Customer Innovation Blog articles

Building an Azure architecture that’s ready for every signature

mtoiba — Tue, 23 Jun 2026 21:07:06 GMT

At Exclaimer, we help organizations manage email signatures at scale, so every message can carry a consistent, compliant, on-brand signature without IT teams manually updating thousands of mailboxes.

This is more difficult than it may seem, especially when you're doing it for more than 80,000 customers, around 9.6 million seats, and more than 21 billion emails a year. Every signature must show up in the right place, with the right details, for the right sender, recipient, device, and business rule. Behind that are constantly changing employee records, customer-specific policies, email chains, recipient lists, regional disclaimers, and brand requirements.

Because our platform sits directly in the email flow, availability is critical. And because many of our customers operate in regulated industries, they also need confidence that data stays in-region and configured signatures are applied consistently.

To support that level of scale and reliability, we’ve spent the last several years evolving our architecture on Microsoft Azure. Today, Azure Kubernetes Service (AKS), Azure SQL Database, Azure Database for PostgreSQL, Azure Cosmos DB, Azure Data Explorer, and Azure Databricks help us run a global platform that’s more responsive, more resilient, and more cost-efficient.

Reading the signs that our architecture needed to change

In the beginning, our cloud product ran more like a multi-server, on-premises product hosted on Azure Virtual Machines (VMs). The platform was split into a smaller number of core services, and the team relied heavily on VM-based infrastructure to keep those services running. As Exclaimer grew, our architecture had to keep pace with higher volumes, more regions, and more complex customer requirements.

Regional demand shifted throughout the day, but scaling infrastructure up and down still relied on scripts, pre-baked VMs, and operational coordination. That created more risk during maintenance and failover. We run parallel data centers in regional pairs so we can move traffic away from one site when needed. But when traffic moves, the receiving environment has to be ready to handle the full load. In the VM world, that meant someone or something had to remember to scale up standby resources at the right moment.

At the same time, our product was becoming more service-oriented. We were moving away from a smaller set of larger services toward well over 100 microservices. Every new service created more conversations about VM sizing, images, patching, and operational overhead. It was time for a model that could scale faster, run more efficiently, and reduce the amount of infrastructure work required to ship and operate the product.

Signing on to AKS for faster, more efficient scaling

By moving many workloads to Linux containers on AKS, we gained a smaller footprint, faster startup times, and a more consistent way to package and deploy services. AKS also gave us a managed Kubernetes foundation for running those containers at global scale, with autoscaling capabilities that better matched our traffic patterns.

With Horizontal Pod Autoscaler, services can react to load in seconds rather than minutes. With Cluster Autoscaler, we can add or remove node capacity based on what the platform actually needs. That means we can pack workloads onto nodes more efficiently, scale down during quiet periods, and scale up quickly when demand returns.

The operational difference is just as important. During an incident, maintenance event, or regional failover, our teams have fewer manual steps to think about. If traffic shifts, the platform can scale with it. That takes away one more thing for engineers to worry about when they should be focused on keeping the customer experience steady.

The move to containers and a more streamlined CI/CD workflow also improved our deployment cadence by making it easier to build, test, and deploy changes across the platform. In 2021, we deployed 285 changes, features, and fixes to production over the course of the entire year. Today, we deploy that many every few days.

Cost has improved, too. Since 2024, when the bulk of our migration to containerized services took place, we’ve reduced our average cost per user by about 39 percent, even as the product has grown more complex and we’ve added more capabilities for customers. We achieved that through a combination of containerized architecture, AKS autoscaling, and expanded reservations across compute and storage technologies.

Choosing the right database for the right kind of data

We started with a strong Microsoft SQL Server foundation, and Azure SQL Database remains core to our platform today. It stores critical customer configuration data and continues to give us the reliability, replication, resizing flexibility, and regional scale we need. But not every workload belongs in the same database. Customer configuration, relational service data, key-value storage, usage events, and business intelligence (BI) all have different access patterns.

