About
We built probuck.ai after experiencing first-hand how AI document tools fail when precision matters most — inconsistent extractions, unexplainable results, and no audit trail to diagnose what went wrong.
The root cause was clear: existing tools rely entirely on probabilistic retrieval, an approach that works well for discovery and summarisation but breaks down when you need to extract a specific number from a specific table and trust the result.
So we rebuilt extraction from the ground up. Our proprietary engine delivers deterministic, auditable results — every value is traceable to its exact source location. When exact matching isn't possible, the system falls back intelligently through multiple retrieval strategies, recording the method at every step.
The result: teams can act on extracted data with confidence and with a full evidence trail for compliance and auditability.
The Problem
Why existing AI tools fail at numeric extraction
AI-powered document tools are excellent for search and summarisation — but they hit a hard accuracy ceiling when tasked with extracting specific values from tables. The root causes are architectural, not tuning-related.
Table structure is destroyed before retrieval begins
Traditional AI document tools break tables into fragments, severing the relationships between headers, subtotals, and values. By the time retrieval runs, the context that makes a number meaningful is already gone.
Semantic search can't tell similar numbers apart
Numbers that mean very different things — like "Approved Amount" and "Total Cost" — look almost identical to AI search. No amount of tuning can fix an architectural limitation.
Inconsistent terminology defeats pattern matching
Different authors label identical concepts differently. Rule-based and schema-driven extraction breaks down the moment the vocabulary isn't perfectly controlled.
LLM-selected numbers aren't trustworthy
When an AI picks a number from a table, the result changes between runs. There's no verifiable chain of evidence, and no way to know whether the right cell was selected.
The Solution
Precision-engineered extraction
Instead of asking an AI to find and report a number using Retrieval Augmented Generation (RAG), our extraction engine uses AI to interpret the query and locate the target data source. The value is then extracted programmatically against preserved document structures. The result is auditable and repeatable.
Structure-preserving ingestion
Documents are processed in a way that preserves the full structural context of tables, headers, and reading order — not just extracted text. This means retrieval works with a faithful representation of the original document, not a degraded approximation.
Deterministic, not probabilistic
AI interprets the query and locates the target data source. The value is then extracted programmatically against the preserved table structure — the result is not the product of generative AI. The result is precise and repeatable.
Intelligent fallback, never a dead end
When an exact match isn't possible — due to ambiguous structure or unconventional formatting — the system automatically falls back through progressively flexible retrieval methods. You always get an answer, and you always know how it was found.
Full audit trail on every extraction
Every extracted value comes with a complete evidence trail: what was found, where it came from, and the method used to retrieve it. Teams can act on results with confidence, without manual verification.
Beyond Extraction
From precise extraction to agentic workflows
Precise extraction is only the starting point. Once a value is sourced precisely and carries a full evidence trail, it becomes safe to feed into downstream automation — something probabilistic pipelines can never offer with confidence.
probuck.ai composes these auditable extractions into agentic workflows that read across portfolios of documents, apply business logic, and produce structured outputs — automation that moves faster than manual review while remaining defensible to auditors, investment committees, and regulators.