Overview

Fisqora is a UK-based fintech company that manages financial operations across multiple entities, with accounting data, ledgers, invoices, contracts, and supporting documents spread across different systems. Manual data exports, disconnected information sources, and time-consuming audit workflows made it difficult for finance teams to access and analyze information efficiently. Bacancy Technology built an AI-powered financial intelligence platform that automated data processing, centralized financial and document-based information, streamlined audit workflows, and enabled faster financial analysis, reporting, and decision-making.

Technologies Used

Python
LangGraph
DuckDB
AWS
Anthropic Claude
Chainlit

Project Highlights

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Automated daily financial data pipeline syncing 22 accounting entity types without manual intervention

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Conversational AI finance assistant powered by a 22-tool LangGraph agent for instant Q&A

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CFO-level contract and audit workflow automation, including VAT and prepayment reconciliation

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Continuous AI accuracy benchmarking through a 115-question evaluation framework

The Challenges

1

The client's teams had to log into the accounting system regularly, export financial reports, and manually consolidate information before they could begin analysis. This daily process consumed valuable time, delayed access to current financial data, and increased the possibility of inconsistencies caused by manual handling.

2

Important business information, including Excel ledgers, invoices, contracts, and supporting documents, was stored across Google Drive without a centralized and searchable structure. Finance teams had to manually locate files and review multiple sources whenever they needed information for analysis, reporting, or decision-making.

3

Financial data from the accounting platform and supporting records from business documents existed in separate silos. Questions requiring teams to cross-reference transactions, ledgers, invoices, and documents involved manual reconciliation, making routine financial analysis slower and dependent on significant human effort.

4

Preparing prepayment reconciliations, VAT schedules, and journal analyses involved repetitive manual processes. Contract and agreement reviews also depended heavily on senior finance stakeholders, particularly CFO-level expertise, creating bottlenecks when financial or business decisions required timely review.

Solutions by Bacancy

1

Bacancy Technology built an event-driven pipeline that automatically initiates scheduled financial data synchronization. Our fintech developers integrated the accounting data source with a processing workflow capable of syncing 22 entity types, processing the data through isolated stages, and loading it into the platform’s analytical environment. We also implemented controlled message handling and automatic retry mechanisms to improve reliability when a processing stage fails.

2

Our team created a document processing workflow that brings spreadsheets and business documents into a structured and searchable environment. Excel-based ledgers are normalized into analytical formats, while documents such as PDFs, Word files, and presentations are converted into searchable content with OCR support for scanned files. This helped create a more accessible information layer for financial teams.

3

We consolidated accounting information and processed financial records into a centralized analytical environment using DuckDB. This allowed the platform to work with structured accounting data, normalized ledgers, and searchable documents from a single interface. Our developers then built an AI-powered financial assistant that can select relevant tools, retrieve information, generate SQL queries, and present results through natural-language responses and visualizations.

4

Bacancy Technology built purpose-specific capabilities for prepayment reconciliation, VAT reconciliation schedules, journal analysis, and financial reporting. We also developed an AI-powered contract review capability that provides structured analysis across key terms, risks, obligations, exit and termination conditions, liabilities, and opportunities. These workflows gave finance teams more structured support for repetitive analysis and review activities.

Core Features

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Natural-language AI finance assistant with instant answers, interactive charts, and downloadable reports

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Automated VAT reconciliation, prepayment schedules, and journal entry analysis for audit readiness

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AI-powered contract and NDA review with structured, six-section risk analysis

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One-click export of any analysis to Word, Excel, or PDF for stakeholder sharing

No. of Resource

01

No. of Resource

Time Frame

January 2026 - Ongoing

Time Frame

Project Snapshot

Fisqora

Outcomes

Eliminated 100% of manual daily data exports across 22 entity types

Instant financial Q&A powered by 22-tool AI agent

Reduced audit preparation time by 40% with automated reconciliation schedules

Cut contract review time by 35% through structured AI-driven risk analysis

Reliable data pipeline with automatic 24-hour retry on failures

Continuous accuracy tracking through 115-question benchmarking framework

Technical Stack

FRONTEND Chainlit
BACKEND Python 3.12+
AI & LLM ORCHESTRATION LangChain LangGraph
AI MODEL PROVIDER OpenRouter Anthropic Claude
DATABASE & ANALYTICS DuckDB
CLOUD INFRASTRUCTURE AWS
EVENT SCHEDULING Amazon EventBridge
MESSAGE QUEUE Amazon SQS
THIRD-PARTY INTEGRATIONS Xero APIGoogle Drive
INFRASTRUCTURE AS CODE Terraform

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