That principle led us to Azure Database for PostgreSQL flexible server for one of our most important migrations. We had used Azure Table storage for a core service that needed to retrieve customer data quickly. It was cost-effective and stable for a long time, but as the product evolved, the data became more relational, and we found ourselves adding complexity in application code that a relational database could handle more naturally.

Azure Database for PostgreSQL gave us that relational model with low management overhead, fast read replicas, reserved instances for predictable workloads, and a path to future scale. After the migration, average request time for a critical service dropped from 18.6 milliseconds to 1.79 milliseconds. That’s a 90 percent improvement across a service that handles around 9 billion requests each month.

Azure Cosmos DB plays a different role, supporting key-value and document storage where we need scale, availability, low latency, encryption at rest, and straightforward dev/test support. Optimized for unstructured data and high-performance reads and writes, it gives us a highly scalable foundation for workloads that don't fit a traditional relational model. We use it to store customer assets for signatures and video branding, high-volume metadata for internal message-processing operations, audit events that help customers track account changes, and tokens used to collect data from third-party systems on behalf of customers. It also gives us a clean way to keep data and services aligned.

Azure Data Explorer solved another scaling challenge: usage and billing data. We need to be able to audit the number of messages we process for our customers so we can bill accurately, and at more than 20 billion emails a year, our previous SQL-based usage pipeline became difficult to manage. With Azure Data Explorer, we can ingest massive volumes of event data at low storage cost, connect to Azure Event Hubs, and avoid maintaining custom plumbing. That move reduced the cost of the system by around 70 percent.

Azure Databricks rounds out the picture as our BI and data platform, giving our teams a shared foundation for transformations, analysis, and reporting across product and business data.

Keeping every region ready for business

Our customers are everywhere, so our platform has to be, too.

Exclaimer runs in seven distinct geographic locations: Australia, Canada, Europe, Germany, the United Arab Emirates, the United Kingdom, and the United States. That global footprint helps us meet customer expectations around availability and data residency. Many organizations want their data to stay in-region, and Azure gives us the coverage we need to support that.

Availability is especially important because our platform is part of a live communication flow. When someone sends an email, they expect it to keep moving. Our Azure architecture helps us support that expectation across the stack. AKS lets compute scale with regional demand. Azure SQL and Azure Database for PostgreSQL support critical relational workloads. Azure Cosmos DB gives us scalable, low-latency storage for document and key-value patterns. Azure Data Explorer handles very high-volume usage ingestion without the complexity of our former custom pipeline.

Across the board, these managed Azure services reduce the amount of operational work our engineers have to carry. We can spend less time maintaining the basics and more time tuning performance, improving stability, and building the capabilities our customers need next.

Building for the future on a stronger foundation

The biggest sign that our architecture is working may be how little we have to reinvent when we build something new.

As we develop upcoming product capabilities, we already have many of the foundational pieces in place: AKS for compute, Azure Cosmos DB for state, and Azure Service Bus for messaging. We also have Azure SQL for core data, Azure Database for PostgreSQL where relational service data needs room to scale, Azure Data Explorer for high-volume event analysis, and Azure Databricks for BI tooling. Together, these services make our platform faster, more efficient, and more resilient.

Email signatures may look simple on the surface. Behind every one, there’s a set of decisions about performance, scale, data, availability, and trust. With Azure, we’ve built an architecture that helps us keep every signature moving, wherever our customers do business.

About the authors

Phil Vetter started in engineering at Exclaimer as a developer at the start of 2013, and now sits at the helm as VP of Engineering.

Lee Jones started at Exclaimer in 2013 in the IT department, and now serves as Director of Platform Engineering, managing the infrastructure and resilience of Exclaimer Cloud.

Multitude builds resilient banking platform with PostgreSQL and MySQL on Azure

Pooja_Y — Wed, 06 May 2026 18:53:20 GMT

Expanding into new markets is usually a sign that things are going well. For digital banking platforms, however, growth brings a different kind of challenge - more customers, more data, and stricter expectations around availability, security, and regulatory compliance.

