Applied AI systems · Finance / Contracts / Operations

Intelligence is
probabilistic.
Trust shouldn't be.

Probabilistic models can interpret ambiguity. Acceptance should still be explicit, testable, and reviewable. I'm Jay Patel — an applied AI systems engineer building AI-assisted workflows for finance, contracts, supply chain, and operations, where models interpret ambiguous inputs, explicit rules govern acceptance, and material exceptions route to human review.

06 systems / explicit constraints / inspectable architecture

jay@systems : clausekinetic TRY TO BREAK IT

$inspect clausekinetic --baseline

Shared invariants. Different architectures. Run baseline probes or inject targeted failures to test containment.

READYSelect a system, then run baseline or inject a failure.
PROBE COMPUTE
Not run
FAILURE GATE
Not run
FIXTURE FINGERPRINT · SHA-256
Not run
Awaiting execution

Deterministic boundaries
Independent verification
Tamper-evident evidence
Human escalation path

01 / SELECTED SYSTEMS

Built to be questioned.

Shared invariants. Different architectures. Five failure surfaces across three business domains, each with an explicit deterministic boundary.

Finance Reconciliation · AP controls · Filing synthesis · Capital allocation Contracts & Supply Chain Amendment precedence · Obligations · Supplier penalties Operations Staffing resilience · Constraint simulation · Disruption testing
SYS.02 / CORPORATE FINANCE

Target Capital Allocation Model

SEC-sourced cash-flow & investment-capacity model · NYSE: TGT

An independent model reconciling Target Corporation's FY2021–FY2025 SEC filings into a 3-scenario, 5-year forecast of deployable capital. The first version blended cash stock, generated flow, and reserves into one number — the taxonomy was rebuilt to keep them separate before the result was called reliable.

8 SEC filings→Filing reconciliation→Capacity taxonomy gate→Validation-gated evidence

[REPRODUCIBLE TEST]

470/470 model-validation checks · 467/467 Python tests passing

8 SEC 10-K/10-Q filings (FY2021–FY2025), each hash-recorded; the database reconstructs bit-for-bit in a clean-room rebuild. Independent portfolio case study — not affiliated with, commissioned by, or endorsed by Target Corporation. Scenario forecasts, not investment advice.

SYS.03 / FINANCE

AuditLedger

Ledger auditor with independent critique

A controller-facing check on journal-entry postings. A Primary model proposes entries; a separate Critic model re-checks them against explicit GAAP balance assertions before anything is sealed. Every accepted batch is appended to a tamper-evident, hash-chained log — every rejected batch is rolled back, not silently corrected.

Primary interpretation→Critic re-check→Balance invariant→Tamper-evident seal

[REPRODUCIBLE TEST] · [DESIGN]

Independent critique + SHA-256 hash-chained audit trail

A SHA-256 hash chain makes a downstream mutation detectable — it does not prove the original entry was correct, and it does not stop a fully privileged operator from replacing the database and recomputing history. No external anchor, transparency log, or signed checkpoint exists yet. Test suite and source are public — see below.

Source (opens in a new tab)
SYS.04 / FINANCE

ReconcileAI

GL-to-bank reconciliation

Separate extraction, auditing, and governance to reconcile general-ledger entries against bank statements without silently clearing exceptions.

Normalization→Deterministic Matching→Policy Governance→AI Explanation→Human Review

[SYNTHETIC RESULT]

88.8%–97.5% match rate across 3 synthetic scenarios

Clean 97.5% · Fraud 95.9% · Messy 88.8%. Evaluated on synthetic benchmark datasets, not a universal accuracy claim.

Source (opens in a new tab)
SYS.05 / RESEARCH · FP&A

StratBrief AI

SEC filing & earnings-call synthesis

Synthesize SEC 10-K / 8-K filings and earnings-call transcripts while enforcing deterministic numeric-source verification. Treat a fluent summary and a financially defensible summary as different outputs.

Filing→Synthesis→Numeric gate

[DESIGN] NUMERIC POLICY

Deterministic numeric-source verification

Published numeric tokens must resolve to cited filing sources. An acceptance policy, not a claim that extraction or language models are error-free.

Source (opens in a new tab)
SYS.06 / WORKFORCE OPS

ShiftProof AI

Rostrix · Coverage simulation

Stress-test operational rosters against staffing call-outs. Surface coverage shortfalls and constraint violations before a disruption becomes an operating failure.

Roster→Call-out→Coverage gate

[DESIGN] CONSTRAINT CHECKING

Staffing disruption stress tests

Scenario-based coverage checks. No production staffing outcome or performance rate is asserted.

Live demo (opens in a new tab)
[LIVE PROBE] Executed in this browser session. [REPRODUCIBLE TEST] Public repo test command available. [SYNTHETIC RESULT] Evaluated on synthetic benchmark datasets. [DESIGN] Architectural invariant, not a benchmark.

02 / ENGINEERING PRINCIPLES

The model proposes.
The system decides.

Business-critical AI needs more than a persuasive response. It needs boundaries that hold when inputs conflict, independent checks disagree, or the happy path disappears. Shared invariants — not one shared architecture: each system below separates probabilistic interpretation from deterministic authority in the way its own domain requires.

01

Separate generation from acceptance.

Use models for interpretation. Use explicit constraints, independent critique, and testable gates to decide what is allowed through.

02

Make evidence a first-class output.

Keep the decision path inspectable. Bind records to integrity checks, retain source context, and distinguish provenance from factual correctness.

03

Expose uncertainty. Contain failure.

An unresolved conflict should remain unresolved—not become a confident answer. Route exceptions deliberately and test the refusal path.

03 / ABOUT

Why these problems.

I'm interested in business processes where a plausible answer isn't enough — invoice approval, reconciliation, contract precedence, financial filing analysis, and workforce planning. I use AI where ambiguity requires interpretation, and conventional software where correctness requires explicit rules. The five systems above are reproducible prototypes, not production deployments — the architecture decisions and the limitations are meant to be inspected, not taken on faith.

Interested in Applied AI, AI Systems, AI Automation, and technical product roles where domain logic matters as much as model capability.

Inspect the implementation, not just the interface.

Repositories, working demos, and explicit limits. The architecture is the argument.
GitHub LinkedIn Jay401331@gmail.com