We’re a cloud-native DevOps and AI consulting firm. We help teams build and run reliable infrastructure and deployment pipelines, secure them, and put AI and agentic automation to practical use. That covers DevOps consulting, cloud architecture, CI/CD, Kubernetes, DevSecOps, platform engineering, MLOps, and agentic workflows. In plain terms: we make it faster and safer for your engineers to ship, and everything we build is in code and documented so you own it.
Most consulting firms deliver recommendations. We help implement them. Our engineers work alongside your team, contribute directly to your workflows, and focus on measurable improvements in delivery, reliability, and operational efficiency.
No. We work with scaling startups, high-growth technology companies, and enterprise organizations. The common factor isn’t company size. It’s engineering complexity.
We work fully remotely with clients across Australia, Singapore, the Middle East, Europe, and North America. Our team is based in India, and we commit to a minimum of four overlapping working hours with your time zone, so there’s always a real window for live conversations alongside asynchronous progress the rest of the day. Overlap hours and communication channels are agreed at kickoff.
We’re a team of 20+ certified developers, so you work directly with people who do the work rather than through layers of account management. We hold partnerships with the major cloud providers and with leading DevOps tools including GitLab, HashiCorp, and Datadog. Certifications aren’t the point on their own, but they mean we operate to a known standard rather than learning on your infrastructure.
Yes. We work across AWS, Azure, GCP, Kubernetes, Terraform, GitLab, Jenkins, Datadog, and many other technologies. Our recommendations are based on what fits your environment, not a preferred vendor ecosystem.
Absolutely. Our goal is to strengthen your engineering capabilities, not create dependency. We embed with your team, transfer knowledge, and ensure your engineers can confidently operate the platform after the engagement ends.
Both. Alongside the core DevOps and cloud work, we build MLOps pipelines that get machine learning into production reliably, and agentic workflows that automate real operational and business processes. We focus on what holds up in production, and we’ll tell you honestly when an AI use case isn’t ready for it yet.
Most engagements start with an assessment and roadmap, but implementation begins quickly. Clients typically start seeing measurable improvements in deployment velocity, reliability, or operational efficiency within the first few weeks.
We bill hourly, across three engagement models you can pick based on how you want to work with us: a Hours Bank, a fixed Project, or Staff Augmentation. There are no long lock-in contracts. Project and Staff Augmentation both begin with a free deep-dive and a written work plan, so you see exactly what you’re paying for before anything starts.
Hours Bank is senior DevOps expertise on tap for teams that want help without making a hire. Project is a defined outcome we plan and deliver end to end, like a migration or a platform build. Staff Augmentation is a steady embedded engineer, part-time or full-time, who works like a member of your team. You can start with whichever fits and move between them as your needs change.
You start with a block of hours (from 20) and draw them down as needs arise, topping up whenever you like. You get the right expert for each task from our team rather than a single fixed person, and a detailed hours log at the end of every month so you can see exactly where the time went. It suits teams that want senior help on tap without committing to a full hire.
It starts with a free deep-dive and a written work plan, so the scope and approach are clear before any paid work begins. From there we plan and implement the whole outcome end to end, through build and ship. There’s no obligation at the deep-dive stage: you meet your engineer, see the plan, and then decide whether to go ahead.
It’s a steady embedded engineer, part-time or full-time, who works like part of your team: in your standups, your Slack, and your roadmap. It also begins with a free deep-dive and written work plan, then runs with a proper kickoff, weekly syncs, and a shared Slack channel. It’s the closest thing to hiring, without the overhead of hiring.
Yes, the deep-dive and written work plan come first and cost nothing, for both Project and Staff Augmentation engagements. You meet the engineer who’d do the work and see a concrete plan before committing a rupee or a dollar. If it’s not the right fit, you walk away with no obligation.
The Hours Bank starts at 20 hours, and there are no long-term lock-in contracts on any model. You can switch as your needs change, for example, starting with a 20 Hours Bank for ad-hoc help and moving to Staff Augmentation once you want steady embedded capacity.
We bill in your local currency, so there’s no foreign-exchange friction on your side. The fee is simply the hours you use at the agreed rate. Third-party costs that are genuinely yours (cloud provider bills, software licenses, tool subscriptions) are paid by you directly to those vendors, stay in your control, and are never marked up.
Not always. We help determine whether public cloud, dedicated GPU environments, or sovereign AI infrastructure is the right fit.
Yes. We build on AWS, Azure, Google Cloud, hybrid environments, and on-prem infrastructure.
Most engagements begin showing measurable outcomes within the first 30–60 days.
Yes. From model training pipelines to production-grade LLM and agent deployments.
