AscendPrime Systems
Infrastructure Consulting & Technical Training

Production Infrastructure Expertise

We help engineering teams design, troubleshoot, scale, and operate reliable distributed systems.

How We Help

Infrastructure Consulting

Architecture, reliability, and production engineering for complex infrastructure.

  • Infrastructure Architecture & Technical Reviews
  • Kafka, Messaging Systems & Distributed Caching
  • Cloud Infrastructure, DevOps & Observability
  • Reliability, Scalability & Disaster Recovery
  • Production Troubleshooting & Technical Advisory
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Technical Training

Hands-on training for engineers who want practical production skills.

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Areas of Expertise

Cloud & Infrastructure

Platform design and operational resilience for cloud-based production workloads.

  • Azure
  • Cloud Architecture
  • Infrastructure Design
  • High Availability
  • Disaster Recovery

DevOps & Automation

Repeatable delivery and environment consistency for faster, safer operations.

  • Terraform
  • Ansible
  • Infrastructure as Code
  • CI/CD
  • Configuration Management

Distributed Systems & Messaging

Event-driven systems built for scale, throughput, and operational reliability.

  • Kafka
  • Redis
  • ActiveMQ Artemis
  • MQTT
  • Event-Driven Architecture

Reliability & Observability

Monitoring, performance, and troubleshooting support for production stability.

  • Monitoring
  • Metrics & Logging
  • Performance Engineering
  • Capacity Planning
  • Production Troubleshooting

AI Infrastructure & Deployment

Support for deploying and operating AI workloads in cloud environments.

  • AI Workload Infrastructure
  • Deployment & Operations
  • Cloud Infrastructure for AI Applications
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Infrastructure & Reliability Audits

A focused technical assessment for teams that need an independent view of their infrastructure, reliability, scalability, or operational risks.

Targeted, time-boxed reviews of your production architecture, identifying hidden bottleneck risks, Kafka/Redis inefficiencies, security gaps, and scaling blockers before they cause downtime.

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Audit types

  • Production Architecture & Scalability Audit
  • Cloud Cost & Resource Optimization Review
  • Messaging & Event-Driven Reliability Check (Kafka/Redis)
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Production Engineering in Practice

Infrastructure Case Studies

Discuss a Similar Challenge

Case Study A

Highly Available Redis Enterprise Infrastructure

Redis Enterprise infrastructure supporting approximately 20 applications with multi-zone high availability and operational rigor across 8 clusters and 90 databases.

Scale and environment

~8 clusters~90 databases~20 applications~50,000 req/s~500 GB memory

Environment and problem statement

The environment was production-facing and required a design that could sustain significant request volume while preserving failover readiness, operational clarity, and low-risk change management.

Challenge

The challenge was balancing throughput, failover behavior, deployment simplicity, and operational discipline across a large multi-database Redis footprint.

Architecture diagram

App tierZone APrimaryReplicationStandbyZone BMonitoring + recovery orchestrationDRRegionBackupRecovery

Illustrative reference architecture — actual designs depend on workload and requirements.

Key decisions and trade-offs

The design used rack and zone awareness to reduce the blast radius of infrastructure failures, while keeping data placement and failover dynamics aligned with production requirements. Monitoring, alerting, and runbooks were treated as first-class architectural controls so operators could respond quickly during degraded conditions.

Reliability, observability, and security

Availability and recovery decisions were shaped by failure-domain awareness, redundant capacity planning, and explicit operational procedures. Disaster recovery and recovery procedures were documented so recovery actions could be tested and repeated under pressure.

Testing and failure scenarios

The operating model included controlled failover exercises, monitoring validation, and recovery drills that checked whether the environment could tolerate broker loss and zone-level disruption without creating operational ambiguity.

Outcomes and lessons learned

The result was a Redis platform designed for resilient production operations at scale, with clear recovery paths and a strong emphasis on operational readiness rather than theoretical capacity alone.

Case Study B

MQTT Messaging Infrastructure at Scale

Apache ActiveMQ Artemis MQTT infrastructure designed for a large device footprint with layered security, connection control, and regional failover considerations.

Scale and environment

~12 brokers~6 clusters~20,000 connected devices1 million device capacity targetmTLS + F5 + HA

Environment and problem statement

A production messaging layer supporting a large number of connected devices needed to handle both connection density and operational resilience while preserving predictable behavior across distributed clusters.

