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CODA Requirement Intelligence Engine

1.7M+Logs observed 177Requirements indexed 5Source reservoirs 25Pilot candidates

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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

Raw operational issue → system-control recommendation — not grievance tone rewriting.

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.”

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

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

Staff production pilot workflow

Controlled staff pilot — not autonomous production.

Report-only Source Observatory

Reads selected source reservoirs; produces JSON/report.

No database mutation on requirements or logs.

Source Correlation

Clusters related signals across sources.

Evidence optional; no auto-promote.

Grouped Candidate Review

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

Defer/dismiss updates review_state only.

Control Gap Diagnosis

Automated diagnosis with suggested system controls.

Diagnosis is not proof or approval.

Selective Evidence Attachment

Attach to existing requirements via RequirementPrompt.

Does not mutate Requirement fields.

Implementation Audit

Compare requirement text to codebase paths.

Code evidence is not human acceptance.

Reconciliation

Staff queue for audit vs backlog decisions.

Records decision — not production authorization.

Findings Report

Pilot metrics and findings for staff learning.

Report-only; production_activation blocked.

Business and investor value

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.

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.

Presenter notes

Audience: Executive

Press N to toggle.

Do not say “AI creates requirements.”
Say: CODA converts operational signals into governed requirement intelligence with humans controlling final decisions.

Opening hook

Do not say 'AI creates requirements.' Say: CODA converts operational signals into governed requirement intelligence — humans control final decisions.

Do / Don't

Don't: imply auto-approval, silent requirement creation, or production activation from this deck. Do: emphasize live routes as proof, guided confirmation, and evidence-only governance.

Problem framing

Organizations drown in fragments: logs, grievances, policies, documents, and a dirty requirement table. None of these alone is the truth.

Breakthrough architecture

One operating layer: SourceSignal → classify → correlate → diagnose → match requirements → check codebase → guided confirmation.

Control gap example

Walk payment/grievance example: system recommends separation of duties and audit trail — not 'employees should not steal.'

Codebase reality

Requirements describe intent. Code is implementation truth. Partial implementation is the norm — diagnosis must say what is missing.

Governance safeguards

Evidence-only, no auto-mutation, production_activation blocked, approval_gate NOT_IMPLEMENTED.

Demo flow

Hub → source candidates → grouped review → control diagnosis → optional evidence attach → audit/reconciliation.

Closing pitch

CODA closes the loop between operations, policy, backlog, code, and human approval — governed execution, not documentation theater.