At Multitude, we operate across 17 countries and deliver digital banking, credit services, payment processing, and regulatory reporting through a platform composed of more than 400 microservices. Each service encapsulates a defined business capability, including onboarding, risk assessment, collections, and compliance workflows.

Historically, our services relied on on-premises PostgreSQL and MySQL environments deployed within our own data centers, where capacity scaled vertically on shared compute and storage resources. This model created contention between unrelated workloads and limited their ability to scale independently. Expanding capacity required adding or upgrading physical hardware, which involved demand forecasting, procurement, delivery coordination, and installation within the data center.

Over time, continued growth amplified these architectural constraints. The database engines themselves remained reliable, but the surrounding infrastructure limited elasticity and domain-level isolation. As a result, sustained growth began to expose structural limits in the underlying infrastructure. "In a regulated financial environment, those constraints carried broader implications. Frameworks such as DORA and GDPR require predictable availability, controlled recovery procedures, and governed access to sensitive data. As workload demands increased, sustaining both growth and compliance required structural changes at the database layer. We decided that redesigning our data architecture was necessary to improve workload isolation, scalability, and governance alignment.

Rearchitecting data boundaries with Azure Databases

We initiated our architectural redesign by migrating database workloads to Microsoft Azure and standardizing on Azure Database for PostgreSQL and Azure Database for MySQL for core application services.

Central to this redesign was the adoption of bounded contexts. Each bounded context represents a logical business domain and encapsulates the services and schemas required to support that capability. Each domain is owned and managed by a single team, aligning technical boundaries with team responsibility and accountability. Rather than maintaining a small number of large, shared database instances, we provisioned dedicated database instances aligned to defined business domains, establishing domain-level isolation at the database layer.

Figure 1: Each bounded context represents logical business domain, bundling the services and schemas that power it.

Today, approximately 35 database instances support more than 400 microservices across the platform. Each instance may host multiple schemas serving related services within the same domain, while cross-domain database dependencies are intentionally avoided. This structure limits the blast radius of configuration changes or workload spikes and allows scaling adjustments to be applied within clearly defined domain boundaries.

While the bounded context model was a strategic architectural decision, leveraging managed database services helped us implement it by drastically reducing the operational overhead of provisioning, scaling, and maintaining independent instances across domains.

Azure Database for PostgreSQL and Azure Database for MySQL provide the managed capabilities required to sustain this model. Instances are provisioned according to the performance and storage requirements of each domain and can be adjusted as workload characteristics evolve. Compute and storage resources are scaled at the instance level, allowing capacity changes to be applied to a specific bounded context without affecting unrelated domains.

Altogether, these architectural decisions balance domain-level isolation with operational manageability. A database-per-microservice pattern would significantly increase provisioning, monitoring, and lifecycle overhead without materially improving data ownership boundaries. By grouping related services within bounded contexts, we maintain clear domain alignment while keeping the number of database instances practical to operate. As a result, data boundaries, scaling behavior, and operational controls remain consistent with business domain structures across the platform.

Operationalizing high availability and backup strategy

To support availability, we deploy Azure Database for PostgreSQL and Azure Database for MySQL with zone-redundant high availability, placing primary and standby replicas in separate availability zones within the same Azure region. Replication preserves transactional consistency, and zone separation reduces exposure to localized infrastructure failures. We periodically exercise failover procedures as part of operational validation to confirm recovery behavior under defined conditions.

Availability controls are complemented by a layered backup strategy. Azure Database for PostgreSQL and Azure Database for MySQL provide automated backups with a retention window of up to 35 days and point-in-time restore capabilities. These features allow us to restore a database to a specific timestamp within the retention window, supporting recovery from application-level errors or unintended data modifications without custom snapshot orchestration.

Together, operational backups and governed archival retention address both short-term recovery and long-term compliance obligations. Restore operations require documented justification and follow established approval workflows, ensuring that recovery actions remain controlled, traceable, and auditable.

We also enforce consistency through lifecycle management. Azure’s managed service model standardizes engine patching and version updates across environments, reducing configuration drift and minimizing manual coordination. By operating within the managed service boundary, the database team can focus on workload analysis, performance tuning, and capacity planning.

