Deterministic vs. AI: Why CAM Reconciliation Requires Reproducible Math
The Short Version
AI CAM reconciliation software can generate plausible-looking outputs, but it cannot produce the same answer twice. For CAM math—which must survive tenant disputes, audits, and litigation—that is not a minor limitation. Deterministic calculation engines apply exact arithmetic to explicit rules and produce identical results on every run. That is what an audit trail requires.
The Court Test: Can Your Reconciliation Stand Up to a Tenant's Attorney?
When a tenant's attorney requests documentation for a $47,000 CAM charge, the question isn't just "is the math right?" It's "can you prove it, step by step, three years from now?"
That's the court test. And it's where AI CAM reconciliation software runs into a problem that no product roadmap can fix: probabilistic systems don't produce the same answer twice.
A tenant files a dispute. Their auditor requests the calculation methodology. You hand them the output from your AI-powered platform. They ask: "Can you re-run this with the same inputs and get the same result?" In many cases, the answer is no—because that's not how language models work.
Courts don't accept "the AI said so" as a defensible accounting methodology. Neither do most lease agreements. For more on what a CAM reconciliation process actually involves, see our CAM reconciliation guide.
What "Deterministic" Means—And Why It Matters for Finance
A deterministic calculation is simple in concept: the same inputs always produce the same output. Not approximately the same. Identical.
For CAM math, this means every expense allocation, every gross-up adjustment, and every cap calculation traces back to a specific formula with a specific result. You hand the inputs to any CPA, three years later, and they arrive at the same number. That's the standard financial documentation requires.
GAAP requires that supporting calculations be reproducible and verifiable. An auditor needs to trace from the reconciliation statement back to source data and verify the arithmetic at each step. Deterministic CAM calculation makes that possible.
At a technical level, deterministic engines use exact arithmetic—Python's Decimal type, not floating-point math. The difference between Decimal("2.10") and float(2.10) is invisible in most contexts, but it adds up across hundreds of tenants and dozens of expense categories over a multi-year lease term. See our pricing for plans that include full calculation audit logs.
The Audit Trail Problem with Probabilistic AI
AI language models generate output by sampling token probabilities. Two calls with identical inputs can produce different results. Models drift between versions. There's no step-by-step ledger showing how each dollar was allocated—just a plausible-sounding answer.
This isn't a flaw. It's how the technology works. But it creates a specific problem for financial documentation: you can't show your work.
A proper CAM reconciliation audit trail requires:
- The gross-up formula applied, including the specific occupancy percentage used
- Each expense category and whether it was included, excluded, or capped
- The pro-rata share calculation, with denominator and numerator
- The cap calculation, showing base year, cumulative increases, and any floor adjustments
- The tenant's estimated payments versus the calculated actual
- The final settlement amount and how it was derived
A deterministic engine produces all of this. An LLM produces a number and a plausible explanation. When a tenant's attorney asks for the underlying calculation, those are very different things.
Side-by-Side Comparison
| Dimension | Deterministic Engine | AI/LLM Calculation |
|---|---|---|
| Accuracy | Exact (IEEE 754 / Decimal) | Approximate (probabilistic) |
| Reproducibility | Identical re-runs guaranteed | Output may vary per run |
| Audit trail | Full step-by-step ledger | Black-box reasoning |
| Court defensibility | Yes—traceable math | High risk—unexplainable |
| Edge case handling | Explicit business rules | May hallucinate precedent |
The reproducibility row is the one that matters most in practice. A 2% discrepancy between two runs of the "same" calculation isn't an edge case—it's a documentation failure.
When AI Is Appropriate: Document Extraction, Not Math
AI is genuinely useful in the CAM reconciliation workflow. Just not for the math.
Document extraction is where AI earns its place. OCR and intelligent classification can parse a 200-page PDF lease, identify the relevant CAM clauses, and flag which GL codes map to which expense categories. Done manually, that work takes hours. AI can cut it to minutes.
CamAudit uses AI for exactly this: extraction and classification, with human verification required before any extracted value feeds into a calculation. The math itself runs on a deterministic Python engine using Decimal arithmetic—no floating-point, no approximation.
Extract
AI reads the lease and identifies CAM inclusions, exclusions, caps, and gross-up provisions
Verify
A human reviews and confirms the extracted values before they enter the pipeline
Calculate
The deterministic engine runs the math—identically every time
Audit
The full calculation ledger is stored and retrievable for disputes or audits
Frequently Asked Questions
Can AI software accurately calculate CAM reconciliation charges?
AI language models can generate plausible-looking CAM reconciliation outputs, but they cannot guarantee accuracy in the way financial documentation requires. The core problem is reproducibility: an LLM may produce a different answer on two runs with the same inputs. For a financial calculation that must be auditable and defensible, that is not acceptable. AI is better suited to extraction tasks—reading leases, classifying GL codes, flagging anomalies—rather than performing the calculation itself.
What makes a CAM reconciliation audit-trail compliant?
A compliant audit trail shows every step of the calculation: the gross-up formula and inputs, each expense category determination, the pro-rata share calculation, cap application, and the final settlement figure. It must be reproducible—a CPA should be able to take the same inputs and arrive at the same number. It should also be stored in a way that is retrievable years later, since CAM disputes often arise 12–24 months after the reconciliation period.
How does deterministic calculation differ from AI-powered reconciliation?
A deterministic calculation engine applies explicit formulas to inputs and produces the same output every time. If you run a $2.1 million expense pool with a 73.4% occupancy rate and a 95% gross-up cap, the answer is the same today, tomorrow, and three years from now. An AI-powered reconciliation uses probabilistic methods—the model estimates what the right answer probably is, based on patterns in its training data. The output may be accurate, but it cannot be proven to be, and it may change between runs.
Can a tenant dispute an AI-generated CAM reconciliation?
Yes, and with increasing frequency. Tenants and their auditors are aware that AI-generated outputs are not reproducible, and some lease audit firms specifically flag AI-generated reconciliations as a red flag. If a landlord cannot produce a step-by-step calculation methodology that an independent auditor can verify, the reconciliation is vulnerable to dispute—and potentially to reversal.
What is the risk of using AI for CAM math in commercial leases?
Three distinct risks. First, legal risk: if a tenant disputes a charge and you cannot reproduce the calculation, your position in arbitration or litigation is weak. Second, compliance risk: GAAP requires that financial calculations supporting reported figures be traceable. An AI-generated number without a calculation ledger may not meet that standard. Third, operational risk: if you cannot reproduce last year's reconciliation, you cannot audit your own errors or demonstrate that a corrected reconciliation is actually correct.
Does CamAudit use AI for calculations?
No. CamAudit uses AI only for document extraction—reading leases, identifying relevant clauses, and classifying GL entries. All of this requires human verification before the data enters the calculation pipeline. The calculations themselves run on a deterministic Python engine using exact Decimal arithmetic. Every step is logged, stored, and reproducible.
See CamAudit's Deterministic Calculation Engine
CAM reconciliation errors are rarely caught until a tenant hires an auditor. CamAudit's deterministic engine runs your reconciliation with the same rigor a tenant's auditor would apply—and gives you a full calculation ledger before the statements go out.
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