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Selected Work · Real Projects

Financial clarity.
Engineering precision.

Every project here is real. Two specialist disciplines — Financial Operations and Digital Engineering — working in tandem to build systems that hold up under scrutiny.

Financial Work Engineering Projects
Real Client WorkFinancial OperationsSoftware EngineeringAI Applications
Two Disciplines · One Firm

Where the evidence lives

FINWEB operates through two specialist engines. Each discipline has its own body of work — explore them independently or see how they connect.

01

Financial Operations Portfolio

QuickBooks systems, chart of accounts, cash flow projections, and multi-company bookkeeping — built for real businesses across four industries.

ConstructionCoachingRetailBusiness Services
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02

Engineering & AI

AI agents, machine learning systems, secure applications, automation platforms, and developer tools — all built and maintained as open-source projects.

AI AgentsML SystemsSecurityAutomation
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Engine One · Financial Operations

Selected Financial Engagements

Six real consulting engagements documenting the business challenge, approach, deliverables, financial areas covered, and outcome — structured to the standard of an enterprise consulting practice.

Financial Records
Structured Operations
Bookkeeping, reconciliation and close support
Management Reporting
Decision Support
Financial statements, forecasts and commentary
Financial Controls
Operational Confidence
Documented processes and review-ready records
Multi-Industry
Business Context
Construction · Retail · Coaching · Services
Financial Capabilities

The accounting disciplines behind every engagement.

Financial Reporting

P&L statements, balance sheets, and executive dashboards designed for leadership decision-making.

  • P&L Statement
  • Balance Sheet
  • Executive Dashboard
  • KPI Reports

Bookkeeping

Accurate daily bookkeeping — general ledger, journal entries, and month-end close.

  • General Ledger
  • Journal Entries
  • Trial Balance
  • Monthly Close

Financial Analysis

Revenue, expense, and profitability analysis that surfaces what the numbers actually mean.

  • Revenue Analysis
  • Expense Analysis
  • Gross Profit
  • Variance Reports

Bank Reconciliation

Full reconciliation of bank accounts, cash positions, and deposit records.

  • Bank Reconciliation
  • Cash Reconciliation
  • Deposit Matching
  • Discrepancy Resolution

Financial Controls

Internal controls, documentation standards, and audit-ready records maintained continuously.

  • Control Documentation
  • Record Standardisation
  • Audit Readiness
  • Compliance Support

Excel Engineering

Advanced financial templates, data automation, PDF-to-Excel conversion, and large dataset processing.

  • Financial Templates
  • PDF to Excel
  • Data Cleaning
  • Spreadsheet Automation

Accounting Operations

End-to-end accounting support — from daily entries through financial statement preparation.

  • Accounts Payable
  • Accounts Receivable
  • Asset Management
  • Liability Tracking

Business Reporting

Structured management reports and business performance documentation for decision-makers.

  • Management Accounts
  • Board Packs
  • Cash Flow Reports
  • Performance Reports

Executive Reporting

Board-ready executive summaries that communicate financial performance without requiring specialist interpretation.

  • Executive Summaries
  • Business Performance
  • Strategic KPIs
  • Commentary

Data Validation & Cleaning

Large-dataset processing: duplicate removal, format standardisation, and data quality assurance.

  • Duplicate Removal
  • Format Standardisation
  • Data Validation
  • Quality Checks
Consulting Engagements

Six engagements. Real work. Documented outcomes.

Each engagement is presented as a full consulting case study: context, challenge, objectives, approach, deliverables, financial areas, outcome, and key lesson.

ConstructionFinancial Analyst

Financial Projection Dashboard

Business Context

A construction business managing multiple active projects had no forward-looking financial model. Revenue, project costs, and cash positions were tracked in separate, disconnected files with no unified view.

Business Challenge

Cash flow managed reactively. Leadership made capital decisions without forward-looking data, creating financial risk during high-expenditure project phases.

Objectives
  • Create a single source of financial truth
  • Build a 90-day cash flow projection
  • Enable weekly updates without specialist support
Approach & Solution

Built a structured Excel financial dashboard connecting revenue forecasting, expense tracking by category, profit margin analysis, and a 90-day cash flow projection — all driven from a single input layer with documented assumptions.