Challenge

The design needed to accommodate device connection churn, broker distribution, and operational policies around TLS, authentication, and message ordering without creating hidden failure paths or uneven traffic distribution.

Architecture diagram

ClientsmTLS / TLSIdentityAuthZ / ACLsBroker clusterRouting + replicationTopic-level policiesSecrets managementAudit loggingAudit trail

Illustrative reference architecture — actual designs depend on workload and requirements.

Key decisions and trade-offs

The architecture spread connectivity across broker clusters with F5-based load distribution, custom authentication handling, and client-IP logic to keep client connections balanced and predictable. Replication or mirroring, availability patterns, and failover routing were planned around the risk of connection storms and single-cluster dependency.

Reliability, observability, and security

Security and reliability were addressed together: mTLS protected device-to-broker trust, while connection distribution and failure handling minimized the impact of individual broker or cluster disruption. Operational risk reduction included deliberate review of client behavior, connection patterns, and recovery paths.

Testing and failure scenarios

Load testing focused on connection scale and distribution behavior, including how failures would surface under partial outage conditions. The work also reviewed ordering guarantees and operational quirks that appear only under sustained traffic and abnormal connection patterns.

Outcomes and lessons learned

The design produced an infrastructure model intended to scale toward a 1 million device target while keeping security, routing, availability, and operational recovery in active view.

Case Study C

Real-Time Communications Infrastructure

WebRTC, Coturn, and Janus infrastructure spanning multiple geographic regions to support camera deployments and production-grade connection establishment at scale.

Scale and environment

Multiple regionsWebRTC + Coturn + JanusCamera deploymentsNAT traversal involvedRegional distribution

Environment and problem statement

The infrastructure needed to support real-time media traffic across multiple geographic areas while handling variable connectivity conditions and the realities of NAT traversal, signaling, and media path establishment.

Challenge

The main challenge was selecting connection patterns and regional placement that would keep sessions reliable while accounting for endpoint mobility, network traversal, and the availability demands of camera-based deployments.

Architecture diagram

ClientsmTLS / TLSIdentityAuthZ / ACLsBroker clusterRouting + replicationTopic-level policiesSecrets managementAudit loggingAudit trail

Illustrative reference architecture — actual designs depend on workload and requirements.

Key decisions and trade-offs

The design centered on geographically distributed media infrastructure with explicit attention to how connection establishment, relay placement, and regional proximity affected availability and user experience. The architecture needed to be resilient enough to handle variable networking conditions without hiding operational complexity.

Reliability, observability, and security

Observability, region-aware placement, and failover planning were important because connection establishment and media path quality depend heavily on network conditions and the behavior of edge devices. Security and operational readiness were handled as part of the same infrastructure discipline.

Testing and failure scenarios

The work included validating the assumptions behind connection establishment, relay behavior, and region-based placement under stressed conditions rather than only ideal network conditions. This type of environment requires scenario testing around NAT traversal, regional degradation, and failure recovery.

Outcomes and lessons learned

The outcome was a realistic production-minded architecture pattern: distributed, capacity-aware, and operationally intentional rather than a single-region design optimized only for a happy path.

Architecture & Reliability

How We Engineer Production Systems

Illustrative reference architecture — actual designs depend on workload, environment, operating model, and existing infrastructure constraints.

Kafka event streaming

Illustrative reference architecture for producers, broker coordination, consumer groups, and observability.

ProducersLoadBalancerbootstrapKafka brokersTopics / partitionsReplicationISR / leadershipConsumer groupsMonitoring + alerting

High availability and disaster recovery

Multi-AZ deployment pattern with replication, monitoring, recovery orchestration, and a disaster-recovery region.

App tierZone APrimaryReplicationStandbyZone BMonitoring + recovery orchestrationDRRegionBackupRecovery

Secure messaging

Reference architecture for client identity, TLS/mTLS, authorization, broker infrastructure, and audit visibility.

ClientsmTLS / TLSIdentityAuthZ / ACLsBroker clusterRouting + replicationTopic-level policiesSecrets managementAudit loggingAudit trail

Observability, Load Testing & Resilience

Production Signals and Failure Scenarios

Illustrative demo data only — not connected to a live client environment.

