Case studies: software, data, automation, growth and remote teams

More projects 37

ERPs, CRMs, point of sale, retrieval assistants and dashboards, plus smaller engagements. Open a row for what was built and the stack.

• A weekly news report on cervical cancer screening, scoped to run itselfMartin Dillon, tracking news on TruScreen and cervical cancer screening. January 2026.

  Martin Dillon wanted every new item about TruScreen and cervical cancer screening in one weekly email. We scoped a report that collects from fixed sources, removes duplicates, sends the email and keeps an archive.

  • A fixed list of sources checked every week
  • Duplicates removed before the report is written
  • The report emailed on a schedule and archived for later reading
  • Acceptance tests and a note that the report is not medical advice

  What changed: The statement of work set out what the report collects, how duplicates are caught and when it arrives: one clean weekly email, with the archive behind it.

  Stack: PythonWeb extractionScheduling and monitoring

• A lead-generation plan for a Saudi cybersecurity firm, with a partnerSecureYes, a Saudi cybersecurity firm, with our partner Unilakes. July to August 2025.

  SecureYes wanted a steady flow of meetings with Saudi buyers of cybersecurity. With our partner Unilakes, we scoped one program across email, LinkedIn, social, paid ads and SEO, and wrote the statement of work to run it.

  • A statement of work for the lead-generation program, July 2025
  • Saudi email outreach, LinkedIn and Sales Navigator prospecting, organic social, LinkedIn, Meta and Google Ads, and SEO, in one plan
  • Delivery split with our partner Unilakes

  What changed: SecureYes had one plan across every channel it wanted and a statement of work to run it against.

  Stack: Apollo.ioGoogle AdsMeta AdsSemrush

• Merchant Center kept healthy for a jewelry brand, until hand-overKay Luxe, a jewelry brand. December 2025 to February 2026.

  Kay Luxe's shopping listings needed someone watching Merchant Center for disapprovals, feed errors and what the traffic was doing. We ran it for three months and handed it over when the brand took the account in-house.

  • Monthly performance reporting from Merchant Center
  • Feed and listing fixes as disapprovals appeared
  • A clean hand-over when the brand took the account in-house

  What changed: Listings stayed live and the brand had a monthly read of its shopping traffic, until it took the account over itself.

  Stack: Google Merchant CenterShopify

• An SEO audit for a design studio search had not foundCircora Studio, a design studio. October 2025.

  Circora Studio was competing with agencies whose sites had years of search history behind them, and had no read on where it stood. We ran a Semrush-based SEO audit of its site and a named competitor set and wrote the findings up as a document it could act on.

  • A crawl-based SEO audit of the studio's own site: authority, indexed pages and the terms it could realistically compete for
  • A named competitor set, read against the same measures
  • The findings written up as a document, with a follow-on lead-generation proposal

  What changed: The studio stopped guessing where it stood and had a document that said so, including the parts that were unflattering.

  Stack: SemrushSearch Console

• An email-infrastructure review for a partner's outbound teamA partner's outbound team. February 2026.

  A partner running cold outreach wanted an outside read on its sending infrastructure before scaling it. One session in February 2026 became a written review of the workspace, the sending tool, the domains, the copy framework and the economics.

  • A review of the sending set-up: workspace, sending tool, domains, authentication, warm-up
  • The ideal-customer definition and copy framework reviewed against what the infrastructure could carry
  • List cleaning and reseller economics, with the numbers the partner should watch

  What changed: The partner had a written view of what would break at scale before it scaled.

  Stack: InstantlyGoogle WorkspaceApollo.io

• Retail catalog scrapers: Costco, Sam's Club, Staples and Depparts product pagesRetail ecommerce. Data pipelines.

  Scrapers that read large retailers' public product pages into clean tables: Selenium and BeautifulSoup collection, MongoDB storage, pandas cleaning, a Django admin to review rows, and a Google Sheets sync for the people who work in sheets.

  • A Selenium and BeautifulSoup collector per retailer
  • MongoDB storage, with pandas cleaning before export
  • A Django admin for reviewing products
  • Google Sheets API sync, and a sync into Shopify and WooCommerce stores

  What changed: Each catalog arrived as a clean table, and one Costco run filled a sheet with 6,652 products.

  Stack: PythonSeleniumBeautifulSoupMongoDBpandasDjangoGoogle Sheets API

• Instagram automation bots, from auto-publishing to profile analysisSocial media. Automation.

  A set of Python bots for running Instagram accounts: one recommends content, one answers comments, one publishes on a schedule, one builds ad campaigns, one analyzes profiles and one downloads media.

  • Content recommendations for what to post next
  • A comment chatbot for replies
  • Scheduled auto-publishing
  • Ad-campaign creation
  • Profile analysis and media download

  What changed: Six bots, delivered as working code in August 2023.