Deliverables
  • Revenue Forecasting Model
  • Expense Tracking by Category
  • Profit Margin Analysis
  • Cash Flow Projection (90-day)
  • Excel Financial Dashboard
Financial Areas
Cash Flow AnalysisRevenue ForecastingExpense ManagementFinancial Reporting
Technologies & Tools
Microsoft ExcelFinancial ModellingChart of AccountsData Validation
Business Outcome
Leadership gained a proactive financial view they could update weekly — replacing reactive cash management with planned, documented operations.
Timeline

1–7 Days

Key LessonConstruction businesses need project-level cost visibility connected to company-wide cash flow. A single model serving both levels eliminates the information gap.
Start a similar project Financial services
CoachingAccountant

QuickBooks Financial Statements

Business Context

A solo coaching business had been operating for over a year without structured bookkeeping. Revenue was deposited and expenses were paid, but no accounting structure existed — making profitability measurement and tax preparation impossible.

Business Challenge

No accounting structure and no visibility into true profitability. Tax season required reconstructing months of transactions from bank statements each year.

Objectives
  • Establish a clean accounting foundation in QuickBooks
  • Produce audit-ready financial statements
  • Enable owner-operated ongoing bookkeeping
Approach & Solution

Configured QuickBooks from scratch with a chart of accounts tailored to coaching services. Produced clean P&L, balance sheet, and budget reports with a system designed for independent maintenance.

Deliverables
  • QuickBooks Setup & Configuration
  • Chart of Accounts (Coaching-specific)
  • Profit & Loss Statement
  • Balance Sheet
  • Budget Reports
Financial Areas
BookkeepingFinancial StatementsAccounting OperationsBusiness Reporting
Technologies & Tools
QuickBooks OnlineChart of Accounts DesignFinancial ReportingBudget Management
Business Outcome
Clean, professional financial statements ready for tax preparation and a sustainable accounting system the owner operates independently.
Timeline

1–7 Days

Key LessonService businesses need a chart of accounts that reflects how revenue is actually earned. Specificity makes ongoing categorisation intuitive and accurate.
Start a similar project Financial services
Retail & WholesaleFinancial Consultant

Chart of Accounts Implementation

Business Context

A retail and wholesale operation had grown organically over several years without a formal financial architecture. Revenue streams from retail and wholesale channels were recorded inconsistently, and inventory costs were commingled with operating expenses.

Business Challenge

Commingled categories made accurate profitability reporting impossible. Historical data could not be trusted for business decisions or tax compliance — requiring a complete structural rebuild.

Objectives
  • Design a COA reflecting retail/wholesale revenue separation
  • Migrate historical transactions into the new structure
  • Deliver a reporting framework for the internal team
Approach & Solution

Designed a complete chart of accounts for the retail/wholesale environment, migrated historical transactions, established expense categorisation standards, and delivered a reporting framework with team training.

Deliverables
  • Chart of Accounts Design
  • Financial Structure Architecture
  • Expense Categorisation System
  • Historical Data Migration
  • Team Training
  • Reporting Framework
Financial Areas
Accounting OperationsFinancial ControlsRecord StandardisationBusiness Reporting
Technologies & Tools
QuickBooksChart of AccountsFinancial ArchitectureData Migration
Business Outcome
Clean, trustworthy financial records with a structure the business maintains independently — and reports that accurately reflect performance by channel.
Timeline

1–7 Days

Key LessonRetail/wholesale businesses need channel-level revenue separation from day one. Retrofitting this structure is always more expensive than building it correctly at the start.
Start a similar project Financial services
Business ServicesSenior Accountant

Bookkeeping Cleanup & Catch-Up

Business Context

A multi-entity business services operation had months of backlogged bookkeeping across two companies with intercompany transactions. Tax deadlines were approaching and books were not in condition for filing.

Business Challenge

Multi-company backlog across two entities. Unreconciled bank accounts. Intercompany transactions misclassified. Audit trail absent. Tax-readiness required a full rebuild from source data.

Objectives
  • Reconcile all bank accounts across both entities
  • Correctly classify all intercompany transactions
  • Deliver tax-ready books with complete audit trail
Approach & Solution

Completed a full catch-up engagement: categorised and reconciled all transactions across both entities, resolved intercompany accounting, reconciled all bank accounts, and delivered tax-ready books with documented audit trails.

Deliverables
  • Multi-company Bookkeeping Catch-Up
  • Intercompany Accounting Resolution
  • Bank Reconciliation (all accounts)
  • Tax-ready Financial Records
  • Audit Trail Documentation
Financial Areas
BookkeepingBank ReconciliationFinancial ControlsAccounting Operations
Technologies & Tools
QuickBooksMulti-entity AccountingBank ReconciliationFinancial Cleanup
Business Outcome
Both entities had fully reconciled, tax-ready books with intercompany transactions properly structured — and a clear audit trail for each account.
Timeline

1–7 Days

Key LessonMulti-entity cleanup requires resolving intercompany balances before any entity can be considered reconciled. Attempting to reconcile entities independently produces circular errors.
Start a similar project Financial services
Professional ServicesFinancial Reporting Specialist

Financial Statement Preparation

Business Context

A professional services firm required complete financial statements prepared from raw accounting data. Existing records had structural inconsistencies that needed resolution before statements could be produced.

