Industries / Retail & Consumer Packaged Goods

Inventory and demand that don't wait for a quarterly review.

Retail and CPG operate on thin margins across thousands of locations, where demand shifts by the day and customer experience is set by e-commerce leaders, not category peers. We build agents that keep forecasting and operations current in real time.

400 stores forecasted in real time Regional retail & CPG client
100% of agent actions logged, replayable & reversible
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Pakistan
UAE
USA / Pakistan
USA
UK
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Qatar
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UAE
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UAE
USA / Pakistan
USA
UK
Pakistan
Qatar
Pakistan
UAE
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UAE
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UAE
Pakistan
Qatar
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UAE
USA
Pakistan
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USA
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Singapore
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USA / Pakistan
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Australia
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Denmark
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China
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Angola
USA
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UAE
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USA
UK
Pakistan
UAE
Singapore
USA
USA / Pakistan
USA
Australia
USA
Denmark
USA
China
USA
Angola
USA
Pakistan
UAE
Pakistan
Pakistan
Pakistan
Pakistan
Pakistan
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KEY CHALLENGES

Demand swings that outpace manual forecasting
Seasonal and promotional spikes break static forecast models.
Inventory visibility across thousands of locations
Stockouts and overstock happen when data lags reality.
Store-level operations that vary site to site
One playbook rarely fits every format and market.
Customer experience set by e-commerce leaders
Shoppers compare every brand to the fastest one they've used.
Systems that weren't built for real-time decisions
Batch-processed data can't support same-day operational calls.

WHERE WE START

Data Analytics & GenAI

Data Analytics & GenAI

Real-time demand forecasting across hundreds of stores, updated as sales data comes in rather than on a weekly batch cycle.

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OUR RETAIL TECHNOLOGY EXPERIENCE

Microsoft Dynamics 365
Microsoft Azure
AWS
Snowflake
Shopify
400 stores forecasted in real time

Regional retail & CPG client

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Ten industries. One audited standard.

Different regulations, different systems of record — the same discipline every time: agents that run inside guardrails and leave a record you can read.

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Retail & Consumer Packaged Goods case studies

Challenge, solution, results. Four engagements in this sector.

Anonymised at the client's request. Each card states the pain point, what we built, and what changed. Plus the service type and country.

04 studies
Web UK

Replatforming a high-street homewares retailer's online shop

High-street homewares retailer, UK

Challenge
The retailer's web shop ran on a heavily customised platform that had fallen two major versions behind. Page loads averaged over four seconds on mobile, promotions needed a developer to set up, and the Christmas peak had taken the site down twice in the previous two years.
Solution
We rebuilt the storefront as a headless front end on a composable commerce back end, migrated catalogue and order history, and moved hosting to a managed cloud platform with autoscaling. Merchandisers now run promotions themselves. We load-tested to three times the previous peak before cutover.
Results
  • Mobile page load down from 4.2 seconds to 1.3
  • First peak season handled with 0 minutes of downtime
  • Promotion set-up moved from developer tickets to 15 minutes in admin
Service type · WebCountry · UK
AI Agents USA

Returns-fraud review agent for a sporting goods chain

Regional sporting-goods chain, Pacific Northwest

Challenge
Two loss-prevention analysts reviewed every online return over $150 by hand, cross-checking order history, return frequency and shipping addresses across three systems. The queue ran two to three days behind, so refunds were either delayed for honest customers or issued before anyone had looked.
Solution
We deployed an agent that gathers the evidence for each flagged return, scores it against the chain's own written rules, and either clears it or writes a short case note for an analyst. Analysts keep the final decision on every hold, and each decision feeds back into the rule thresholds.
Results
  • Review backlog cleared from 2.5 days to same-day
  • Analyst time per case cut from 11 minutes to 3
  • Refunds to honest customers issued 2 days faster on average
Service type · AI AgentsCountry · USA
AI MVPs USA

Demand-forecast MVP for a mid-market snack manufacturer

Mid-market snack manufacturer, Pennsylvania

Challenge
Weekly production planning was based on last year's volumes adjusted by gut feel, so the plant either ran short on promoted lines or built stock that expired in the warehouse. The planning team had retailer sell-through data but no practical way to combine it with promotion calendars.
Solution
Over nine weeks we built a forecasting MVP that joins retailer sell-through, the promotion calendar and weather history at SKU and depot level, then produces a weekly plan with a plain-language note explaining each significant change. Planners review and override before anything reaches the production schedule.
Results
  • Forecast error on promoted SKUs down 27% over the pilot
  • Write-offs from expired stock reduced by roughly $180k a year
  • Weekly planning meeting shortened from 3 hours to 45 minutes
Service type · AI MVPsCountry · USA
Apps USA