For migration and synchronization scenarios, we use Azure Data Migration Service to orchestrate controlled cutovers between database environments. Engineers validate configuration and readiness before initiating synchronization, after which Azure-managed replication then maintains data alignment until final switchover. Provisioning decisions and structural modifications remain subject to internal governance approvals to preserve change control and oversight.

By combining zone-redundant availability, structured recovery workflows, governed retention policies, and standardized lifecycle management, we operate a database layer engineered for resilience, auditability, and regulatory alignment at scale.

Compliance as an architectural property

For us, governance is embedded directly into how the platform operates, beginning at the identity layer. Access to Azure Database for PostgreSQL and Azure Database for MySQL integrates with Microsoft Entra ID, aligning database authentication with centrally managed corporate identities. Role-based access control is enforced through enterprise identity policies, providing centralized visibility into access assignments and authentication events across environments.

These controls extend into production access management. Privileged access is approval-based and time-bound, and administrative roles are not permanently assigned. Access requests follow defined workflows, and all privileged actions are logged for review under established oversight procedures, ensuring traceability of operational interventions. Database isolation reinforces these identity controls. By aligning database instances with bounded contexts, each business domain maintains a discrete data boundary at the database layer. This structure limits lateral access across domains and confines sensitive data to clearly defined ownership scopes, simplifying monitoring and audit review.

In a regulated financial environment, these architectural controls also support compliance requirements under frameworks such as DORA and GDPR. By embedding identity integration, domain isolation, and lifecycle controls directly into the platform architecture, governance becomes an operational property of the system rather than a separate procedural layer. The simplicity of this architecture is a strong driver for both auditability and security of the whole platform.

Measurable impact across engineering teams and business outcomes

Beyond improved stability, our ability to respond to growth has changed significantly since moving to Azure. In the past, expanding database capacity meant procuring hardware and planning installation in the data center. Now, capacity adjustments happen directly within Azure and can be applied to individual databases instances, allowing us to scale in near real time as workload demands change.

Maintenance effort has also decreased. Managed patching, version alignment, and automated backups have reduced the need for manual coordination and reactive capacity management. Infrastructure-level tasks that once required continuous oversight are now handled within the managed service boundary. Our DBAs are now focused on improving performance and stability. We spend far less time maintaining the basics.

Resilience by design

The structural changes behind these results reflect a deliberate long-term strategy. Our database architecture now aligns with the operating model we expect to sustain over the next five years and beyond.

Bounded contexts define discrete data domains, while Azure Database for PostgreSQL and Azure Database for MySQL provide managed high availability, scaling controls, and standardized lifecycle management across those domains. Identity integration and governed recovery procedures operate consistently across environments.

With this architecture in place, Multitude scales responsibly in regulated markets while maintaining strict governance and availability standards. Expanding into new markets still means more customers and more data - but now our platform is designed to handle that success.

Pantone’s Palette Generator enhances creative exploration with agentic AI on Azure

mtoiba — Tue, 09 Dec 2025 17:12:11 GMT

Color can be powerful. When creative professionals shape the mood and direction of their work, color plays a vital role because it provides context and cues for the end product or creation. For more than 60 years, creatives from all areas of design—including fashion, product, and digital—have turned to Pantone color guides to translate inspiration into precise, reproducible color choices. These guides offer a shared language for colors, as well as inspiration and communication across industries. Once rooted in physical tools, Pantone has evolved to meet the needs of modern creators through its trend forecasting, consulting services, and digital platform.

Today, Pantone Connect and its multi-agent solution called the Pantone Palette Generator seamlessly bring color inspiration and accuracy into everyday design workflows (as well as the New York City mayoral race). Simply by typing in a prompt, designers can generate palettes in seconds. Available in Pantone Connect, the tool uses Azure services like Microsoft Foundry, Azure AI Search, and Azure Cosmos DB to serve up the company’s vast collection of trend and color research from the color experts at the Pantone Color Institute. reached in seconds instead of days. Now, with Microsoft Foundry, creatives can use agents to get instant color palettes and suggestions based on human insights and trend direction.”

