Executive Summary

Tamimi Markets, one of Saudi Arabia's largest and longest-established grocery retailers, faced a familiar enterprise problem at an unfamiliar scale: hundreds of millions of operational data points locked behind BI queues and specialist teams, while high-volume retail work, ordering, forecasting, customer engagement — stayed manual and spreadsheet-bound. Rather than pursue a narrow point solution, Tamimi partnered with NorthBay Solutions, an AWS Premier and Anthropic partner, to build AskBaba: a bilingual (Arabic/English) agentic AI platform powered by Anthropic's Claude on Amazon Bedrock, running inside Tamimi's own AWS environment and delivered where employees already work, in Microsoft Teams.

The result is a business milestone as much as a technical one: Tamimi Markets is the first retailer in the Kingdom of Saudi Arabia to run agentic AI at enterprise scale, not a pilot, not a single chatbot, but a governed, production platform of 19+ specialized Claude-powered agents that answer questions in seconds, automate daily purchase ordering, forecast demand, and re-engage customers. AskBaba delivers measurable value across every persona in the organization — store managers, category buyers, HR, IT, finance, and executive leadership and across every operational stream, from analytics and procurement to forecasting and customer engagement, all while keeping enterprise data under Tamimi's own governance. Claude powers both what AskBaba does for the business and how NorthBay designed, built, and delivered it.

Results at a Glance

Note: figures reflect observed production usage; select figures are being validated with Tamimi Markets during Anthropic's reference and consent process.

The Challenge

Tamimi Markets runs a nationwide network of stores generating enormous operational data, hundreds of millions of transaction line items across sales, inventory, stock movement, promotions, and procurement, flowing from SAP S/4HANA into an Amazon Redshift data warehouse of more than 130 tables. The data existed; access to it did not.

Store managers, category buyers, and executives depended on BI dashboards and analyst queues for even routine questions, how a store performed yesterday, which products were running low, whether a promotion moved volume. Answers took hours or days, arrived in English only, and required desk access. Meanwhile, high-volume operational processes such as demand forecasting, competitor price tracking, and customer win-back campaigns were manual, spreadsheet-driven, and dependent on a small group of specialists. Daily purchase-order creation, by contrast, already ran on TIPS, Tamimi’s own proven ordering platform, and one of the retailer’s strongest existing systems with room to add a real-time validation layer as volume and complexity grew.

Tamimi Markets wanted a single, trustworthy assistant its employees could talk to: one that understood retail language in both Arabic and English, answered from governed enterprise data rather than guesswork, respected each employee's role and permissions, and could safely take action, not just report. It had to serve every persona in the business, from the store floor to the boardroom, run inside the tools employees already use (Microsoft Teams), and scale from a pilot to the entire organization.

The Solution: An Agentic Platform Built on Claude & AWS

NorthBay Solutions designed AskBaba as an agentic AI platform with Claude at its core, running on Amazon Bedrock inside Tamimi Markets' own AWS environment, keeping the client's enterprise data under the customer's governance, so every persona gets a different, role-appropriate answer from the same trusted foundation.

A supervisor–specialist agent architecture

Rather than a single monolithic bot, NorthBay built a LangGraph (Day 1 Architecture and progressing towards an AgentCore architecture) multi-agent system in which a Claude-powered supervisor understands each request, then routes it to one of 19+ specialist agents, sales analytics, inventory, stock movement, sales budgets, product catalog, competitor pricing, predictive forecasting, purchase-order creation and inquiry, HR knowledge, IT incidents, business events, weather, scheduled reporting, and more. Each specialist combines Claude's reasoning with governed tools: text-to-SQL over the Redshift warehouse, retrieval-augmented generation (RAG) over HR and policy documents, ServiceNow ticketing, SAP purchase-order APIs, and Airflow job scheduling. Agents complete their task or escalate back to the supervisor, so conversations flow naturally across domains.

Why Claude

Reasoning over enterprise schemas. Claude reliably translates ambiguous, bilingual retail questions into precise SQL over a 130+ table warehouse, respecting business rules (posting vs. transaction dates, fresh vs. non-fresh classification, store hierarchies).

