The average Fortune 500 company runs 40% of its critical workloads on infrastructure that is older than the people maintaining it. We help organizations migrate, re-architect, and modernize — without the 18-month timeline, the 3x budget overrun, and the catastrophic weekend cutover that define most cloud transformations.
Your CEO reads about cloud-native competitors launching features in days while your team takes months. Your CFO sees the data center lease renewal and wonders why you're spending $8 million a year on hardware that depreciates the moment it arrives. Your CISO watches the threat landscape evolve while your security perimeter remains defined by a firewall installed during the Obama administration. The question is no longer whether to modernize. It is whether to modernize intelligently — or to become another cautionary tale of a $200 million migration that delivered a larger cloud bill and the same architecture, just rented instead of owned.
Brindwell's Cloud & Platform Modernization practice exists because most cloud transformations fail — not technically, but economically. They lift and shift without re-architecting. They migrate without modernizing the operating model. They move to the cloud and then operate it like a data center. We do something different: we align the migration to business outcomes, we re-architect what deserves re-architecting, we implement FinOps from day one, and we build the internal platform engineering capability so that when we leave, your team can run what we built.
Every engagement follows a structured methodology designed to eliminate the three failure modes of cloud migration: uncontrolled costs, timeline overruns, and the "just renting a data center" trap.
From strategy through steady-state optimization — every capability needed to move from legacy infrastructure to a modern, cost-governed, team-owned cloud platform.
Most cloud strategies start with the technology and work backward to the business case. We reverse this: starting with the business outcomes the organization needs to achieve — cost reduction, speed to market, geographic expansion, regulatory compliance, M&A integration — and designing a cloud strategy that delivers those outcomes. The readiness assessment evaluates application portfolio complexity, data gravity and sovereignty requirements, team capability and readiness, vendor and licensing constraints, and regulatory obligations. The output is a business case with specific TCO projections, a workload classification matrix, a phased migration roadmap, and an honest assessment of what should stay on-premises.
Lift-and-shift is faster, but it creates an ongoing cost premium and misses cloud-native benefits entirely. Re-architecture is slower, but it delivers elastic scaling, managed service cost savings, and developer velocity improvements that compound over years. We help organizations determine which workloads deserve re-architecture (strategic, high-change-velocity systems) and which should be rehosted or replatformed (stable, low-change systems). For workloads that are re-architected, we decompose monoliths along domain boundaries, containerize with Kubernetes, implement event-driven patterns for asynchronous processing, and adopt managed services (databases, queues, caches) that eliminate operational overhead. The result is not just a cloud deployment — it is a modern application architecture that enables the release velocity the business has been asking for.
Cloud migration without platform engineering is like building a highway without on-ramps. The infrastructure exists, but developers can't use it without filing tickets and waiting weeks. We build internal developer platforms that provide self-service infrastructure provisioning (need a new environment? Click a button), golden path templates (standardized, secure, cost-governed starting points for new services), CI/CD pipelines that enforce quality gates and security scanning automatically, and observability stacks that make debugging a production issue a 10-minute task instead of a 4-hour archaeology expedition. The platform is designed to make the right thing the easy thing — so developers follow best practices not because a governance document says to, but because the platform makes it the path of least resistance.
Cloud bills average 35% higher than necessary. The waste comes from over-provisioned instances, orphaned resources, missed reserved instance opportunities, and the absence of cost accountability. We implement FinOps as a discipline, not a tool: cost allocation tagging that attributes every dollar to a business unit and product, unit economics that measure cost per transaction or cost per customer, automated rightsizing recommendations, reserved instance and savings plan optimization, and anomaly detection that catches cost spikes before they become invoice shocks. Every engineering team sees their cloud cost in real time. Every product owner understands the cost of their architecture decisions. Cloud cost becomes a first-class engineering metric alongside latency and availability.
Cloud security is not firewall security with a cloud prefix. The perimeter is gone. Identity is the new perimeter. We design zero trust architectures where every request is authenticated and authorized regardless of network location, where microsegmentation limits blast radius, where data is encrypted at rest and in transit by default, and where security policies are enforced through code — not through manual configuration that drifts over time. Policy-as-code guardrails prevent the misconfigurations that cause 90% of cloud security incidents: public S3 buckets, overly permissive IAM roles, unencrypted databases, and open security groups. The guardrails are preventive, not detective — they block the misconfiguration before it deploys, not after it's been exploited.
Legacy data architectures — on-premises data warehouses, ETL batch processing, and siloed databases — cannot support the real-time analytics, machine learning, and AI workloads that modern businesses require. We design and implement modern data platforms: migrating data warehouses to cloud-native analytics services, building data lakehouse architectures that unify structured and unstructured data, implementing streaming data pipelines for real-time processing, and establishing data governance frameworks that ensure quality, lineage, and compliance. The migration preserves existing reporting while enabling new capabilities — because your board still needs last quarter's report while data science builds next quarter's predictive model.