Business Challenge

Inconsistently structured records. Missing documentation for several transaction categories. Balance sheet did not balance — indicating misclassified entries requiring investigation and correction.

Objectives
  • Investigate and resolve balance sheet discrepancies
  • Prepare complete financial statements from source data
  • Document all adjustments with supporting rationale
Approach & Solution

Conducted a systematic review of the general ledger, identified and corrected misclassified entries, reconciled balance sheet items, and prepared a complete financial statement set with full supporting documentation.

Deliverables
  • Profit & Loss Statement
  • Balance Sheet
  • Trial Balance
  • General Ledger Review
  • Adjusting Journal Entries
  • Financial Documentation
Financial Areas
Financial StatementsGeneral LedgerTrial BalanceFinancial Reporting
Technologies & Tools
Microsoft ExcelFinancial ReportingGeneral LedgerDocumentation Standards
Business Outcome
Clean, balanced financial statements supported by a complete audit trail — ready for external accountant review and tax filing.
Timeline

1–7 Days

Key LessonFinancial statement preparation is only as reliable as the underlying ledger. Starting with a trial balance reconciliation before preparing statements prevents cascading errors.
Start a similar project Financial services
Multiple IndustriesData & Financial Systems Analyst

PDF to Excel Conversion & Data Cleaning

Business Context

Multiple client engagements required converting financial data locked in PDF documents — bank statements, supplier invoices, and financial reports — into structured, analysis-ready Excel formats for accounting processing.

Business Challenge

PDF financial documents cannot be analysed, reconciled, or imported into accounting systems without conversion. Manual re-entry introduced errors and consumed significant time across repeated engagements.

Objectives
  • Convert PDF financial records to structured Excel format
  • Standardise data across inconsistent source formats
  • Validate and clean extracted data before accounting use
Approach & Solution

Engineered a systematic PDF-to-Excel conversion and data cleaning process covering bank statements, reports, and financial documents — applying format standardisation, duplicate detection, and data validation at each stage.

Deliverables
  • Structured Excel Data Files
  • Data Cleaning & Validation Report
  • Duplicate Detection & Removal
  • Format Standardisation
  • Data Quality Documentation
Financial Areas
Data ManagementExcel EngineeringFinancial Data ProcessingAccounting Operations
Technologies & Tools
Microsoft ExcelPDF ProcessingData ValidationData CleaningFormat Standardisation
Business Outcome
Financial data previously inaccessible in PDF format converted to analysis-ready Excel files — reducing manual processing time and eliminating transcription errors.
Timeline

1–5 Days

Key LessonData quality at the point of extraction determines the accuracy of every downstream report, reconciliation, and analysis. Validation at the source is not optional.
Start a similar project Financial services
Industries Supported

Financial operations experience across multiple sectors.

Each industry presents distinct accounting challenges. The work described reflects real engagement experience across each sector.

Construction

Cash flow projections, project cost tracking, revenue forecasting, and financial dashboard engineering.

Retail & Wholesale

Chart of accounts design, channel-level revenue separation, inventory reporting, and expense categorisation.

Coaching & Services

QuickBooks setup, P&L preparation, balance sheet management, and budget reporting for service businesses.

Business Services

Multi-entity bookkeeping, intercompany accounting, bank reconciliation, and tax-ready book preparation.

Professional Services

Financial statement preparation, general ledger review, trial balance reconciliation, and audit-trail documentation.

Multiple Industries

PDF-to-Excel conversion, data cleaning, financial data standardisation, and reporting system engineering.

Engine Two · Digital Engineering

Engineering & AI Projects

Sixteen GitHub repositories grouped into six engineering disciplines. Each represents a real, maintained body of work — not a tutorial clone, not a demo project.

AI & Agent Engineering

AI & Agent Engineering

Production-quality AI engineering built around the OpenAI ecosystem and the Model Context Protocol — spanning raw API integration to fully autonomous agent architectures that connect AI to real business tools.

The Problem

Building reliable AI systems requires more than calling an API. Real-world agent systems demand orchestration, tool delegation, context management, and the emerging MCP standard for connecting AI to external data sources.

Engineering Approach

Designed and built multi-agent workflows using the OpenAI Agent SDK, implemented MCP-compatible servers for structured context delivery, and engineered agentic AI pipelines suitable for production business environments.