• Multimodal understanding. Claude's vision capability reads product photos, shelf images, and competitor brochures; voice queries flow through transcription into the same agents.
• Arabic + English fluency. One assistant serves the whole workforce, detecting and responding in each user's language.
• Safety and control. Amazon Bedrock Guardrails, role-based access control enforced at the agent layer, and prompt-injection hardening keep responses grounded and appropriate for the workplace.
• The Solution: An Agentic Platform Built on Claude & AWS

Built and delivered with Claude

The platform is built on AWS with infrastructure-as-code (Terraform), CI/CD with environment promotion and production deploy authorization, SageMaker ML pipelines for demand forecasting with SHAP explainability, and event-driven integration (SQS, Lambda, Airflow) to SAP and messaging channels. NorthBay's delivery team also adopted Claude in its own engineering workflow, using Claude-assisted development with mandatory human review, plan-first tickets, and self-review gates. In effect, Claude powers both what AskBaba does for Tamimi Markets and how NorthBay built and delivered it, accelerating architecture, coding, and testing from the first release through every extension phase, shortening time-to-production without compromising the engineering rigor an enterprise platform demands.

Delivery & Adoption

AskBaba is in production and has been serving Tamimi Markets employees since 2025, delivered through four major releases and two funded extension phases, evidence of sustained customer commitment rather than a one-off pilot.

• Production platform. Live across development, QA, and production environments on AWS, with formal release management, a 12-week software-quality-assurance cycle, and a hotfix-governed production freeze discipline.
• Where employees work. Delivered inside Microsoft Teams (with an embedded web chat option), so adoption required no new application, employees message AskBaba like a colleague.
• Broad functional adoption. Natural-language sales/inventory/procurement analytics; HR-policy Q&A via RAG; ServiceNow incident creation from chat; a real-time validation layer on daily TIPS purchase-order creation, with SAP integration; ML demand forecasts with explainable drivers; image, barcode, and voice inputs; and administrator-composed broadcast messaging.
• Operational governance. Enterprise security program (IAM least-privilege, KMS encryption, secrets management, OIDC-only pipelines), PDPL-aligned data handling, and full auditability of agent actions.

Business Impact

AskBaba has changed the day-to-day economics of data access and retail operations at Tamimi Markets:

• From days to seconds. Routine analytics questions that previously waited on BI/reporting queues are now answered in under 30 seconds, self-service, 24/7, in Arabic or English.
• Growing daily usage. Adoption grew more than 40% within months of launch (from ~100 to 140+ daily active employees) across stores and head office.
• Half a billion+ records at conversational reach. The assistant reasons over 500M+ transaction line items and 130+ warehouse tables previously accessible only to data specialists.
• TIPS, validated. AskBaba adds a real-time validation layer on top of TIPS, Tamimi’s proven purchase-order platform, cross-checking daily purchase-order creation across the store network and giving every PO an explainable rationale freeing buyer hours for higher-value work.
• Forecasting with accountability. SageMaker demand forecasts with SHAP explainability inform ordering decisions, with drivers (events such as Ramadan and Eid, weather, promotions) visible to the business.
• Faster employee communications. Administrators broadcast rich announcements to 1,000+ recipients across Teams and web chat in minutes, with per-recipient delivery tracking.

Value Across Business

AskBaba's supervisor-agent architecture means the same platform serves every persona in the business differently — each getting the analytics, automation, or knowledge relevant to their role, without separate tools or integrations to manage.

What's Next?

Tamimi Markets and NorthBay Solutions continue to extend the platform on the same Claude-on-Bedrock foundation: competitor price intelligence across major KSA retailers, and personalized customer re-engagement identifying dormant customers and lapsed products, matching them to live promotions, and reaching them over WhatsApp and email with consent-managed, bilingual campaigns. Each increment reuses the same governed agent architecture, shortening time-to-value with every release and extending Tamimi's first-mover position as Saudi retail's digital transformation, aligned with Vision 2030, accelerates.

About NorthBay Solutions

NorthBay Solutions is an AWS Premier consulting partner and Anthropic partner specializing in data, analytics, and generative AI. NorthBay designs and delivers production AI systems, from agentic assistants to ML platforms, for enterprises across the Middle East, North America, and beyond.

