Build dependable bookings, applications and transactions that survive real-world interruptions. We connect the interface, data and business rules behind the task, then verify the complete workflow.
What’s included.
Three steps.
Understand
Agree the task, baseline, source systems and delivery boundary.
Build
Develop and review the code, configuration and integrations in scope.
Verify
Test representative cases, compare with the baseline and hand over.
Recognise the challenge?
Start with the problem you see. Explore what we can change, then open the implementation notes for the detail.
01Complex checkout exception repair
What you may be seeing
- Checkout rejects valid addresses or customer details.
- A payment retry loses the cart or form information.
- Some browsers or customer types cannot place an order.
- A failed payment sometimes creates duplicate orders.
- Customers see an error without a useful recovery route.
We repair the exceptions that stop complex orders from completing, including payment, pricing and stock conflicts.
Implementation & verification
We reproduce checkout failures using affected address, device, payment and account combinations. We repair validation, session/cart persistence and payment-state handling in the frontend and backend. We make retry and order creation idempotent and reconcile asynchronous payment confirmation with the order record. We test failed, delayed and interrupted transactions so customers can recover without duplicate charges or lost information.
How we verify the work
- Trace an affected attempt across validation, payment and order creation using shared identifiers.
- Preserve entered data and model pending, failed and confirmed payment states explicitly.
- Test retries, webhook reordering, expired sessions and international address edge cases.
- Measure valid checkout success, unrecovered errors and duplicate order/payment incidence.
Tools considered for this work
Delivery: commerce/frontend/backend development + payment integration configuration. Platforms: existing Shopify/WooCommerce/custom checkout; changes depend on permitted checkout extensibility. Payment: Stripe or the current processor's test mode, webhook logs and retry APIs (controlled connections between applications). Diagnosis: browser/network traces and application error logs. QA: Playwright checkout matrix and idempotency/state-transition fixtures.
02Save-and-resume customer applications
What you may be seeing
- Customers lose long application answers when they leave.
- People cannot continue the application on another device.
- An expired session erases their work.
- Two open tabs overwrite each other's changes.
- Customers do not know whether their draft was saved.
We let customers pause an application and continue from a reliable saved state.
Implementation & verification
We add durable draft saving with explicit saved-state feedback and a secure way to resume the correct application. We code incremental saves, version checks and conflict handling so concurrent tabs or devices do not silently erase answers. We restore the last confirmed draft and revalidate changed application rules when the customer returns. We test interruptions, expired authentication and device changes before connecting final submission to the existing workflow.
How we verify the work
- Separate draft, submitted and superseded states with server-side ownership checks.
- Use versioned saves and safe retry behavior; preserve meaningful conflicts for the customer.
- Recheck prerequisites on resume and prevent an old draft from overwriting a submitted record.
- Measure restored-draft accuracy, lost-work incidents and resume-to-submission completion.
Tools considered for this work
Delivery: frontend/backend/database development. Storage: existing application database or PostgreSQL/Supabase. Resume: authenticated account or scoped expiring link according to data sensitivity. Client: debounced autosave, retry queue and visible save/version state. QA: Playwright multi-tab/device and network-interruption scenarios.
03Resumable customer file-submission journey
What you may be seeing
- A large upload restarts when the connection drops.
- Customers cannot tell which files finished uploading.
- The page says complete before the server has accepted the file.
- Slow connections cause customers to abandon applications.
- Repeated retries create duplicate attachments.
We make file submissions recoverable, with clear progress, validation and a way to resume interrupted work.
Implementation & verification
We replace restart-only uploads with resumable transfer and durable upload identifiers. We code client/server coordination for chunk progress, retry, finalization and attachment creation. We distinguish transferred, validated and accepted states, and attach files to the request only after successful server-side checks. We verify interruptions and finalization retries on realistic file sizes and unstable connections.
How we verify the work
- Use durable upload/session state and resume from confirmed offsets or completed parts.
- Verify permitted types/sizes, ownership and final integrity before acceptance.
- Make finalization idempotent and clean up expired incomplete uploads.
- Measure completed accepted uploads, repeated bytes and abandonment on weak connections.
Tools considered for this work
Delivery: uploader/frontend/backend development + storage configuration. Protocol option: tus with a compatible resumable client/server. Storage option: Amazon S3 multipart upload and scoped part-upload URLs. Client option: Uppy with an appropriate tus or multipart integration. QA: network interruption, finalization retry and duplicate-attachment fixtures.
