Industry

Thegapbetweenwhatcustomersexpectfromomnichannelretailandwhatmostretailerscanactuallydeliverisadataproblem,notastrategyproblem.

Customers want to check store availability online, buy wherever is convenient, return anywhere, and have staff recognise them across channels. All of that requires inventory, customer identity, and order data to exist in one place and stay accurate in real time. That is the infrastructure problem most retailers are actually working on.

See how we work

Industry_Focus

Unified Commerce

Cross-Channel

Inventory Sync

Customer Journey

Industry Analysis

What We Know

The reality of modern infrastructure, unpacked.

Operational Reality

Omnichannel retail is genuinely hard to execute because most of the systems that handle different parts of the retail operation — e-commerce platform, point of sale, ERP, loyalty programme, fulfilment management — were acquired at different times, from different vendors, and were designed to work independently rather than together. Inventory that is accurate in the e-commerce system may not reflect what is actually on the warehouse floor because the two systems sync on a schedule rather than in real time. A customer's purchase history in-store is invisible to the e-commerce recommendation engine because the two systems share a customer name but not a customer identity.

The Technology Gap

The most common gap is not a missing feature — it is a missing shared record. A unified inventory record that every channel reads from and writes to in real time. A customer identity that connects the in-store transaction, the app session, and the website visit to the same person. An order record that knows whether the customer's item is being picked from the store shelf or shipped from a warehouse, and that communicates that status to both the customer and the store staff. Building these shared records requires connecting systems that were designed to be isolated — and the integration work is more demanding than it appears, because every system has its own data model, its own latency tolerance, and its own failure modes.

The Human Cost

A store associate who cannot tell a customer whether the size they need is available at another location because the inventory system only shows what is in that store. An online customer who drives to a store to pick up an order marked as ready, and finds the item is not actually there because the online inventory was last updated four hours ago. A customer service team manually processing refunds for online orders returned to stores because the return was entered in the POS but the e-commerce system has no record of it. These are the daily operational costs of omnichannel infrastructure that has not been connected, and they are felt by customers and staff before they appear in any metric.

Focus Areas

Solving the Right Problems

We target specific workflows where manual effort meets its ceiling, delivering measurable, high-leverage outcomes.

Real-time inventory across channels

Inventory that is updated on a batch schedule — every hour, every four hours, overnight — will be inaccurate by the time any channel reads it. Oversells happen online because the store just sold the last unit. BOPIS orders are promised on items that are not actually in stock. Customers lose trust in inventory information and stop relying on it.

A shared inventory record updated in near real time as transactions occur across all channels — with allocation rules that prevent oversells and availability logic that reflects what is genuinely available for each fulfilment method.

Unified customer identity

A customer who shops online and in-store exists as two or more separate records in most retail systems — one in the e-commerce database, one in the POS, possibly another in the loyalty programme. Without a resolved identity connecting these records, personalisation is limited to each individual channel, and staff in-store have no context about the customer standing in front of them.

A customer identity resolution layer that connects online and in-store transaction history, loyalty activity, and browsing behaviour to a single profile — accessible to the e-commerce personalisation engine and to store associates through their tooling.

Buy online, pick up in store

BOPIS that is implemented as a workaround — an online order routed to a store email address, manually picked by a staff member, with no system-to-system integration — generates the customer satisfaction problems it was supposed to solve. Items are not ready when the customer arrives, the pickup confirmation is manual and inconsistent, and the inventory is not properly allocated until the pick actually happens.

A BOPIS workflow with inventory reservation at order time, automated pick notification to store staff, confirmation sent to the customer when the order is ready, and inventory adjustment when the item leaves the shelf.

Cross-channel returns

A customer who buys online and wants to return in-store encounters a process that is seamless for the customer but operationally fragmented — the store POS processes the return but has no connection to the e-commerce order record, the refund has to be manually processed in a second system, and the inventory adjustment may or may not happen correctly depending on which staff member handles it.

Returns processing connected across systems — the in-store return triggers the e-commerce order update, the refund, and the inventory adjustment as a single workflow rather than three separate manual steps.

Consistent pricing and promotions

Price inconsistencies between online and in-store — even small ones, even ones that are explained by different cost structures — generate customer complaints and staff awkwardness. Promotions that apply online but not in-store, or vice versa, create friction at the exact moment when a customer is completing a purchase.

A pricing engine that updates all channels simultaneously when a price or promotion changes — with the configuration to allow deliberate location-specific pricing where the business model requires it, while eliminating unintentional inconsistencies.

What We Build

Actionable Technologies

Outcomes in the reader's language, focused on actual usage.

BLD 01

Commerce platform unification

Integration of the e-commerce platform, POS system, ERP, and fulfilment management into a shared commerce backend — with a single source of truth for inventory, orders, and customer data that all channels read from and write to.

Operations teams managing multi-channel commerce; engineers maintaining the integration layer

BLD 02

Real-time inventory management

A distributed inventory system that updates stock levels across all channels as transactions occur — with allocation rules, safety stock configuration, and fulfilment priority logic that prevents oversells and enables cross-channel fulfilment decisions.

