An AI bot checks your bank account, figures out which customer paid which invoice, and updates your books automatically so your team doesn't have to.

FullSeam's cash application agent connects directly to banking portals, ERP systems, and customer communication channels to ingest remittance data, parse unstructured payment references (e.g., email confirmations, bank memo fields, customer portal exports), and probabilistically match payments to outstanding invoices. The agent uses a combination of NLP-based document understanding to extract remittance details from varied formats, supervised classification models trained on historical match/exception patterns, and fuzzy matching algorithms that handle partial payments, overpayments, and multi-invoice remittances. When confidence is below a configurable threshold, the agent escalates to a human reviewer with a pre-populated recommendation and supporting evidence. Over time, reinforcement learning from human corrections improves match accuracy and reduces escalation rates. The system operates continuously, eliminating batch-processing delays and enabling real-time cash visibility for treasury teams.

It's like having a tireless intern who opens every bank notification, cross-references it against every unpaid invoice, and updates the spreadsheet before you've even finished your morning coffee.

An AI reads your customer contracts, figures out what to bill and when, creates the invoices, and posts them to your accounting system—no human calculator needed.

FullSeam's contract-to-invoice agent ingests signed customer agreements (PDFs, DocuSign exports, CRM attachments) and applies large language model-based extraction to identify billing schedules, pricing tiers, usage thresholds, discount structures, and revenue recognition triggers (e.g., ASC 606 performance obligations). The extracted structured data is validated against a business rules engine configured per client, then used to automatically calculate invoice amounts, apply taxes and adjustments, generate compliant invoice documents, and post them to the ERP general ledger. The ML pipeline includes fine-tuned transformer models for legal/financial document understanding, named entity recognition for monetary terms and date parsing, and anomaly detection that flags contracts with unusual or ambiguous terms for human review. As the system processes more contracts, active learning continuously improves extraction accuracy for client-specific contract templates and edge cases, reducing the need for manual configuration over time.

It's like hiring a paralegal who speed-reads every contract, a mathematician who never miscalculates a proration, and an accountant who posts the journal entry—all rolled into one AI that never takes PTO.

An AI watches every transaction flowing through your books in real time, spots anything that looks wrong before the month ends, and tells your team exactly how to fix it.

FullSeam's reconciliation agent operates as a continuous monitoring layer across general ledger, sub-ledger, bank, and intercompany accounts. Rather than waiting for month-end to begin reconciliation, the agent ingests transaction streams in near real-time and applies unsupervised anomaly detection models (isolation forests, autoencoders) trained on historical transaction patterns to identify statistical outliers—such as duplicate entries, missing accruals, unusual vendor payments, or timing mismatches between sub-ledgers. When an anomaly is detected, a classification model categorizes the likely root cause (e.g., duplicate posting, FX mismatch, cutoff error) and generates a recommended corrective journal entry with supporting documentation. The agent also builds a predictive model of expected account balances based on seasonality, business cycles, and historical trends, alerting teams to material variances days before close. Over successive close cycles, the system learns organization-specific patterns, reducing false positives and increasing the precision of its recommendations, effectively transforming month-end close from a reactive scramble into a proactive, continuously validated process.

It's like having a smoke detector for your general ledger—instead of discovering the fire during month-end close, the AI smells smoke on day three and hands you the extinguisher.