Store-audit Power App for a grocery co-operative

Regional grocery co-operative, Upper Midwest

Challenge
District managers audited stores with a paper checklist and a phone camera, then typed results into a spreadsheet back at the office. Photos were rarely attached to the right line, follow-ups were lost, and head office could not say which stores had open food-safety actions without calling each district.
Solution
We built a Power App for tablets that walks the manager through the checklist, attaches photos to each item, works offline, and creates follow-up tasks assigned to the store manager with due dates. A Power BI dashboard shows open actions by store and category for head office.
Results
  • Audit write-up time cut from 90 minutes to 10 per store
  • Open food-safety actions closed 5 days faster on average
  • Rolled out to 38 stores in 7 weeks
Service type · AppsCountry · USA

Named client work in Retail & Consumer Packaged Goods.

Real, named engagements — web, app, and UX delivery, not composites.

RDX Sports logo

RDX Sports

A global combat-sports gear brand got a web experience built for its catalogue.

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MoltyFoam logo

MoltyFoam

Pakistan's leading mattress brand modernised its web presence and storefront.

Read case study →
Raulde logo

Raulde

A UK decorating-tools brand got a web build to match its retail range.

Read case study →
Master Paints logo

Master Paints

One of Pakistan's largest paint manufacturers got a catalogue-ready web build.

Read case study →
IM Hardware logo

IM Hardware

A Lahore home-hardware retailer took its fittings catalogue online.

Read case study →
Dvago logo

Dvago

A rapid-delivery online pharmacy got its web and app experience rebuilt together.

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Posch Care logo

Posch Care

A Karachi skincare brand got a storefront to match its formulations.

Read case study →
Searle logo

Searle

A pharmaceutical manufacturer since 1965 modernised its public-facing site.

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Wani Bakhoors logo

Wani Bakhoors

A bakhoor and home-fragrance brand got a web storefront built for the category.

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Pastels Salon logo

Pastels Salon

A Dubai salon group got a booking-ready web presence.

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Glam Gas logo

Glam Gas

A Karachi kitchen-appliance brand got a catalogue site built for comparison shopping.

Read case study →
Think Crucial logo

Think Crucial

A US appliance-parts retailer got a catalogue built for exact-fit search.

Read case study →
Naheed.pk logo

Naheed.pk

A supermarket chain since 1971 took its full range into e-commerce.

Read case study →
Fresh St logo

Fresh St

A grocery retailer got a web storefront built for everyday reordering.

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Shan Foods logo

Shan Foods

A spice brand exporting to 70+ countries got a web presence to match.

Read case study →
Dolmen Mall logo

Dolmen Mall

Pakistan's largest mall chain got a digital front door to match its footprint.

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Myle logo

Myle

A US consumer goods brand got a web build for age-gated retail.

Read case study →
Ginyaki logo

Ginyaki

A build-your-own-bowl restaurant chain got a web and app experience for ordering.

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FAQ

Retail & Consumer Packaged Goods + agentic AI: questions, answered.

How can AI agents improve demand forecasting for retail and CPG?

AGS agents forecast demand in near real time, updating as point-of-sale data arrives instead of waiting for a weekly batch run. Seasonal and promotional spikes that break static models are absorbed as signals come in. One regional retail and CPG client now has 400 stores forecasted in real time, with every model action logged and reviewable.

What retail operations can AGS automate with autonomous agents?

AGS automates replenishment, inventory rebalancing, pricing, and promotion execution across thousands of locations. Agents tied to live POS and warehouse data reorder and move stock before a shelf goes empty, and adjust prices from governed data automatically. Every action is logged and reversible, so store and category teams keep full oversight of what agents change.

Can agents prevent stockouts and overstock across many stores?

Yes. AGS agents watch live inventory and sales signals and rebalance stock across stores and channels before a location runs short or piles up excess. Because they act on real-time data rather than a lagging batch view, they close the gap between demand signal and action. Each move is recorded, so exceptions stay easy to trace.

How do autonomous agents handle retail pricing and promotions?

AGS agents ground every pricing and promotion decision in governed data and execute the change automatically across the network. Margins under pressure get protected without waiting for a quarterly review or a manual repricing cycle. Human-in-the-loop gates hold on sensitive moves, and each decision is logged and reversible so merchandising teams retain control.

Can AI agents modernise retail customer service without losing brand control?

Yes. AGS agents resolve common customer service requests at the speed shoppers now expect from e-commerce leaders, handing off cleanly on anything that needs a person. Agents run under least-privilege access and log every action, so response times improve while brand voice and policy stay enforced. Certified to ISO 27001 and SOC 2.

See what real-time forecasting looks like.

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