Merchandising teams, store managers, and customers checking availability

BLD 03

Unified customer data platform

Customer identity resolution connecting online behaviour, in-store purchases, loyalty activity, and app engagement to a single profile — with the privacy consent management that cross-channel data collection requires.

Personalisation and CRM teams; store associates through the associate tool

BLD 04

Order management system

Intelligent order routing across fulfilment locations based on inventory availability, cost, and delivery speed — with split order support, partial fulfilment handling, and real-time status updates across every channel the customer might check.

Operations teams managing fulfilment; customers tracking their orders

BLD 05

Store associate tools

Mobile applications for store staff covering customer profile lookup, real-time inventory search across all locations, mobile checkout, and access to the full catalogue for endless aisle ordering — replacing the experience of a staff member saying they do not know if an item is available elsewhere.

Store associates and floor managers

BLD 06

Cross-channel analytics

Customer journey analytics tracking how shoppers move across channels — which touchpoints influence purchase decisions, where channel-to-channel transitions create friction, and how omnichannel engagement correlates with retention and lifetime value.

Marketing teams and retail operations leadership

Our Approach to AI

Grounded Intelligence

Inventory forecasting models require historical data that reflects the unified channel picture — not just e-commerce history or just store history. For retailers in the early stages of unifying their data, forecasting models trained on channel-specific data will inherit the gaps in that data. We are direct about the data maturity required before forecasting models add meaningful value beyond well-configured business rules. The concern we hear most often about AI personalisation across channels is around privacy — specifically whether customers have consented to the level of cross-channel data connection that personalisation requires. We build consent management into the customer identity platform from the start: customers who have not consented to cross-channel data use receive channel-specific experiences rather than unified ones. The personalisation improves with consent, but the system functions correctly without it.

Use Case01

Inventory allocation and demand forecasting

A model trained on sales history, seasonal patterns, and channel-level demand signals recommends how to allocate incoming stock across locations — and flags locations where stock levels are likely to be insufficient before the stockout occurs rather than after. For BOPIS-heavy traffic patterns, the model adjusts allocation toward stores with higher pickup demand.

Use Case02

Cross-channel personalisation

A model that combines in-store purchase history, online browsing behaviour, and app engagement data surfaces product recommendations that reflect the customer's full relationship with the brand — not just their behaviour in the current channel. A customer browsing bedding online who recently bought pillows in-store receives recommendations that account for both signals.

Use Case03

Fulfilment routing optimisation

For orders where multiple fulfilment locations have the requested item, a model that weighs shipping cost, delivery time, in-store pickup demand, and current stock levels at each location routes each order to the fulfilment source that minimises cost while meeting the delivery commitment — rather than routing to the nearest or largest location by default.

How We Work

Our Philosophy

We start with inventory and customer identity — because everything else in omnichannel depends on having a reliable shared record for both.

PHASE 01

We map the current systems and data flows before proposing any integration

The integration challenge in omnichannel commerce is almost always more complex than it initially appears, because each system has its own data model, its own latency, and its own failure behaviour. We spend the first phase of every engagement understanding what each system does, what data it holds, how frequently it updates, and what happens when it is unavailable — before proposing how to connect them. Surprises discovered mid-integration are more expensive than surprises discovered during the assessment.

PHASE 02

We start with inventory synchronisation and validate it before building anything on top of it

Customer experience features — BOPIS, cross-channel returns, associate tools — all depend on inventory data being accurate. If inventory synchronisation is unreliable, everything built on top of it will produce wrong answers. We treat inventory accuracy as a hard prerequisite and validate it against real transaction data before any customer-facing features are enabled.

PHASE 03

We involve store operations in the design of associate tools

Technology designed for store associates by people who do not work in retail stores frequently fails the usability test of a busy Saturday afternoon. We involve store managers and associates in the design and testing of associate-facing tools before they are deployed — because the decisions that determine whether associates actually use a tool are made in the first shift it is available, not in the UAT environment.

PHASE 04

We plan for peak trading periods during the implementation, not after

An omnichannel integration that works correctly at normal trading volumes but has not been tested at Black Friday levels will discover its failure modes at the worst possible time. We design load testing, failover behaviour, and degraded-mode operation — what happens when a channel system is temporarily unavailable — as part of the implementation rather than as a post-launch activity.

Proof

Operational Metrics

Measured by operational outcomes, not just technical uptime.

0% → 97%

Inventory accuracy improvement

fashion retailer following real-time inventory synchronisation

~0%

Increase in cross-channel sales

electronics chain following pricing unification and associate tools

~0%

Increase in in-store pickup traffic

home goods retailer following BOPIS workflow implementation

Case Stories

Field Outcomes

Quiet, honest, and specific results.

Context

Case Study

A 200-store fashion retailer had separate e-commerce and in-store systems with no real-time connection between them. Inventory accuracy was roughly 68% — a significant portion of items shown as available online were not actually in stock. Customers could not return online purchases to stores because the systems had no shared order record, and BOPIS was not offered because the inventory data was too unreliable to promise availability.

