Case Studies
Anonymous, technically detailed engagements. If your situation looks like one of these, ADAAS has already solved something similar.
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Case 01From Two Systems to One Shared ArchitectureWhen one company acquires another, the hardest part is rarely the balance sheet — it is the software. Both organizations quietly built the same capabilities in different ways. This is how an enterprise found the common architectural ground between two systems and consolidated on it, without throwing away what already worked.Cross-industryEnterprise1000+Case 02AI Orchestration Without Sacrificing Operational ControlA large enterprise had years of highly reliable pipelines that almost no one dared change and only a few specialists understood. We let AI orchestrate those proven procedures — deciding which to run and with what parameters — without letting it modify a single line of production logic.Infrastructure & OperationsEnterprise1000+Case 03Modernize the Team Without Putting Production at RiskLong-lived enterprise applications carry several generations of engineers around the same code. The organization wanted new talent — including AI-native developers — without giving anyone the ability to accidentally break decades of production logic. The answer was an explicit architectural boundary.Enterprise SoftwareEnterprise250–1000Case 04Know What Will Break Before You DeployIn a large microservices estate, a small change can ripple across many services. When an AI coding agent modifies one component, the expensive question is always the same — what else does this affect? This is how an enterprise made that answer instant.Enterprise SoftwareProduct250–1000Case 05From Business Question to Working ApplicationBusiness analysts constantly need small applications that are too small for a full development cycle yet too complex to build safely alone. We removed development from the critical path for those apps — without creating shadow IT.BI & AnalyticsEnterprise1000+Case 06Stop Paying AI to Rediscover Your ArchitectureAn organization was using an AI agent for frequent trivial changes — yet each one made the agent scan and re-reason over large parts of the codebase. They were paying AI to repeatedly rediscover information it already knew. Targeted architectural context cut feature delivery cost by 200%.SaaS / ProductProduct50–250
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