Absolutely. We design architectures aligned with industry and regional compliance requirements.
A DevOps assessment reviews your delivery pipeline, infrastructure, CI/CD processes, security practices, observability, cloud architecture, and team workflows. The outcome is a prioritized roadmap highlighting bottlenecks, risks, and opportunities for improvement.
The timeline depends on the scope. Assessments and audits typically take 2–4 weeks, while implementation and transformation projects can range from 1–6 months. Most engagements begin delivering measurable improvements within the first few weeks.
We measure success through outcomes, not activities. Common metrics include deployment frequency, lead time for changes, infrastructure reliability, cloud cost optimization, change failure rate, MTTR (Mean Time to Recovery), and overall engineering productivity.
Yes. Our team works across AWS, Microsoft Azure, and Google Cloud Platform, including their managed DevOps, Kubernetes, security, and cloud infrastructure services. We recommend solutions based on your requirements, not vendor preferences.
Absolutely. We embed alongside your engineers, collaborate on architecture and implementation, and transfer knowledge throughout the engagement. The goal is to strengthen your team’s capabilities, not create long-term dependency.
In most cases, we optimize the tools you already use. We only recommend switching when the current platform creates clear limitations that outweigh the cost of migration. If a migration makes sense, we’ll explain exactly why and what you’ll gain from it.
Yes. We design CI/CD pipelines for large monorepos using affected-service detection, parallel builds, shared libraries, and coordinated releases. Whether you’re scaling a monorepo or evaluating a move to polyrepos, we’ll help you choose the right approach.
We treat database changes as code using tools like Flyway and Liquibase. Migrations are automated within the deployment pipeline and designed to support canary, blue-green, and rolling deployments while minimizing risk and enabling safe rollbacks.
Absolutely. Most of our CI/CD engagements involve Kubernetes. We design end-to-end pipelines covering builds, testing, container registries, GitOps workflows, ArgoCD or Flux deployments, progressive delivery, and deployment observability.
Automation and compliance work well together when designed correctly. We build pipelines with audit trails, approval workflows, access controls, and compliance guardrails that satisfy requirements such as SOC 2, ISO 27001, HIPAA, and PCI-DSS without slowing delivery.
A well-implemented DevSecOps program does the opposite. By catching security issues early through automated checks, teams avoid costly fixes, emergency patches, and production incidents later. Security becomes part of the workflow, not a bottleneck at the end of it.
Having tools isn’t the same as using them effectively. Many teams generate security findings but struggle with false positives, poor adoption, or unclear policies. We help configure the tools, define workflows, and build governance that turns security into an operational capability instead of a reporting exercise.
DevSecOps helps automate the security controls and audit evidence required by frameworks such as SOC 2, ISO 27001, HIPAA, and PCI-DSS. We map security controls to your compliance requirements and ensure the necessary evidence is generated as part of normal operations.
False positives are one of the biggest adoption challenges in DevSecOps. We tune tools to your technology stack, reduce unnecessary noise, establish clear triage processes, and define which findings should block deployments versus those that are informational.
Yes. We work with GitHub Actions, GitLab CI, Jenkins, CircleCI, Azure DevOps, AWS CodePipeline, ArgoCD, and other major platforms. Security controls are integrated into your existing workflows rather than forcing a complete rebuild.
A penetration test is a point-in-time assessment that identifies vulnerabilities through simulated attacks. DevSecOps provides continuous security throughout the delivery lifecycle by detecting and preventing issues before they reach production. The two work best together, not as replacements for each other.
Yes. We take a practical approach by introducing security controls that work within your current environment, including SAST, SCA, and DAST. Modernization can happen over time, but you don’t need a complete rebuild before improving security.
Yes. Most of our engagements focus on optimization, not rebuilding. We review your architecture, identify gaps across cost, security, reliability, and automation, then prioritize improvements with minimal disruption to running workloads.
A landing zone is the secure foundation of your cloud environment, covering account structure, networking, IAM, logging, and governance. If your cloud has grown organically without a clear framework, a landing zone helps bring consistency, security, and control.
We recommend multi-cloud or hybrid architectures only when there is a clear business need, such as compliance, data residency, redundancy, or platform-specific requirements. For most organizations, a well-designed single-cloud strategy is simpler and more cost-effective.
We begin with a workload assessment to determine what should be migrated, modernized, or retained on-premises. From there, we design the target architecture, establish the cloud foundation, and execute the migration in controlled phases.
FinOps relies on visibility and governance. We implement tagging standards, cost allocation models, infrastructure automation, and cost monitoring so teams can track, manage, and optimize cloud spending continuously.