Tamimi Markets is one of Saudi Arabia’s largest grocery retail chains, operating a nationwide network of stores and serving millions of customers with a commitment to quality and value. It is the first retailer in the Kingdom to run agentic AI at enterprise scale, built end to end on Anthropic’s Claude, extending value across every persona in the business, from store operations to executive leadership.

Availability

AskBaba was designed and delivered by NorthBay Solutions on Anthropic's Claude via Amazon Bedrock. To learn how NorthBay can bring production-grade Agentic AI to your enterprise, connect with an expert or find offerings on the AWS Marketplace.

Meet Our Customer

Solution

NorthBay’s technology team supporting Grow Credit established Snowflake as the central analytical data layer.

Operational data is replicated into Snowflake, creating a dedicated analytical environment separate from customer-facing transaction processing. This gives Grow Credit a reusable foundation for reporting, analysis, and downstream workflows while reducing the need to run analytical workloads directly against production systems.

Snowflake also provides a common data layer for multiple internal and external use cases. Internal teams can use centralized data for reporting and analytics, while downstream workflows and approved partner requirements can be supported without creating separate point-to-point connections to production systems.

Role-based access and purpose-specific Snowflake environments help govern how data is accessed across different workloads, supporting controlled use of data within Grow Credit’s regulated operating environment.

The Outcome

Snowflake has given Grow Credit more than a traditional data warehouse.

The platform provides a reusable, governed data layer between operational systems and the teams, workflows, and partners that require analytical data.

This enables Grow Credit to:

• Separate analytical workloads from customer-facing transaction processing.
• Support reporting and analytics from a common data foundation.
• Reduce the need for individual integrations directly against production systems.
• Support controlled internal and external data-consumption requirements.
• Scale new analytical and data-engineering use cases from a shared foundation.

The result is a simpler and more scalable data architecture in which production systems remain focused on customer operations while Snowflake provides the centralized foundation for reporting, analytics, integrations, and controlled data sharing.

Why NorthBay?

NorthBay Solutions is an AWS Premier Consulting Partner and Snowflake AI Data Cloud Select Services Partner. With Snowflake, NorthBay helps organizations retire legacy, on-premises data warehouses and modernize onto a unified data platform running on AWS.

NorthBay's Snowflake practice spans data warehouse and data lake migration, data engineering and ETL/ELT modernization, BI and reporting, master data management, and real-time streaming, with generative AI enablement via Snowflake Cortex as an emerging capability.

Backed by 10+ years of AWS delivery experience and Snowflake platform expertise, NorthBay gives Snowflake customers a single, security-first partner to plan, migrate, and operate data and AI workloads on AWS.

Learn more at northbaysolutions.com/partners/snowflake.

Meet Our Customer

Our customer is one of Saudi Arabia's largest and longest-established grocery retail chains, operating a nationwide network of stores and serving millions of customers across the Kingdom.

As the business grew, so did its data. Every day its stores generate an enormous volume of operational detail — sales, inventory, stock movement, promotions and procurement — flowing from SAP S/4HANA into an Amazon Redshift data warehouse that now holds more than 600 million transaction line items and 125 million baskets, growing by over half a million line items each day. The company had built a genuine enterprise data asset. What it wanted next was to put that asset directly into the hands of the people running the business, in the language they think in, at the moment they need it.

The Challenge

The data existed. Access to it did not.

Store managers, category buyers and executives depended on BI dashboards and analyst queues for even routine questions — how a store performed yesterday, which products were running low, whether a promotion moved volume. Answers took hours or days, arrived in English only, and required desk access. A store manager and a member of the executive team waited in the same report queue.

At the same time, the highest-volume operational processes remained manual. Daily purchase-order creation for fresh and non-fresh goods, demand forecasting, competitor price tracking and customer win-back campaigns were spreadsheet-driven and dependent on a small group of specialists — work that consumed skilled hours without scaling.

The customer set NorthBay a deliberately demanding brief: a single, trustworthy assistant that employees could simply talk to. It had to understand retail language in both Arabic and English, answer from governed enterprise data rather than guesswork, respect each employee's role and permissions, and safely take action — not just report on it. It had to serve every persona in the business, from the store floor to the boardroom, run inside the tools employees already use, and scale from a first release to the entire organisation.

