Zero LLM Variance: Why Evaaluate Uses Deterministic Computation for All Financial Math
Large Language Models are transformative tools for research, analysis, and natural language generation. They are also fundamentally unsuitable for performing financial calculations in professional-grade valuation reports. This article explains why Evaaluate uses a strict architectural separation between LLM-powered tasks and deterministic mathematical computation.
The Variance Problem
LLMs are probabilistic systems. Given the same input prompt, they may produce different outputs across runs. This variance is a feature for creative writing but a critical flaw for financial computation. When a registered valuer signs a report stating a specific fair value per share, that number must be exactly reproducible from the stated inputs and methodology.
In testing, we observed that LLMs asked to perform multi-step DCF calculations produced variance of 2-8% across runs. For a company valued at INR 100 crore, this represents a range of INR 92-108 crore, which is neither acceptable nor defensible in a statutory context. Even with temperature set to zero, floating-point arithmetic in LLMs does not match the precision of structured mathematical computation.
Where LLMs Excel
The distinction is not that LLMs are bad tools; it is that they are the wrong tool for arithmetic. LLMs excel at tasks involving pattern recognition, synthesis, and generation. In a valuation workflow, these include extracting structured data from unstructured documents, synthesizing industry research, generating narrative methodology descriptions, writing contextual analysis of comparable companies, producing executive summaries, and drafting responses to common questions about the valuation approach.
These tasks involve language understanding and generation, which is precisely what LLMs are designed to do. The key insight is that none of these tasks involve performing arithmetic on which the valuation conclusion depends.
Where Deterministic Computation Is Required
Every calculation that feeds into the final valuation number must be performed by a deterministic mathematical engine. This includes DCF model projections, discount rate calculations, terminal value computation, NAV calculations, comparable company multiples, weighted blending of multiple methodology outputs, and all sensitivity and scenario analyses.
These computations are implemented as structured mathematical functions with defined inputs, outputs, and intermediate steps. Every intermediate result is stored and available for inspection. The same inputs will always produce the same output, regardless of when or how many times the computation runs.
The Architecture: Separation of Concerns
Evaaluate's architecture enforces a strict boundary between the LLM layer and the computation layer. The LLM layer handles data extraction, research, narrative generation, and quality checking of text. The computation layer handles all mathematical operations. Communication between layers occurs through structured data contracts: the LLM layer outputs structured data which the computation layer consumes as inputs, and the computation layer outputs structured results which the LLM layer uses for narrative generation.
At no point does the LLM perform a calculation that affects the valuation number. At no point does the computation engine attempt natural language tasks. Each system does what it does best.
Verification and Auditability
Deterministic computation enables complete auditability. For any valuation output, a reviewer can inspect the input data as extracted and confirmed by the practitioner, every formula applied at each computation step, all intermediate results, the final output, and the mathematical relationship between each step and the next. This audit trail is equivalent to reviewing a well-structured spreadsheet model, but with the additional benefit that the computation has been validated against a verified formula library.
The Practical Impact
This architectural choice has practical implications for practitioners. When a tax authority questions a specific number in the valuation report, the practitioner can trace it back through the computation chain to the source data and the formula that produced it. There is no ambiguity, no variance, and no reliance on a system that might produce a different number if run again.
For the valuation profession, this approach represents the best of both worlds: the efficiency and intelligence of AI for research and language tasks, combined with the precision and reliability of structured mathematical computation for everything that affects the bottom line. LLMs and deterministic math are not competitors; they are complementary tools that, when properly separated, produce output that is both intelligent and precise.