04Service-area eligibility before purchase
What you may be seeing
- Customers buy services we cannot deliver to their address.
- Staff manually check whether a location is covered.
- A postcode is accepted even though the actual address is outside our area.
- Customers discover location restrictions after payment.
- Different pages show different service-area answers.
We check service availability early so people know whether their location or requirements are supported before purchasing.
Implementation & verification
We encode approved service coverage by address, area or distance using the business's actual delivery rules. We code an early location check that explains supported services and handles uncertain or incomplete addresses. We reuse the same eligibility rules in booking or checkout and revalidate changed addresses before commitment. We test boundary locations and exception cases so the check prevents false acceptance without unnecessarily excluding eligible customers.
How we verify the work
- Choose the rule actually used operationally: administrative area, polygon or travel constraint.
- Separate address uncertainty from a definite ineligible result.
- Test polygon edges, new developments and business-approved exceptions.
- Measure incorrect acceptance/rejection and location-related cancellations or staff checks.
Tools considered for this work
Delivery: frontend/backend eligibility development + location-data integration. Coverage: business-maintained postcode lists or geographic polygons. Geospatial option: PostgreSQL/PostGIS or an equivalent spatial service. Address/routing option: existing maps/geocoding integration; Google Maps where appropriate. Integration: current service catalogue, checkout or booking API (a controlled connection between applications).
05Fresh availability display for customer decisions
What you may be seeing
- Our website shows slots or stock that are no longer available.
- Two customers reserve the same resource.
- Staff calendars and the website drift apart.
- A cached availability result is mistaken for a reservation.
- Customers finish a long journey before learning the slot is gone.
We connect availability to current records and recheck it when a customer commits to a booking or purchase.
Implementation & verification
We connect availability displays to the authoritative scheduling or inventory source and make freshness explicit. We code synchronization, cache limits and a final availability check at the point of commitment. Where supported, we implement expiring holds or transactional reservations so competing customers cannot confirm the same resource. We test race conditions, cancellation updates and dependency failures rather than validating only a single happy-path booking.
How we verify the work
- Define display freshness separately from confirmed reservation semantics.
- Use holds/atomic booking writes where available and recheck before payment or confirmation.
- Recover from dropped webhooks with reconciliation and show unsupported availability as unknown.
- Measure stale offers, double confirmations and successful reservation completion.
Tools considered for this work
Delivery: backend/API (a controlled connection between applications) + frontend development; scheduler/inventory configuration. Source: existing booking/inventory system; Cal.com where it fits the service model. Consistency: database constraints/transactions or the provider's reservation API (a controlled connection between applications). Synchronization: provider webhooks plus scheduled reconciliation when needed. QA: concurrent reservation tests and stale-cache/event-delivery fixtures.
Connect the steps behind the screen.
We build the integrations that keep applications, uploads, availability and transaction states consistent. Automation resumes recoverable work and routes exceptions; AI can explain a recorded status without making up a cause.
Explore the technical toolkit
Selected around your existing stack, licences, permissions and agreed scope.
Define what better means.
Resolution
Supported tasks completed correctly without repeat contact.
Control
Verified access boundaries, confirmations and handover quality.
Effort
Manual steps and staff handling time for the agreed workflow.
Targets follow the baseline. These are measurement areas, not promised results.
Engineering terms, explained
- Grounded AI / RAG
- An assistant retrieves relevant approved evidence before generating an answer. Access controls and evaluations still need to be engineered and tested.
- Idempotency
- Retrying the same operation does not create an extra order, payment or record.
- Controlled experiment
- A concurrent comparison of assigned variants, with a defined outcome, observation window and decision rule.
- Core Web Vitals
- Measures of main-content loading, responsiveness to interaction and visual stability.
Technical references: Web Vitals · Azure document access · Structured data guidelines.
Quick answers.
Does an internal order-status assistant fit this service?
Yes. Staff self-service is part of workflow improvement. We connect the records behind the process so the assistant can explain supported facts, identify the next owner and prepare an appropriate action.
Can the assistant change records automatically?
Only within the actions you approve. Sensitive changes can require human confirmation. Permissions and business rules are enforced by the application, with records of what was requested and completed.
What if the answer is missing or a system is unavailable?
The assistant should say what it cannot verify. We design fallback states and a human handover that carries the useful context without presenting an unconfirmed action as complete.