candidates/ Open

Reconciliation queue

Implementation audit findings vs requirement truth.

/ai_services/requirements/implementation-audit/reconciliation/ Open

Staff pitch center

AI-generated pitches (login) — separate from this flagship page.

/portfolio/requirements/ Open

This presentation

Flagship demo command center with live evidence links.

/portfolio/requirement-intelligence-platform/demo/ Open

Roadmap — what production learning unlocks

Now

Controlled production staff pilot with guardrails.

Then

Google Drive adapter discovery (adapter only).

Later

Canonical assessment model if UAT proves persistent fields.

Future

Controlled status mutation only after explicit governance approval.

Roadmap does not change current approval boundaries — governance_state_change remains forbidden.

CODA closes the loop

CODA closes the loop.

What happened in operations.

What policy required.

What the backlog claimed.

What the code actually implemented.

What humans approved.

This is the difference between documentation and governed execution.

CODA Analytics Investor