Kafka operations dashboard

System health overview

Illustrative demo data

Consumer lag

8.4s

Group: payments-3

Msgs in / s

14.6k

Peak 22.4k

Broker CPU

72%

One broker trending high

URP / offline

2 / 0

Replica drift

Consumer lag by consumer group

Request latency + error rate

P95 latency174 ms
Error rate0.9%
Network throughput1.6 GB/s

Failure-injection exercises

  • Broker outage and leadership change

    Stop a Kafka broker and observe how partition leadership and consumer traffic react during a controlled degradation.

    Leader changesBroker CPU and disk utilizationUnder-replicated partitions
  • Consumer delay simulation

    Introduce slower processing and observe how consumer lag scales across consumer groups and partitions.

    Consumer lagMessages in/out per secondRebalance events
  • Network interruption or AZ failure

    Simulate a network issue or availability-zone incident to observe failover behavior and request error rates.

    Request latencyError rateBroker and network throughput
  • Client certificate revocation

    Revoke or expire a certificate to confirm that unauthorized clients are rejected before they can connect to message brokers.

    Authentication failuresConnection countsAudit log entries
  • Disaster recovery validation

    Exercise the documented recovery workflow to confirm that backup restoration, rehydration steps, and team procedures are still consistent.

    Recovery timeFailover statusOperational checklist completion

How We Work

Consulting Engagements

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Engagement model

Infrastructure Assessment / Fixed-Scope Audit

Starting at $2,500

Indicative introductory price subject to scope confirmation.

Architecture review, reliability assessment, risk register, and prioritized recommendations for a targeted production infrastructure problem.

Engagement model

Fractional / Part-Time Infrastructure Engineering

Custom quote

Based on weekly hours, scope, and engagement duration.

Flexible engineering support for teams that need experienced DevOps, messaging, or cloud infrastructure coverage without a full-time hire.

Engagement model

Ongoing Technical Advisory

Custom monthly engagement

Architecture reviews, decision support, and mentorship.

Hands-on technical advisory for architecture decisions, production reliability planning, and engineering mentorship.

Engagement model

Implementation / Modernization Projects

Fixed-scope or milestone-based proposal

Discovery and scoping required before proposal sign-off.

Kafka, messaging infrastructure, cloud improvements, observability, automation, and reliability work delivered through a defined implementation plan.

Security, Reliability & Operational Readiness

Production systems need clear guardrails

Security and compliance requirements are assessed against the customer environment, existing controls, and applicable frameworks. No technology automatically makes a deployment compliant.

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  • TLS and mutual TLS (mTLS) for service and client trust boundaries.
  • Authentication and authorization controls aligned to Kafka ACLs, broker policies, and least-privilege access.
  • Secrets management, credential rotation, and operational hygiene for key material and service identities.
  • Network segmentation and private connectivity patterns for production environments.
  • Least privilege and role-based access for operators, deployment systems, and support workflows.
  • Encryption in transit and at rest where supported by the selected platform and architecture.
  • Operational audit logging, access review, and traceability for privileged actions.
  • Vulnerability management, patching, configuration management, and change governance.
  • Disaster recovery validation, backup checks, recovery procedures, and incident response readiness.

Built From Real Production Experience

Tulika Gupta

Founder & Principal Consultant

AscendPrime Systems is founded by Tulika Gupta, a DevOps and infrastructure leader with 15+ years of experience building, scaling, and operating production systems.

Her work has centered on production infrastructure design and operations, event-driven systems, messaging, Redis, WebRTC, cloud infrastructure, observability, and high-availability architecture.

The emphasis is on practical engineering: understanding how systems behave under real workloads, identifying failure modes, strengthening reliability, and guiding teams through technical decisions with clarity.

Who We Help

Engineering Teams

Teams operating distributed systems that need architecture, reliability, scalability, or production troubleshooting expertise.

Growing Technology Companies

Companies whose infrastructure is becoming more complex and need help preparing systems for scale, reliability, and operational maturity.

Engineering Leaders

CTOs, VPs, engineering managers, and technical leaders who need experienced infrastructure expertise without immediately hiring a full-time specialist.

How We Work

01

Tell Us About the Problem

Start with a short conversation about your infrastructure, challenges, and goals.

02

Assess

We review the relevant architecture, operational constraints, and technical requirements.

03

Recommend

You receive practical recommendations and a clear path forward.

04

Execute

When needed, we can support implementation or provide ongoing technical advisory.

Have a production problem you’re trying to solve?

Whether you need an architecture review, infrastructure strategy, reliability assessment, or hands-on engineering support, let’s talk about the problem first.