How the work actually happens.

Walk through three engagement blueprints — the challenge, the architecture, the timeline and the measures we would track — so you know exactly what working with us looks like.

TransparencyThese are illustrative blueprints, not client case studies. We publish verified client stories only with permission — they will appear here as they are approved.

01 Blueprint · E-commerce · Web · SEO

Re-platforming a growing D2C store

Challenge

A direct-to-consumer brand has outgrown its template store: pages slow down during sales, catalogue edits need a developer, and stock is updated by hand across channels.

Business context

Revenue depends on campaign days, so downtime or lost search rankings during migration would be costly. The team is small and needs to run the store without technical help.

Solution

A headless storefront on a modern framework, connected to inventory and order systems through APIs, with an editor-friendly admin and a redirect plan that protects existing rankings.

Interface concept · illustrative

Project walkthrough

From brief to launch.

Weeks 1–201

Audit & plan

Funnel analysis, URL inventory, integration map and success measures agreed.

Weeks 3–502

Design

Mobile-first product, listing and checkout designs tested with real shoppers.

Weeks 6–1103

Build & integrate

Storefront, admin and integrations built in two-week cycles.

Week 1204

Migrate & launch

Data migrated, redirects verified, launch outside peak hours.

Ongoing05

Optimise

Search tuning, speed work and A/B tests on key steps.

Architecture

ExperienceNext.js storefront · Search & filters · Mobile-first checkout

CommerceCatalogue & pricing · Cart & orders · Payment gateways

IntegrationInventory sync · Courier APIs · Marketing tools

PlatformCDN & edge caching · CI/CD · Monitoring

Technologies

Next.jsHeadless commercePayment gatewayREST APIsCloudflareAnalytics

Key features

Typo-tolerant search
Real-time stock across channels
One-page checkout
Editor-managed landing pages
Automated order status emails

Outcomes we would measure

Page load time on mobile
Checkout completion rate
Organic traffic retained after migration
Hours spent on manual stock updates

Measures, not promised results.

02 Blueprint · Mobile · API · Cloud

A field-operations app for service teams

Challenge

Technicians receive jobs over phone calls and messaging apps, record work on paper, and the office re-types it all for billing — days later.

Business context

Sites often have weak connectivity, technicians range widely in comfort with technology, and billing depends on accurate job records.

Solution

A cross-platform mobile app that works offline, a dispatch console for the office, and an API layer that pushes completed jobs straight into billing.

Interface concept · illustrative

Project walkthrough

From brief to launch.

Weeks 1–201

Ride-alongs

Shadowing technicians to understand the real workflow on site.

Weeks 3–402

Prototype

Clickable app tested by technicians in the field.

Weeks 5–1003

Build

App, console and sync engine developed and tested on low-end devices.

Weeks 11–1204

Pilot

One team runs the app alongside paper for two weeks.

Week 13+05

Roll-out

Training, phased roll-out and support.

Architecture

FieldReact Native app · Offline storage · Photo & signature capture

OfficeDispatch console · Job board · Reports

ServicesJob API · Sync engine · Notifications

IntegrationBilling system · Maps · Customer SMS

Technologies

React NativeNode.jsPostgreSQLOffline syncPush notificationsAWS

Key features

Offline job checklists
Photo and signature capture
Live job status for the office
Automatic billing hand-off
Customer arrival SMS

Outcomes we would measure

Time from job completion to invoice
Jobs recorded without re-typing
Technician adoption after pilot
Missed or duplicate jobs

Measures, not promised results.

03 Blueprint · AI · Automation · Integration

Automating document intake with AI

Challenge

An operations team receives hundreds of forms and invoices by email each week and enters every field manually into two separate systems.

Business context

Documents vary in layout and quality, errors have financial consequences, and the team needs to trust the system before relying on it.

Solution

An intake pipeline that reads documents, extracts and validates fields against existing records, and places each one in a review queue — with confidence scores that decide what needs a human look.

Interface concept · illustrative

Project walkthrough

From brief to launch.

Weeks 1–201

Sample & measure

A representative document sample is collected and current handling time measured.

Weeks 3–502

Accuracy pilot

Extraction tested on the sample; field-level accuracy reported honestly.

Weeks 6–903

Pipeline build

Review queue, validation rules and integrations built.

Weeks 10–1204

Supervised run

Every document approved by a person while accuracy is tracked.

Later05

Selective autonomy

High-confidence documents processed automatically; exceptions reviewed.

Architecture

IntakeEmail & upload capture · OCR & layout parsing · Classification

IntelligenceField extraction · Validation rules · Confidence scoring

ReviewApproval queue · Side-by-side view · Corrections feedback

SystemsAccounting API · ERP API · Audit log

Technologies

PythonOCRLLM APIValidation rules engineREST integrationsAudit logging

Key features

Multi-format document capture
Field extraction with confidence scores
One-click approval
Automatic posting to systems
Full audit trail

Outcomes we would measure

Minutes of manual entry per document
Field-level extraction accuracy
Share of documents needing correction
Time from receipt to posting

Measures, not promised results.

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