Turning Pantone’s color legacy into an AI offering

The Palette Generator accelerates the process of researching colors and helps designers find inspiration or validate some of their ideas through trend-backed research. “Pantone wants to be where our customers are,” says Rohani Jotshi, Director of Software Engineering and Data at Pantone. “As workflows become increasingly digital, we wanted to give our customers a way to find inspiration while keeping the same level of accuracy and trust they expect from Pantone.” The Palette Generator taps into thousands of articles from Pantone’s Color Insider library, as well as trend guides and physical color books in a way that preserves the company’s color standards science while streamlining the creative process. Built entirely on Microsoft Foundry, the solution uses Azure AI Search for agentic retrieval-augmented generation (RAG) and Azure OpenAI in Foundry Models to reason over the data. It quickly serves up palette options in response to questions like “Show me soft pastels for an eco-friendly line of baby clothes” or “I want to see vibrant metallics for next spring.”

Over the course of two months, the Pantone team built the initial proof of concept for the Palette Generator, using GitHub Copilot to streamline the process and save over 200 hours of work across multiple sprints. This allowed Pantone’s engineers to focus on improving prompt engineering, adding new agent capabilities, and refining orchestration logic rather than writing repetitive code.

Building a multi-agent architecture that accelerates creativity

The Pantone team worked with Microsoft to develop the multi-agent architecture, which is made up of three connected agents. Using Microsoft Agent Framework—an open source development kit for building AI orchestration systems—it was a straightforward process to bring the agents together into one workflow. “The Microsoft team recommended Microsoft Agent Framework and when we tried it, we saw how it was extremely fast and easy to create architectural patterns,” says Kristijan Risteski, Solutions Architect at Pantone. “With Microsoft Agent Framework, we can spin up a model in five lines of code to connect our agents.”

When a user types in a question, they interact with an orchestrator agent that routes prompts and coordinates the more specialized agents. Behind the scenes an additional agent retrieves contextually relevant insights from Pantone’s proprietary Color Insider dataset. Using Azure AI Search with vectorized data indexing, this agent interprets the semantics of a user’s query rather than relying solely on keywords. A third agent then applies rules from color science to assemble a balanced palette. This agent ensures the output is a color combination that meets harmony, contrast, and accessibility standards. The result is a set of Pantone-curated colors that match the emotional and aesthetic tone of the request. “All of this happens in seconds,” says Risteski.

To manage conversation flow and achieve long-term data persistence, Pantone uses Azure Cosmos DB, which stores user sessions, prompts, and results. The database not only enables designers to revisit past palette explorations but also provides Pantone with valuable usage intelligence to refine the system over time. “We use Azure Cosmos DB to track inputs and outputs,” says Risteski. “That data helps us fine-tune prompts, measure engagement, and plan how we’ll train future models.”

Improving accuracy and performance with Azure AI Search

With Azure AI Search, the Palette Generator can understand the nuance of color language. Instead of relying solely on keyword searches that might miss the complexity of words like “vibrant” or “muted,” Pantone’s team decided to use a vectorized index for more accurate palette results. Using the built-in vectorization capability of Azure AI Search, the team converted their color knowledge base—including text-based color psychology and trend articles—into numerical embeddings. “Overall, vector search gave us better results because it could understand the intent of the prompt, not just the words,“ says Risteski. “If someone types, ‘Show me colors that feel serene and oceanic,’ the system understands intent. It finds the right references across our color psychology and trend archives and delivers them instantly.”

The team also found ways to reduce latency as they evolved their proof of concept. Initially, they encountered slow inference times and performance lags when retrieving search results. By switching from GPT-4.1 to GPT-5, latency improved. And using Azure AI Search to manage ranking and filtering results helped reduce the number of calls to the large language model (LLM). “With Azure, we just get the articles, put them in a bucket, and say ‘index it now,’ says Risteski. “It takes one or two minutes—and that’s it. The results are so much better than traditional search.”