Kubernetes consulting delivers value at every stage. Smaller teams benefit from architecture and GitOps foundations, while larger organizations typically focus on security, scalability, cost optimization, and platform engineering.
Absolutely. Most of our engagements involve existing production clusters. Common goals include improving security, reducing costs, increasing reliability, and preparing for growth.
A security audit focuses solely on cluster security, compliance, and risk assessment. Kubernetes consulting is broader, covering architecture, GitOps, observability, cost optimization, security, and Day 2 operations.
Yes. Knowledge transfer is built into every engagement through documentation, working sessions, architecture reviews, and hands-on collaboration. The goal is long-term independence, not dependency.
Both. We support managed platforms like EKS, AKS, and GKE, as well as self-managed environments including kubeadm, Loki, and RKE2.
We integrate with your existing workflows, tools, and cloud environment. Our focus is to strengthen your DevOps foundation, not replace it.
Yes. We support GPU workloads, Kubeflow, MLflow, AI infrastructure, and the operational patterns required to run ML workloads reliably and cost-effectively at scale.
Kubernetes provides the infrastructure foundation. Platform engineering builds the developer experience on top of it through self-service platforms, automation, and internal developer portals. Many organizations implement both together.
Yes. Tools provide visibility, but SRE creates the practice around them. We help teams define SLOs, reduce alert fatigue, improve incident response, and turn observability data into measurable reliability outcomes.
An SLI measures service performance, such as response time or availability. An SLO is the internal target for that metric. An SLA is the customer-facing commitment, often with contractual consequences if it’s missed.
Absolutely. You don’t need a dedicated SRE team to adopt SRE practices. Defining SLOs, improving alerting, and building a strong incident response process can deliver significant reliability gains at any team size.
Many teams see improvements within 30 days through better monitoring, alert tuning, and tracing. Broader reliability gains, such as SLO performance and on-call maturity, typically emerge over 60–90 days.
Chaos engineering is the practice of intentionally testing failures to validate how systems respond. It helps teams verify failover mechanisms, recovery processes, and operational readiness before real incidents occur.
Yes. Mixed environments are the norm, not the exception. We manage Linux distributions such as RHEL, Ubuntu, Debian, and CentOS alongside Windows Server environments using automated patching, configuration management, monitoring, and centralized logging. The goal is one operational model across your infrastructure, regardless of operating system.
We start with the workload, not the vendor. We assess compute, memory, storage, and network requirements, then model the full 5-year TCO across platforms such as Dell, HPE, Lenovo, and Supermicro.
Hardware typically accounts for only 15-20% of total ownership cost. Power, cooling, support contracts, maintenance, and refresh cycles make up the rest. Our recommendations are vendor-neutral and backed by both operational and financial analysis.
It depends on your workload, facility capacity, compliance requirements, connectivity needs, and long-term operating model.
We compare on-prem expansion, co-location, and hybrid approaches using a side-by-side TCO assessment so decisions are based on data, not assumptions. Sometimes co-location is the right answer. Sometimes staying on-prem is.
For most enterprise workloads, Tier III is the sweet spot. It provides concurrent maintainability and approximately 99.982% uptime without the significant cost premium of Tier IV.
If downtime carries major financial, operational, or safety consequences, Tier IV may be justified. We help evaluate the right level based on your business requirements.
The most common alternatives are Proxmox VE, Microsoft Hyper-V, and OpenShift Virtualization.
The right choice depends on your VM footprint, operational expertise, migration complexity, and projected licensing costs over the coming years. We evaluate both the technical and financial impact before recommending a migration path.
Proactive monitoring identifies issues before users notice them.
That includes disk health monitoring, hardware degradation alerts, capacity forecasting, network anomaly detection, UPS monitoring, and infrastructure health checks. Every alert is tied to a documented response process, so engineers know exactly what action to take.
Maybe. Workloads that are predictable, data-intensive, GPU-heavy, or running continuously are often strong candidates for on-prem infrastructure.
We evaluate the full 5-year TCO before recommending a move. If cloud remains the better option, we’ll tell you that too.
Yes. It’s one of our fastest-growing engagements.
For organizations running sustained AI workloads, owning GPU infrastructure can be significantly more cost-effective than renting it long term. We design, deploy, and operate GPU clusters optimized for training and inference workloads.
Platform Engineering typically starts delivering significant value once you have 20-30 engineers working across multiple teams. At this stage, tool sprawl, inconsistent workflows, and growing infrastructure dependencies begin creating measurable delivery friction.
For smaller teams, a shared DevOps model is often sufficient. For organizations with 50+ engineers, Platform Engineering usually shifts from a nice-to-have to a necessity.
