Client Overview
OmiSoft is an enterprise software and product development company delivering full-cycle software services across discovery, UX, development, QA, maintenance, and client support. As the company expanded its use of AI across internal delivery teams, OmiSoft needed a structured way to move from experimental AI usage toward controlled, measurable, and production-ready Claude/LLM adoption.
Umbrelly.Cloud guided an OmiSoft to integrate Anthropic Claude Code & Claude Cowork into the company’s software delivery workflows and create a practical framework for AI usage visibility, cost tracking, model selection, and responsible adoption across departments.
Challenge
OmiSoft’s teams were already experimenting with AI across multiple parts of the software delivery lifecycle, including:
Code generation and engineering assistance
Technical documentation
QA test-case generation
Requirements analysis
Client support and delivery communication
Product and project management workflows
The challenge was no longer whether AI could help the team. The real challenge was how to make AI usage repeatable, measurable, cost-efficient, and safe across multiple delivery teams.
As OmiSoft started using Claude Code API and Claude-based workflows more actively across development, product, QA, and delivery departments, several operational questions became important:
Which teams and employees were using Claude most actively?
Which use cases created real productivity gains?
Which AI workflows justified the cost?
When should the team use the most capable Claude model, and when would a lower-cost model be sufficient?
How could OmiSoft avoid uncontrolled AI spend while still increasing Claude adoption?
How could developers safely use AI when working with sensitive business logic, client requirements, private codebases, and product documentation?
OmiSoft needed better visibility into AI costs, model usage, employee-level adoption, department-level consumption, and the value created by different AI workflows.
Umbrelly.Cloud Solution
Umbrelly.Cloud helped OmiSoft transition from fragmented AI experimentation to a controlled Claude adoption framework.
The implementation focused on four key areas:
1. Claude Integration Across Software Delivery Workflows
Umbrelly helped OmiSoft identify where Claude could create the highest value inside the company’s delivery cycle.
Priority workflows included:
Turning discovery notes into structured requirements
Creating and improving technical documentation
Supporting developers with code generation, refactoring, and code review
Generating QA test cases and edge-case scenarios
Supporting product managers with user stories and specifications
Drafting client-facing delivery updates and support responses
This allowed OmiSoft to move from ad hoc AI experiments to a clear set of repeatable Claude-powered workflows across departments.
2. AI Usage Tracking and Cost Visibility
Umbrelly created a visibility layer to help OmiSoft understand how Claude and other LLM tools were being used across the organization.
This included tracking:
AI usage by department
AI usage by individual employee
AI usage by workflow type
Model consumption by task category
Cost per AI-assisted workflow
Adoption trends across development, product, QA, and delivery teams
This gave OmiSoft management a clearer view of where Claude adoption was creating value and where AI usage needed additional control.
3. Model Routing and AI Workflow Optimization
As OmiSoft adopted Claude more broadly, Umbrelly helped design an internal model-selection framework.
The goal was to avoid using the most advanced and expensive model for every task. Instead, Umbrelly helped OmiSoft match each workflow with the appropriate model based on task complexity, quality requirements, latency, and cost.
The framework separated workflows into categories:
High-complexity tasks: architecture support, advanced reasoning, complex code review, and critical engineering decisions
Medium-complexity tasks: requirements analysis, product specifications, and QA scenario generation
Low-complexity tasks: summarization, classification, documentation formatting, repetitive text generation, and internal communication drafts
This helped OmiSoft preserve access to the strongest Claude models for high-value work while routing simpler tasks to more cost-efficient options.
4. Responsible AI Usage Framework
Because OmiSoft developers work with client requirements, private codebases, business logic, and product documentation, Umbrelly helped create a responsible AI usage framework for AI-assisted software delivery.
The framework included guidance on:
What information can and cannot be sent to AI tools
How to anonymize sensitive client data
When human review is required
How to handle AI-generated code
How to reduce hallucination risk in technical outputs
How to separate internal experimentation from production workflows
How to govern AI usage across teams and employees
This helped OmiSoft scale Claude adoption while keeping security, quality, and delivery accountability in place.
Results
After implementing Umbrelly’s Claude adoption and optimization framework, OmiSoft achieved measurable improvements across documentation, QA, cost visibility, and AI workflow control.
Key results included:
40% less time spent preparing project documentation
30% faster QA test-case preparation
Lower cost per AI-assisted development task through model routing and workflow optimization
Improved visibility into Claude usage by department and individual employee
Clearer understanding of which AI use cases justified continued Claude usage
More structured adoption of Claude across development, product, QA, and delivery teams
Better internal governance for AI-assisted work involving client requirements, private codebases, and technical documentation
The most important result was that OmiSoft moved from experimental AI usage to a controlled, measurable, and scalable Claude adoption model.
Business Impact
Umbrelly helped OmiSoft make Claude adoption more practical at the company level.
Instead of treating AI as a set of isolated productivity experiments, OmiSoft gained a structured operating model for using Claude across software delivery teams.
The company could now answer critical management questions:
Which AI workflows create the highest return?
Which teams are adopting Claude successfully?
Which tasks require the most capable model?
Which tasks can be routed to lower-cost models?
How much does each AI-assisted workflow cost?
How should AI usage be governed across client projects?
This created a stronger foundation for production-ready AI adoption and allowed OmiSoft to increase Claude usage without losing control over cost, security, or quality.
Why This Matters
This case study shows how Umbrelly.Cloud can help software companies adopt Anthropic Claude in a practical and measurable way.
Umbrelly’s role goes beyond basic API access or reseller support. The company helps customers:
Identify the right Claude use cases
Integrate Claude into real business workflows
Track adoption across departments and employees
Measure productivity and cost impact
Optimize model selection
Build internal AI governance
Move from AI experimentation to production-ready AI usage
Train customer teams on practical Claude usage across real delivery workflows
Create repeatable implementation playbooks that can be scaled across other enterprise software companies
Support production adoption with a focus on reliability, security, measurable ROI, and responsible AI practices
For Anthropic, this represents the type of partner-led customer outcome that can help more companies adopt Claude at scale: not only by providing access to the model, but by helping customers use Claude responsibly, efficiently, and with measurable business value.
Customer Quote
“Umbrelly helped us move from scattered AI experiments to a structured Claude adoption framework. We gained better visibility into how our teams use AI, where Claude creates the most value, and how to scale AI-assisted delivery without losing control over cost or quality.”
Dmytro Romaniuk, CEO at OmiSoft
Summary
Umbrelly.Cloud helped OmiSoft integrate Claude into software delivery workflows and build a measurable AI adoption framework across development, product, QA, and delivery teams.
Through AI usage tracking, model routing, cost visibility, and responsible usage guidelines, OmiSoft reduced time spent on documentation by 40%, accelerated QA test-case preparation by 30%, and gained better control over Claude adoption across the organization.
The partnership demonstrates Umbrelly’s ability to help enterprise software companies move from experimental AI usage to controlled, production-ready Anthropic Claude adoption.