  Stack: Python

• Business website with a content management platform behind itProfessional services. Custom software.

  A marketing site whose content is edited by the people who own it. The public pages are a fast front end; the editing surface is a real content model with roles, drafts and a publish step, so a page change does not require a developer or a deploy.

  • Content model with typed fields
  • Draft, review and publish states with per-role permissions
  • Media library with derivative sizes generated on upload
  • Front end rendered from the content API, so an edit is live without a rebuild

  Stack: Next.jsReactWordPressREST APITypeScript

• Logix ERP, a logistics management systemLogistics. Custom software.

  An operating system for a freight business: consignments from booking through delivery, the fleet and drivers assigned to them, the rates that price them, and the invoices that follow. Built from the schema up, because the parts a logistics business gets wrong are the joins between them.

  • Consignment lifecycle from booking to proof of delivery, with status history retained
  • Fleet, driver and route assignment with availability checked at assignment time
  • Rate cards per client and per lane, applied at booking rather than at invoicing
  • Invoicing and settlement generated from the movements, not re-keyed
  • Role-based access separating dispatch, finance and management views

  Stack: ReactNode.jsPostgreSQLREST APIDocker

• Residential compound management systemProperty management. Custom software.

  The administration of a multi-unit residential compound in one place. Units with their tenancies. The rent plus service charges that come off them. Maintenance requests from report to closure. Visitor and gate records. The office ran on paper files and a shared drive before it.

  • Unit register with tenancy history, so a unit's past is readable years later
  • Rent and service charge schedules generated per unit with an arrears view
  • Maintenance tickets from resident report through assignment to sign-off
  • Visitor and vehicle records against the unit that authorised them

  Stack: ReactNode.jsPostgreSQLREST API

• Dar Realty, a real estate CRM platformReal estate. Custom software.

  A CRM shaped around how property is actually sold in the Gulf: the listing inventory on one side, the buyer and inquiry pipeline on the other, and the agent activity that connects them. Arabic and English throughout, including in the data, not only in the interface chrome.

  • Listing inventory with media, availability and per-listing agent ownership
  • Inquiry pipeline with stages, assignment and follow-up scheduling
  • Bilingual interface and bilingual content fields, right to left and left to right
  • Agent activity and pipeline reporting for management

  Stack: ReactNode.jsPostgreSQLi18nREST API

• HR management system with attendance and payrollHuman resources. Custom software.

  Employee records, attendance and payroll as one system rather than three spreadsheets and a bank file. Attendance feeds the pay run directly, so the hours that were worked and the hours that are paid come from the same source.

  • Employee register with contracts, documents and expiry reminders
  • Attendance capture with shift patterns, overtime and leave accrual
  • Payroll run computed from attendance with allowances and deductions applied per policy
  • Payslip generation and a per-run register for finance
  • Leave requests with an approval chain

  Stack: ReactNode.jsPostgreSQLPDF generation

• Offline-first restaurant point of saleHospitality. Custom software.

  A till that keeps working when the connection does not. Orders, payments and the kitchen queue run against local storage first and reconcile with the server when the link returns, because a restaurant during service cannot stop for the internet.

  • Local-first writes with conflict resolution on reconnect
  • Table, counter and takeaway order flows on the same terminal
  • Kitchen display queue driven by order state rather than paper tickets
  • Split payment, part payment and refund handled at the till
  • Day-end reconciliation per terminal and per shift

  Stack: ReactIndexedDBNode.jsPostgreSQLService workers

• Sagetap, B2B SaaS product designB2B SaaS. Custom software.

  Product design for a B2B software company: the flows, the screens and the component set behind them, delivered as a system a front-end team can build from rather than a set of pictures.

  • User flows mapped before screens were drawn
  • Component library with states, not just default renders
  • Responsive behavior specified rather than left to implementation
  • Handoff with spacing, type and color tokens named

  Stack: FigmaDesign tokensPrototyping

• Document question answering over a private document setKnowledge management. Automation.

  A chat surface over a company's own documents. Questions are answered from the document set rather than from the model's general knowledge, and every answer carries the passages it came from so a reader can check it.

  • Retrieval augmented generation over a private corpus, not a public index
  • Chunking and embedding pipeline rebuilt on document change
  • Citations returned with every answer
  • Refusal path when the corpus does not contain the answer

  Stack: PythonOpenAILangChainVector searchFastAPI

• Retrieval chatbot on Azure OpenAI and Azure AI SearchEnterprise IT. Automation.

  The same retrieval pattern built inside Azure, for an organization whose data was not permitted to leave its tenant. Indexing, embedding and inference all run on Azure services under the customer's own subscription.