Most cloud consulting firms have a primary hyperscaler relationship that drives their recommendations — they recommend AWS because they are an AWS partner, not because AWS is the best fit for your workload. We are hyperscaler-independent. We design multi-cloud and hybrid architectures based on workload requirements: compute-intensive ML workloads may belong on GCP, enterprise applications on Azure, and high-throughput web services on AWS — with on-premises infrastructure retained for data sovereignty, latency-sensitive workloads, or regulatory requirements. The architecture includes unified observability, consistent security policies, and centralized cost management across all environments — preventing the management complexity that undermines multi-cloud value.
Moving to the cloud without observability is flying blind at 40,000 feet. Traditional monitoring tells you that the server is up. Observability tells you that a specific user cohort in a specific region is experiencing 3-second latency on a specific API endpoint due to a database query that was introduced in Tuesday's deployment. We implement full-stack observability — metrics, logs, traces, and profiling — correlated across services so that debugging a distributed system is a 10-minute investigation, not a 4-hour war room. We establish SRE practices: Service Level Objectives that define reliability in terms the business understands, error budgets that balance reliability against release velocity, and incident management processes that learn from failure instead of assigning blame.
A Fortune 200 insurance company had operated its core policy administration, claims processing, and underwriting systems on an IBM mainframe since 1994. The MIPS costs were $14 million annually and rising. The four engineers who understood the COBOL codebase were all within five years of retirement. Meridian led a 14-month migration that re-architected the policy administration system into containerized microservices on AWS EKS, migrated the claims engine to an event-driven architecture, and replatformed the data warehouse to Snowflake. Zero unplanned downtime during migration. Annual infrastructure cost reduced from $14M to $7.3M. Deployment frequency increased from quarterly to daily. And the mainframe was decommissioned on a Tuesday afternoon — not a terrifying weekend cutover.
A PE-backed SaaS company preparing for IPO had a single-cloud AWS deployment with no FinOps discipline, no infrastructure-as-code, and a cloud bill that had tripled in 18 months without corresponding revenue growth. Meridian implemented FinOps (reducing cloud spend 31% within 90 days), rebuilt the infrastructure as code with Terraform, implemented multi-cloud capability for enterprise customers requiring Azure, established SOC 2 Type II compliance through policy-as-code guardrails, and built an internal developer platform that reduced deployment friction from days to minutes. The company passed its IPO readiness audit with zero infrastructure findings — a result that the board attributed directly to the modernization engagement.
A regional health system operating 42 hospitals and 200+ clinics had fragmented infrastructure across three data centers with aging hardware approaching end-of-life. Regulatory requirements demanded HIPAA-compliant cloud architecture with data residency controls, encryption-at-rest for all PHI, and audit logging that met OCR investigation requirements. Meridian designed a HIPAA-compliant landing zone on AWS GovCloud, migrated clinical data systems with zero patient data exposure, implemented full-stack observability that reduced mean time to resolution from 4 hours to 12 minutes, and trained a 15-person internal platform engineering team. The health system's annual infrastructure cost decreased 38% while availability improved from 99.5% to 99.97%.
We had been told by three other consulting firms that our mainframe migration would take three years and cost $40 million. Meridian told us it would take 14 months and cost $12 million — and they were right on both counts. The difference was their approach: they didn't try to re-architect everything. They classified every workload and gave us an honest recommendation — this one gets re-architected, this one gets replatformed, this one gets retired. The mainframe was decommissioned on a Tuesday afternoon. Not a dramatic weekend cutover. A Tuesday afternoon. My team went home at five o'clock and the mainframe was off. That is what competent migration looks like.
Our cloud bill had tripled in 18 months. Our CFO was asking why we moved to the cloud if it costs more than the data center. Brindwell's FinOps team found $1.8 million in waste in the first two weeks — orphaned resources, oversized instances, and missed reserved instance opportunities. They cut 31% of our cloud spend within 90 days without reducing any capacity or performance. Then they built the discipline so it wouldn't come back: every resource tagged to a cost center, every team seeing their own spend in real time, and anomaly alerts that catch cost spikes before they become surprises. Our CFO is no longer asking why we moved to the cloud. He's asking why we didn't do FinOps from the start.
When our production system went down before the migration, it took four hours to figure out what happened. Four hours of war rooms, blame shifting, and checking logs in six different tools. After Meridian implemented observability, the same class of incident takes twelve minutes to resolve. Not because the problems are simpler — they're actually more complex in a distributed system. But the observability stack correlates metrics, logs, and traces so that when something breaks, the on-call engineer sees exactly which service, which deployment, and which code change caused it. The four-hour war room is gone. My engineers sleep through the night. That alone was worth the engagement.
Request a Cloud Readiness Assessment — a 4-6 week engagement that produces a business-case-driven migration roadmap with TCO projections and wave plan.