Moving from inspiration to palettes faster

The Palette Generator has transformed how designers and color enthusiasts interact with Pantone’s expertise. What once took weeks of research and review can now be done in seconds. “Typically, if someone wanted to develop a palette for a product launch, it might take many months of research,” says Jotshi. “Now, they can type one sentence to describe their inspiration then immediately find Pantone-backed insight and options. Human curation will still be hugely important, but a strong set of starting options can significantly accelerate the palette development process.”

Expanding the palette: The next phase for Pantone’s design agent

Rapidly launching the Palette Generator in beta has redefined what the Pantone engineering team thought was possible. “We’re a small development team, but with Azure we built an enterprise-grade AI system in a matter of weeks,” says Risteski. “That’s a huge win for us.” Next up, the team plans to migrate the entire orchestration layer to Azure Functions, moving to a fully scalable, serverless deployment. This will allow Pantone to run its agents more efficiently, handle variable workloads automatically, and integrate seamlessly with other Azure products such as Microsoft Foundry and Azure Cosmos DB. At the same time, Pantone plans to expand its multi-agent system to include new specialized agents, including one focused on palette harmony and another focused on trend prediction.

Staying in the flow: SleekFlow and Azure turn customer conversations into conversions

mtoiba — Tue, 09 Dec 2025 17:12:29 GMT

A customer adds three items to their cart but never checks out. Another asks about shipping, gets stuck waiting eight minutes, only to drop the call. A lead responds to an offer but is never followed up with in time. Each of these moments represents lost revenue, and they happen to businesses every day.

SleekFlow was founded in 2019 to help companies turn those almost-lost-customer moments into connection, retention, and growth. Today we serve more than 2,000 mid-market and enterprise organizations across industries including retail and e-commerce, financial services, healthcare, travel and hospitality, telecommunications, real estate, and professional services. In total, those customers rely on SleekFlow to orchestrate more than 600,000 daily customer interactions across WhatsApp, Instagram, web chat, email, and more.

Our name reflects what makes us different. Sleek is about unified, polished experiences—consolidating conversations into one intelligent, enterprise-ready platform. Flow is about orchestration—AI and human agents working together to move each conversation forward, from first inquiry to purchase to renewal.

The drive for enterprise-ready agentic AI

Enterprises today expect always-on, intelligent conversations—but delivering that at scale proved daunting. When we set out to build AgentFlow, our agentic AI platform, we quickly ran into familiar roadblocks: downtime that disrupted peak-hour interactions, vector search delays that hurt accuracy, and costs that ballooned under multi-tenant workloads. Development slowed from limited compatibility with other technologies, while customer onboarding stalled without clear compliance assurances.

To move past these barriers, we needed a foundation that could deliver the performance, trust, and global scale enterprises demand.

The platform behind the flow: How Azure powers AgentFlow

We chose Azure because building AgentFlow required more than raw compute power. Chatbots built on a single-agent model often stall out. They struggle to retrieve the right context, they miss critical handoffs, and they return answers too slowly to keep a customer engaged. To fix that, we needed an ecosystem capable of supporting a team of specialized AI agents working together at enterprise scale.

Azure Cosmos DB provides the backbone for memory and context, managing short-term interactions, long-term histories, and vector embeddings in containers that respond in 15–20 milliseconds. Powered by Azure AI Foundry, our agents use Azure OpenAI models within Azure AI Foundry to understand and generate responses natively in multiple languages. Whether in English, Chinese, or Portuguese, the responses feel natural and aligned with the brand. Semantic Kernel acts as the conductor, orchestrating multiple agents, each of which retrieves the necessary knowledge and context, including chat histories, transactional data, and vector embeddings, directly from Azure Cosmos DB. For example, one agent could be retrieving pricing data, another summarizing it, and a third preparing it for a human handoff.

The result is not just responsiveness but accuracy. A telecom provider can resolve a billing question while surfacing an upsell opportunity in the same dialogue. A financial advisor can walk into a call with a complete dossier prepared in seconds rather than hours. A retailer can save a purchase by offering an in-stock substitute before the shopper abandons the cart.