  • Indexing and retrieval through Azure AI Search
  • Inference through Azure OpenAI inside the customer tenant
  • Document ingestion pipeline with incremental reindexing
  • Access scoped through the tenant's own identity provider

  Stack: Azure OpenAIAzure AI SearchPythonAzure Functions

• Caption generator on GPT-4oMarketing. Automation.

  A tool that writes post captions from an image and a short brief, in a house voice supplied as examples rather than as adjectives. Built for a team producing a high volume of social posts against a fixed tone.

  • Vision input, so the caption describes the actual image
  • Tone carried by supplied examples rather than instruction words
  • Variant generation with length and platform constraints applied
  • Batch mode for a content calendar rather than one post at a time

  Stack: OpenAI GPT-4oPythonFastAPIReact

• Multi-source stock movement modelingFinancial markets. Data pipelines.

  A model that combines price history with signals drawn from filings and news rather than from price alone. Built as a research instrument with the backtest as the deliverable, not as a trading recommendation.

  • Price, fundamental and text-derived features in one feature store
  • Regulated-source ingestion from SEC EDGAR
  • Walk-forward backtesting with the evaluation window held out
  • Feature attribution reported alongside the prediction

  Stack: Pythonpandasscikit-learnSEC EDGARPostgreSQL

• Semantic text analyticsResearch. Data pipelines.

  A pipeline that turns a large body of unstructured text into something countable: topics, entities, sentiment and the relationships between them, with the output shaped for a dashboard rather than for a notebook.

  • Entity extraction and normalization across inconsistent source text
  • Topic modeling with a labeled taxonomy rather than bare cluster ids
  • Sentiment scored per entity, not per document
  • Output written to a warehouse table the reporting layer reads

  Stack: PythonspaCyTransformersPostgreSQL

• DocTalk, a document conversation surfaceKnowledge management. Automation.

  Upload a document, then ask it questions. Built for people working through long documents one at a time rather than searching a whole library, so the retrieval is scoped to the file in front of them.

  • Per-document session scope, so answers cannot drift to another file
  • Handles long documents by chunk with overlap rather than truncating
  • Passage highlighting against the source view
  • Supports the document formats people actually send

  Stack: PythonOpenAILangChainStreamlit

• Analytics assistant over a business data setBusiness intelligence. Data pipelines.

  A question-answering layer over a company's own metrics: a plain-language question becomes a query against the warehouse, and the answer comes back with the numbers and the chart rather than as prose.

  • Natural-language question translated to SQL against a governed schema
  • Generated query shown alongside the answer so it can be checked
  • Result rendered as a table and a chart, not a paragraph
  • Schema scoped so the assistant cannot reach tables it should not

  Stack: PythonOpenAISQLBigQueryPlotly

• LangChain question answering botKnowledge management. Automation.

  A question answering service built on LangChain, with the retrieval chain, prompt templates and evaluation harness kept as code so a change to the prompt is reviewable in a pull request.

  • Retrieval chain defined in code, versioned with the repository
  • Prompt templates parameterized rather than pasted per environment
  • Evaluation set run against changes before release
  • Swappable model provider behind one interface

  Stack: PythonLangChainOpenAIVector search

• Telegram agent for writersPublishing. Automation.

  A writing assistant that lives where the writer already is. Drafting, rewriting and research run as a Telegram conversation, with per-user state so a thread picks up where it left off.

  • Conversational state held per user across sessions
  • Draft, rewrite and summarize as distinct commands rather than one prompt
  • Long output split to fit the platform's message limits
  • Rate limiting per user to keep model cost bounded

  Stack: PythonTelegram Bot APIOpenAIRedis

• Machine learning predictions augmented with GPT-4 VisionApplied machine learning. Data pipelines.

  A conventional model handled the structured features while a vision model read the images the structured data described. The two signals were combined so the prediction used what the photograph showed as well as what the record said.

  • Structured model and vision model combined at the feature level
  • Vision output constrained to a fixed schema rather than free text
  • Fallback to the structured model when the image is absent or unusable
  • Per-source contribution reported for each prediction

  Stack: PythonGPT-4 Visionscikit-learnpandas

• Healthcare regulations and compliance assistantHealthcare. Automation.

  A question answering surface over healthcare regulation, where being confidently wrong is the failure mode that matters. Answers are grounded in the regulatory text and cite the clause, and the assistant declines rather than guesses.

  • Answers grounded in regulatory source text with the clause cited
  • Explicit refusal when the corpus does not support an answer
  • Regulated-source ingestion with the effective date carried through
  • Query and answer log retained for review

  Stack: PythonOpenAILangChainVector searchFastAPI

• Meeting copilot, a SaaS assistantB2B SaaS. Custom software.

  A product rather than a script: meetings are transcribed, summarized into decisions and actions, and made searchable across the history, with the multi-tenant plumbing a SaaS needs behind it.