Resolution

Inventory accuracy improved from roughly 68% to approximately 97%. BOPIS was launched and grew to represent around 180% of its initial target within the first year. Cross-channel returns were available for the first time, and customer satisfaction for returns improved by approximately 42%. Overall revenue increased by around 23% in the first year — attributed to both the BOPIS addition and the reduction in online cart abandonment caused by inaccurate availability information.

Context

Case Study

A consumer electronics chain had a showrooming problem — customers would examine products in-store and then buy online, often from a competitor, because they could not confirm the in-store and online prices were the same. Store associates had no visibility into online purchase history and could not provide continuity for customers who had engaged with the brand online before visiting the store.

Resolution

Price consistency eliminated the specific price-discrepancy complaint that had been the primary stated reason for buying elsewhere. Associates reported that having access to purchase history changed the nature of in-store conversations — they could reference what a customer had already bought rather than starting from zero. Cross-channel sales increased by roughly 67%. Customer lifetime value across the combined base grew by approximately 31%.

Context

Case Study

A home goods retailer wanted to launch BOPIS but their Shopify storefront and custom POS system had no API connection. Store staff were manually checking emails for online orders, manually picking and setting aside items without any system update, and manually sending pickup confirmation messages. Daily inventory discrepancies were causing both oversells and items being set aside for orders that had already been cancelled.

Resolution

Manual inventory reconciliation work was eliminated. Customer satisfaction scores for the fulfilment experience improved by roughly 58%. In-store traffic attributable to online order pickup increased by approximately 140% in the six months following launch — with a meaningful proportion of those pickup visits resulting in additional in-store purchases.

Strategic Domains

Segments We Serve

System SegmentFashion and apparel

Inventory management across sizes, colours, and locations — with the BOPIS and cross-channel returns workflows that fashion customers expect and the in-store associate tools that make selling across the full catalogue possible.

Engagement

Flexible Models

Ref // 01

Verified

Discovery and assessment

A two-week review of current systems, data flows, inventory accuracy, and the specific omnichannel gaps that are creating the most customer and operational friction. Output is a prioritised roadmap with honest estimates of integration complexity and dependency.

Ref // 02

Verified

Platform integration

An 8–12 week integration engagement starting with inventory synchronisation and customer identity — the foundation — before adding order management and fulfilment capabilities. Timeline depends on the number of systems in scope and their integration surface.

Ref // 03

Verified

Store enablement

A 4–6 week rollout of associate tools and in-store technology — with store staff involved in the design and pilot, training built around real workflows, and the change management support that makes technology adoption stick.

Ref // 04

Verified

Ongoing optimisation

Continued involvement after launch — integration monitoring and incident response, new channel additions, fulfilment logic refinement based on operational data, and feature development as the omnichannel strategy evolves.

Security

Rigorous Compliance

Enterprise-grade security embedded at the core.

Secure by design.

Enterprise-grade controls, rigorous compliance baselines, and delivery discipline woven into the architecture from day zero.

Audit Ready

Payment security

All payment processing across channels — online, in-store, and mobile — is handled through PCI DSS compliant infrastructure. Payment card data is tokenised at capture and is not stored in the commerce backend. In-store and online payment tokens are separate by design to prevent cross-channel card data exposure.

Customer data privacy

Cross-channel customer data collection requires consent management that works across all touchpoints — a customer who consents in-store should have that preference reflected online, and vice versa. We build consent management into the customer identity platform from the start, with the data processing limited to what each consent scope permits.

API and integration security

All channel integrations use OAuth 2.0 authentication, encrypted transmission, and rate limiting. API access is scoped to the minimum data required for each integration. Integration events are logged with sufficient detail for incident investigation without capturing more customer data than necessary.

Compliance

Industry Certifications

Adhering to the highest standards of security and regulatory compliance.

PCI DSS Level 1

SOC 2 Type II

ISO 27001

GDPR Compliant

CCPA Compliant

AWS Retail Competency

Technical Architecture

Engineered for scale.

Our foundational technology stack is designed around principles of immutability, deterministic performance, and zero-trust security. We deploy modern, enterprise-grade tooling to ensure every architecture we deliver is robust and extensible.

SYSTEM_HEALTH: NOMINAL

LATENCY: < 10ms

Commerce backend

Unified commerce platform handling inventory, orders, and customer data across all channels

Node.js microservices architecture with separate services for inventory, orders, customer, and pricing

GraphQL API layer for flexible, channel-specific data access patterns

Redis for sub-second inventory read performance with write-through cache invalidation on stock updates

PostgreSQL for transactional data with event sourcing for inventory state history

FAQ

Frequently Asked Questions

Everything you need to know about partnering with us and our engineering standards.

Ready to scale

Unify your operations.

Every retailer is at a different point with omnichannel — some are dealing with a specific problem like BOPIS not working reliably, some are trying to connect systems that have never been integrated, and some are starting from scratch with a new retail concept. If something on this page reflected a situation you recognise, we are glad to hear where you are. No presentation. Just a conversation about what you are working through.

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