Autonomous agent workflows with tool delegation
Model Context Protocol server implementations
Structured output and response validation
Async agent pipelines with error recovery
Production-ready agentic AI architecture
PythonOpenAI SDKAgent SDKModel Context ProtocolGPT-4oAsync Python
Demonstrates the capability to build autonomous AI systems that integrate with real business tools — not just chatbots.
Discuss this work View all engineering services
Machine Learning Systems

Machine Learning Systems

End-to-end machine learning implementations covering the full ML pipeline — from data preprocessing and feature engineering through model training, evaluation, and deployment-ready packaging.

The Problem

Machine learning in practice requires clean pipelines, reproducible experiments, and well-documented code that can be maintained and extended. Most ML code is exploratory — these systems are built to be production-ready.

Engineering Approach

Built structured ML systems covering supervised and unsupervised learning, implemented scikit-learn and deep learning pipelines, and documented every model decision for reproducibility and peer review.

Supervised learning: regression, classification, ensembles
Unsupervised learning: clustering, dimensionality reduction
Clean data pipelines with preprocessing layers
Model evaluation with cross-validation and metrics
Documented experiments with reproducible results
Pythonscikit-learnPandasNumPyMatplotlibJupyter
Demonstrates the ability to apply ML rigorously — from problem framing through to a deployable, interpretable model.
Discuss this work View all engineering services
Secure Applications

Secure Applications

A suite of security-focused applications built with encryption, authentication, and privacy as first-class concerns — not afterthoughts. Each system implements security patterns found in enterprise environments.

The Problem

Most application security is bolted on after the fact. These systems were designed security-first: defining the threat model before writing the first line of code, then implementing cryptographic primitives correctly.

Engineering Approach

Built secure file management, encrypted vault storage, and credential management systems using industry-standard cryptographic libraries. Every input is validated, every secret is hashed, and every session is bounded.

AES-256 file encryption with PBKDF2 key derivation
Secure vault with authentication and access control
Credential management with encrypted local storage
Session management and secure logout
Audit trail for all sensitive operations
PythonAES-256PBKDF2FastAPISQLiteStreamlitCryptography
Clients handling sensitive financial data or personal information can trust that engineering defaults to secure-by-design.
Discuss this work View all engineering services
Automation Platforms

Automation Platforms

Productivity-focused automation applications that eliminate repetitive work. From task management to file conversion pipelines, these systems are built on the principle that software should reduce friction, not create it.

The Problem

Business operations generate recurring, low-value tasks that consume time without creating value. Automation tools need to be reliable enough to run unsupervised and simple enough that non-technical users can operate them.

Engineering Approach

Built an AI-powered task assistant for intelligent delegation, a structured task management application with priority logic, and a multi-format file conversion utility — all with clean interfaces built on Streamlit.

AI-powered task assistant with intelligent delegation
Priority-based task management with persistence
Multi-format file conversion (CSV, Excel, JSON, PDF)
Clean Streamlit interfaces for non-technical users
Event-driven workflow triggers
PythonStreamlitFastAPIOpenAI APISQLitePandas
Demonstrates ability to build internal tools that real teams adopt — not just technical demos.
Discuss this work View all engineering services
Data Processing Tools

Data Processing Tools

Data utility tools built for the messy realities of business data — inconsistent formats, duplicate records, and files that need to be merged, cleaned, and transformed before they are useful.

The Problem

Raw business data is rarely clean. Before analysis or reporting can happen, data must be merged from multiple sources, validated for consistency, duplicates removed, and outputs standardised for downstream systems.

Engineering Approach

Built a file fusion tool for merging and transforming structured files across formats, an automated data cleaning and deduplication system, and a Python utilities library covering common data engineering patterns.

Multi-file merge with schema alignment
Automated duplicate detection and removal
Data type inference and normalisation
Export to Excel, CSV, and JSON
Visual data preview before processing
PythonPandasStreamlitOpenPyXLCSVJSONExcel
Financial reporting and operations depend on clean data. These tools address the data preparation layer that most projects ignore.
Discuss this work View all engineering services
Developer Utilities

Developer Utilities

A set of developer-focused utilities that solve specific, high-friction problems in everyday engineering workflows — format conversion, code scaffolding, and reusable Python patterns.

The Problem

Developer productivity suffers from accumulated small frictions: converting file formats by hand, writing the same boilerplate repeatedly, or searching for a reliable implementation of a common pattern.

Engineering Approach

Built and documented a utility library covering common Python engineering patterns, a multi-format converter, and scaffolding templates — all structured as importable, well-tested modules.