Each of these conversations is different, yet the foundation is consistent on AgentFlow.

Fast, fluent, and focused: Azure keeps conversations moving

Speed is the heartbeat of a good conversation. A delayed answer feels like a dropped call, and an irrelevant one breaks trust. For AgentFlow to keep customers engaged, every operation behind the scenes has to happen in milliseconds.

A single interaction can involve dozens of steps. One agent pulls product information from embeddings, another checks it against structured policy data, and a third generates a concise, brand-aligned response. If any of these steps lag, the dialogue falters.

On Azure, they don’t. Azure Cosmos DB manages conversational memory and agent state across dedicated containers for short-term exchanges, long-term history, and vector search. Sharded DiskANN indexing powers semantic lookups that resolve in the 15–20 millisecond range—fast enough that the customer never feels a pause. Microsoft Phi’s model Phi-4 as well as Azure OpenAI in Foundry Models like o3-mini and o4-mini, provide the reasoning, and Azure Container Apps scale elastically, so performance holds steady during event-driven bursts, such as campaign broadcasts that can push the platform from a few to thousands of conversations per minute, and during daily peak-hour surges.

To support that level of responsiveness, we run Azure Container Apps on the Pay-As-You-Go consumption plan, using KEDA-based autoscaling to expand from five idle containers to more than 160 within seconds. Meanwhile, Microsoft Orleans coordinates lightweight in-memory clustering to keep conversations sleek and flowing.

The results are tangible. Retrieval-augmented generation recall improved from 50 to 70 percent. Execution speed is about 50 percent faster. For SleekFlow’s customers, that means carts are recovered before they’re abandoned, leads are qualified in real time, and support inquiries move forward instead of stalling out.

With Azure handling the complexity under the hood, conversations flow naturally on the surface—and that’s what keeps customers engaged.

Secure enough for enterprises, human enough for customers

AgentFlow was built with security-by-design as a first principle, giving businesses confidence that every interaction is private, compliant, and reliable.

On Azure, every AI agent operates inside guardrails enterprises can depend on. Azure Cosmos DB enforces strict per-tenant isolation through logical partitioning, encryption, and role-based access control, ensuring chat histories, knowledge bases, and embeddings remain auditable and contained. Models deployed through Azure AI Foundry, including Azure OpenAI and Microsoft Phi, process data entirely within SleekFlow’s Azure environment and guarantees it is never used to train public models, with activity logged for transparency. And Azure’s certifications—including ISO 27001, SOC 2, and GDPR—are backed by continuous monitoring and regional data residency options, proving compliance at a global scale.

But trust is more than a checklist of certifications. AgentFlow brings human-like fluency and empathy to every interaction, powered by Azure OpenAI running with high token-per-second throughput so responses feel natural in real time. Quality control isn’t left to chance. Human override workflows are orchestrated through Azure Container Apps and Azure App Service, ensuring AI agents can carry conversations confidently until they’re ready for human agents.

Enterprises gain the confidence to let AI handle revenue-critical moments, knowing Azure provides the foundation and SleekFlow provides the human-centered design.

Shaping the next era of conversational AI on Azure

The benefits of Azure show up not only in customer conversations but also in the way our own teams work. Faster processing speeds and high token-per-second throughput reduce latency, so we spend less time debugging and more time building. Stable infrastructure minimizes downtime and troubleshooting, lowering operational costs.

That same reliability and scalability have transformed the way we engineer AgentFlow.

AgentFlow started as part of our monolithic system. Shipping new features used to take about a month of development and another week of heavy testing to make sure everything held together. After moving AgentFlow to a microservices architecture on Azure Container Apps, we can now deploy updates almost daily with no down time or customer impact. And this is all thanks to native support for rolling updates and blue-green deployments.

This agility is what excites us most about what's ahead. With Azure as our foundation, SleekFlow is not simply keeping pace with the evolution of conversational AI—we are shaping what comes next. Every interaction we refine, every second we save, and every workflow we streamline brings us closer to our mission: keeping conversations sleek, flowing, and valuable for enterprises everywhere.