  • Transcription and speaker separation per meeting
  • Summary split into decisions, actions and open questions rather than one blob
  • Search across the meeting history, not only within one call
  • Multi-tenant data isolation with per-workspace access control

  Stack: PythonOpenAIFastAPIReactPostgreSQL

• Forex assistantFinancial markets. Automation.

  A conversational surface over currency market data: rates, movement over a window, and the definitions traders ask about, answered from live data rather than from the model's memory of it.

  • Live rate lookup through a market data API rather than model recall
  • Historical windows computed on request
  • Terminology answered from a curated reference set
  • Explicit statement that nothing returned is trading advice

  Stack: PythonOpenAIMarket data APIFastAPI

• Automated Power BI financial dashboardFinance. Data pipelines.

  A monthly finance pack that stopped being assembled by hand. The source data is pulled, transformed and refreshed on a schedule, and the report renders the current position without anyone opening a workbook.

  • Scheduled refresh against the accounting source, no manual export
  • Profit and loss, balance sheet and cash views from one model
  • Period comparison and variance computed in the model rather than per visual
  • Row-level security so each viewer sees their own entity

  Stack: Power BIDAXPower QuerySQL

• Reporting layer across a businessBusiness intelligence. Data pipelines.

  A reporting layer rather than a single report: a shared data model with consistent definitions, and the individual dashboards built on top of it, so two reports asking the same question return the same number.

  • One semantic model with metric definitions held centrally
  • Report set built on the shared model rather than per-report queries
  • Scheduled distribution to the people who will not open a portal
  • Definitions documented alongside the model

  Stack: Power BILooker StudioSQLBigQuery

• Business insights dashboard in Power BIBusiness intelligence. Data pipelines.

  An operating view for a management team: the measures that matter, at the grain decisions are taken, with drill-through from the summary to the transactions behind it.

  • Summary tiles that drill through to the underlying rows
  • Filters that persist across pages so a question survives navigation
  • Measures written once in the model and reused across pages

  Stack: Power BIDAXPower Query

• KPI dashboardBusiness intelligence. Data pipelines.

  A dashboard built around a defined set of indicators with targets attached, so a reading is against a bar rather than against nothing.

  • Each indicator carries its target and its trend
  • Period-on-period comparison in the model
  • Alert thresholds surfaced visually rather than buried in a table

  Stack: Power BIDAXSQL

• Customized KPI dashboard in Power BIBusiness intelligence. Data pipelines.

  A KPI dashboard fitted to one organization's own definitions rather than to a template, including the awkward measures that no standard report carries.

  • Metric definitions taken from the client's own policy documents
  • Non-standard measures implemented in DAX rather than approximated
  • Layout organized by team, so each audience finds its own section

  Stack: Power BIDAXPower Query

• Bicycle chain performance visualization in RManufacturing. Data pipelines.

  Test data from a component testing program turned into a set of statistical graphics: distributions, wear curves and comparisons across product variants, produced as a reproducible script rather than a spreadsheet chart.

  • Reproducible plots generated from raw test output
  • Distribution and variance shown rather than averages alone
  • Variant comparison on shared axes

  Stack: RRStudioggplot2

• Customer segmentation dashboardRetail. Data pipelines.

  A segmentation built from behavior rather than assumption: transaction history clustered into groups, then rendered as a dashboard where a marketer can see who is in each group and what changed since last period.

  • Clustering over recency, frequency and value from transaction history
  • Segment membership refreshed on schedule, not fixed at build
  • Migration between segments shown period on period

  Stack: Pythonscikit-learnPower BISQL

• Vendor performance dashboardProcurement. Data pipelines.

  A view of suppliers against the terms they agreed to: delivery timeliness, quality returns, price variance and responsiveness, ranked so a procurement conversation starts from the record.

  • On-time delivery measured against the agreed date, not the revised one
  • Quality returns and price variance attributed per vendor
  • Vendor ranking with the components of the score exposed

  Stack: Power BIDAXSQL

• Key metrics report in Looker StudioBusiness intelligence. Data pipelines.

  A shared report for a team that lives in Google Workspace: connected directly to the sources they already use, scheduled to their inboxes, with no new tool to log into.

  • Connected to Google Analytics, Sheets and BigQuery sources directly
  • Scheduled email delivery to stakeholders
  • Blended sources so one chart can span two systems

  Stack: Looker StudioBigQueryGoogle AnalyticsGoogle Sheets

• Power BI dashboard developmentBusiness intelligence. Data pipelines.

  Dashboard development as a delivery rather than a demo: the data model, the transformations, the measures and the report, handed over with the model documented so the client's own analyst can extend it.

  • Star schema modeled before any visual was placed
  • Transformations in Power Query rather than in the source
  • Measures documented so the model can be extended without the author

  Stack: Power BIDAXPower QuerySQL