Multi-format conversion (CSV → Excel → JSON → PDF)
Reusable Python utility modules
CLI tools built with Typer and Rich
Well-documented with examples
Tested with pytest
PythonTyperRichPathlibClickpytest
Code that is structured as reusable utilities is code that can be maintained, extended, and handed off — not thrown away.
Discuss this work View all engineering services
Full-Stack Capability

Technology Expertise

Eight domains of expertise built through real projects — not certifications alone. Every category maps to production work in the portfolio above.

Frontend

Modern web interfaces

Next.jsReactTypeScriptTailwind CSSCSS Modules

Backend

APIs and server systems

FastAPINode.jsRESTPythonSQLite

AI & Agents

Autonomous AI systems

OpenAI SDKAgent SDKGPT-4oMCPTool Use

Machine Learning

Data-driven models

scikit-learnPandasNumPyJupyterMatplotlib

Security

Cryptography & access control

AES-256PBKDF2AuthEncryptionAudit Trails

Data Tools

Processing & transformation

PandasStreamlitExcelCSVOpenPyXL

Financial Systems

Accounting & forecasting

QuickBooksExcelFinancial ModellingChart of Accounts

Developer Tools

CLI and utility libraries

TyperRichClickpytestPathlib
How We Work

How we solve problems

A consistent process applied to every engagement — whether it is a QuickBooks setup or a multi-agent AI system.

01

Discover

Understand the business, the systems already in place, and the specific problem that needs solving. No assumptions — only documented requirements.

02

Plan

Map the solution architecture before writing a line of code or touching a spreadsheet. Deliverables, timeline, and scope agreed in writing.

03

Build

Execute with precision. Financial systems are built to accounting standards. Engineering projects follow clean architecture and are tested before delivery.

04

Validate

Every deliverable is checked against the original requirements. Financial work is reconciled. Software is tested. Nothing ships until it is verified.

05

Deliver

Structured handover. Documentation included. The client receives the work and understands how to use and maintain it independently.

06

Support

Available for questions, amendments, and follow-on work. Long-term relationships are built from reliable delivery, not from lock-in.

How We Think

Engineering Principles

Eight principles that guide every project — financial or technical. These are not aspirational values. They are constraints applied from day one.

Performance First

Every system is designed with performance as a constraint, not an afterthought. Fast interfaces, efficient queries, and lean pipelines.

Accessibility by Default

WCAG AA compliance is the baseline. Semantic HTML, proper heading hierarchy, keyboard navigation, and visible focus states — built in, not added later.

Scalable Architecture

Systems are designed to grow. A financial model built for 10 transactions should still work cleanly at 10,000. A codebase built for one developer should be navigable by a team.

Security by Design

Threat modelling before the first line of code. Secrets never in plaintext. Input validated at every boundary. Encryption applied to data at rest and in transit.

Maintainability

Code is written to be read by humans, not just executed by machines. Clear naming, documented decisions, and consistent structure that future maintainers can follow.

Reusable Components

Interfaces are built from composable parts, not one-off screens. Financial models share logic. Engineering modules are designed for re-use from the start.

Clean Code

Every function does one thing. Every module has a clear boundary. Dependencies are explicit. Complexity is earned, not inherited from careless construction.

Business-Focused Development

Technical decisions are always tied to business outcomes. The goal is never just working code — it is working code that solves a real problem for real people.

What Gets Built

Results & Deliverables

Tangible outputs — not reports, not recommendations, not theory. Every engagement ends with something the client can use.

Financial Dashboards

Excel-based dashboards with live P&L, cash flow, and margin views

Cash Flow Forecasts

90-day and annual projections based on real revenue and cost data

Tax-Ready Books

Reconciled, categorised accounts ready for accountants and tax filing

QuickBooks Systems

Full QuickBooks setup, chart of accounts, and reporting structure

AI Applications

Autonomous agent systems built on the OpenAI SDK and MCP

Automation Platforms

Workflow automation tools that reduce manual, repetitive operations

Secure File Systems

Encrypted storage, vault management, and password utilities

Machine Learning Projects

End-to-end ML pipelines: data prep, training, evaluation, deployment

Streamlit Applications

Interactive data applications with clean UIs for non-technical users

Data Processing Tools

File merge, cleaning, and transformation pipelines for messy data

Built by FINWEB · Engineered for Business

Engineering built to a professional standard.

Every engineering capability at FINWEB is grounded in practical, working systems — not certifications or theoretical knowledge. From AI agent architectures to encrypted security platforms, the work reflects the standard applied to every client engagement.

FINWEB builds software that is secure, maintainable, and aligned to a business outcome — delivered under the FINWEB brand, owned by the client.

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Whether you need clean financial books, a cash flow model, an AI application, or a complete engineering system — the work starts with one conversation.

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