The Vision

NorthBay designed an agentic AI platform running on Amazon Bedrock inside the customer's own AWS environment, so enterprise data stays under the customer's governance and every persona receives a different, role-appropriate answer from the same trusted foundation.

A supervisor-and-specialist agent architecture

Rather than a single monolithic bot, NorthBay built a multi-agent system on Amazon ECS Fargate in which a supervisor agent interprets each request and routes it to one of nineteen specialists — sales analytics, inventory, stock movement, sales budgets, product catalogue, competitor pricing, predictive forecasting, purchase-order creation and inquiry, HR knowledge, IT incidents, business events, weather and scheduled reporting among them. Each specialist pairs model reasoning with governed tools: text-to-SQL over the Redshift warehouse, retrieval-augmented generation over HR and policy documents on Amazon Aurora Serverless v2 with pgvector, ServiceNow ticketing, SAP purchase-order APIs, and Airflow job scheduling on Amazon MWAA. Agents either complete their task or escalate back to the supervisor, so a conversation can move across domains without the employee needing to know which system holds the answer.

Reasoning that holds up against a real enterprise schema

The hard part of retail analytics is not generating SQL — it is generating correct SQL against a warehouse that encodes years of business rules. Anthropic's Claude models on Amazon Bedrock translate ambiguous, bilingual retail questions into precise queries across 130+ tables, respecting posting versus transaction dates, fresh versus non-fresh classification and store hierarchies. Vision capability reads product photos, shelf images and competitor brochures; voice queries flow through Amazon Transcribe into the same agents. One assistant detects and responds in each user's language, so Arabic and English speakers share a single platform rather than two.

Governed by design

Amazon Bedrock Guardrails, role-based access control enforced at the agent layer and prompt-injection hardening keep responses grounded and workplace-appropriate. The platform is deployed with infrastructure-as-code, CI/CD with environment promotion and explicit production-deploy authorisation, Amazon SageMaker AI pipelines for demand forecasting with SHAP explainability, and event-driven integration to SAP and messaging channels. Security follows an enterprise programme of IAM least-privilege, KMS encryption, managed secrets and OIDC-only pipelines, with PDPL-aligned data handling and full auditability of agent actions.

Outcome

The platform has been in production since 2025, delivered through four major releases and two funded extension phases, with a third in planning — sustained commitment rather than a one-off pilot. It runs across development, QA and production environments with formal release management, a 12-week software-quality-assurance cycle and hotfix-governed production freeze discipline. It is delivered inside Microsoft Teams, with an embedded web chat option, so adoption required no new application: employees message the assistant like a colleague.

The change is as much economic as technical:

• Self-service analytics. Questions that previously entered a reporting queue are answered conversationally, removing a dependency that constrained decisions at the point of operation.
• Procurement automation. Purchase orders are created automatically every morning at 08:30 KSA, integrated end to end with SAP, replacing a manual spreadsheet process and returning buyer hours to negotiation and assortment strategy.
• Forecasting with accountability. SageMaker demand forecasts with SHAP explainability inform ordering, with drivers — events such as Ramadan and Eid, weather, promotions — visible to the business rather than buried in a model.
• Enterprise data at conversational reach. Records that were the preserve of data specialists are now open to any authorised employee, under the same governance.
• Measurable internal communication. Organisation-wide announcements carry per-recipient delivery tracking, on a pipeline validated at 1,000+ recipients.

Value across the business

Because the supervisor-agent architecture separates routing from expertise, the same platform serves each function differently — without separate tools or integrations to manage. Six of seven functions are live in production today.

AWS Services

The project heavily utilized AWS services to ensure scalability and efficiency:

Why NorthBay?

From the outset this was built for daily operational use across an entire workforce — which meant release management, security review, bilingual quality assurance, and the discipline to ship one use case, prove it in production, and expand from there. NorthBay is an AWS Premier Consulting Partner with an AI Consulting Services Competency and deep delivery experience in the region, and applied the same engineering rigour to an agentic system that it would to any enterprise platform: governed data access, explainable automation, and a production operating model from the first release.