Yes. This is one of the most common challenges we see.
Backstage adoption usually fails because developers stop trusting the catalog, workflows don’t match how teams actually work, or nobody owns platform adoption as a product.
We identify the root cause, improve adoption where possible, and provide an honest recommendation if another platform would be a better fit.
A Golden Path is a pre-configured workflow that embeds your organization’s best practices into a simple self-service experience.
For example, a developer can create a new service and automatically receive a repository, CI/CD pipeline, security scanning, monitoring, Kubernetes configuration, and documentation template from a single request.
It makes the best way to build software the easiest way to build software.
Most teams see measurable improvements within 6-12 weeks of launching an MVP platform.
Onboarding becomes faster, infrastructure ticket volume decreases, and self-service adoption begins to grow. Broader improvements across DORA metrics typically emerge within 3-6 months as teams adopt new workflows.
Platform maturity is a journey, but value should be visible early.
Platform Engineering connects your existing tools into a unified developer experience.
Kubernetes becomes self-service infrastructure. CI/CD pipelines become standardized Golden Paths. Security and compliance controls become automated defaults rather than manual checks.
The result is greater consistency, faster onboarding, and higher adoption of the investments you’ve already made.
Yes. This is one of the most common situations we see. We build the deployment, monitoring, and operational infrastructure so data scientists can focus on model development while automated pipelines handle the rest.
Traditional ML focuses on training pipelines, model registries, and drift detection. GenAI introduces prompt versioning, evaluation frameworks, hallucination monitoring, RAG pipelines, and token cost management. Most organizations need both.
Both are valid. SageMaker offers managed infrastructure and strong AWS integration. Open-source stacks provide flexibility and vendor independence. We help choose based on your team, cloud strategy, and compliance needs.
Financial services, healthcare, and insurance require model documentation, validation records, lineage tracking, and approval workflows. We embed governance directly into the MLOps lifecycle.
Most teams see their first production-ready MLOps foundation within 6-10 weeks. Larger platforms with monitoring, governance, and automated retraining typically take 3-5 months.
A good agentic use case is high-volume, repeatable, measurable, and clearly defined. If a workflow requires frequent human judgment or only runs occasionally, traditional automation may be a better fit. Our readiness assessment helps identify where agents can create the most value.
Copilots assist humans. Agents execute workflows.
A copilot helps generate content, answer questions, or suggest actions. An agent receives a goal, interacts with tools and systems, makes decisions within defined boundaries, and completes tasks autonomously. A copilot might write a deployment runbook. An agent can execute the deployment using that runbook.
We use multiple layers of control, including permission boundaries, policy-based validation, human approval checkpoints, and execution limits. Every action is governed, monitored, and auditable, ensuring agents operate only within approved boundaries.
The right framework depends on your requirements, existing stack, and workflow complexity. We commonly use LangGraph for production-grade workflows, CrewAI for rapid multi-agent implementations, and Microsoft Agent Framework for .NET and Azure-centric environments. Our recommendations are based on your needs, not framework preferences.
Every engagement includes an evaluation framework. We test agents against real-world scenarios, edge cases, and failure conditions before deployment. After launch, we continuously track metrics such as task completion rate, escalation rate, accuracy, and output quality.
Yes. Organizations in financial services, healthcare, insurance, and government are already deploying agentic systems. The key is designing for compliance from the start through audit trails, approval workflows, explainability, and governance controls aligned with regulatory requirements.
MLOps focuses on training, deploying, monitoring, and governing machine learning models. Agentic workflows focus on orchestrating LLMs, tools, memory, and decision-making to automate multi-step processes. They solve different problems and often work together within the same AI ecosystem.
Traditional consulting delivers recommendations, roadmaps, and systems. Forward Deployed Engineering delivers those outcomes while embedding directly into your team. Our engineers work inside your workflows, code reviews, and architectural discussions, ensuring knowledge transfer happens throughout the engagement, not just at handoff.
Most FDEs deliver their first meaningful outcomes within the first two weeks. Week one focuses on understanding your environment and team. Week two is typically where the first quick wins and high-impact improvements start shipping.
Our minimum engagement is three months. This gives the engineer enough time to understand your environment, deliver meaningful outcomes, and transfer knowledge effectively to your team. For shorter initiatives, a focused consulting engagement is usually a better fit.
Yes. Many engagements begin with a single embedded engineer and expand into a small team for larger transformation programs. We can also transition from full-time embedding to an advisory model once the primary objectives are achieved.
Real projects evolve, and so do priorities. When requirements change, we review the impact, align on timelines and deliverables, document the adjustments, and keep moving. The goal is solving the problem, not creating process overhead.