When an AI Company Builds With AI: Renderforest's Claude Enablement Story
Executive Summary
Project at a Glance
Renderforest, the AI creative platform used by more than 40 million creators worldwide, had already built a mature, high-performing product and engineering organization. As AI reshaped how software gets built, Renderforest saw an opportunity to move beyond individual use of AI tools and establish a structured, organization-wide approach to working withClaude. The company engaged VOLO, as a Claude Partner Network company, to jointly design and implement that initiative.
It was a deliberate investment by a strong team in a new capability: embedding Claude into the software delivery lifecycle to increase the productivity of existing engineers, accelerate product delivery, and reduce the effort spent on repetitive, knowledge-intensive work.
About the Client
Founded in 2013,Renderforest has grown into one of the leading AI creative platforms on the market, giving more than 40 million creators the tools to produce professional videos, images, designs, and websites from a single subscription. Its product suite spans anAI video generator, anAI website builder, anAI logo generator, and a broader library of design and animation tools used by marketers, freelancers, educators, and businesses worldwide.
Behind that scale sits a mature engineering and product organization that has spent over a decade refining how it ships software. Renderforest's teams were already fluent in modern development practices and had individually adopted AI coding tools well before this engagement began. What the company wanted next was structure: a consistent, organization-wide way to bring Claude into how its engineers, product managers, and designers actually work, rather than leaving AI adoption to individual habit.
The Challenge
Renderforest's product and engineering leadership recognized that ad hoc, individual use of AI assistants captures only a fraction of the value available to a team of its size and maturity. The opportunity was to translate Claude from a general-purpose assistant into a purpose-built capability, one that understood Renderforest's codebase, conventions, and delivery process well enough to be useful across the full software lifecycle rather than in isolated moments.
Key objectives for the engagement included:
- Establishing a reusable AI enablement layer instead of relying on general-purpose prompting by individual engineers.
- Giving Claude meaningful context about Renderforest's architecture, coding conventions, and engineering standards.
- Extending AI-assisted workflows beyond development into product research, technical design, documentation, and code review.
- Making these capabilities consistent and repeatable across engineering, product, and operational teams, not confined to early adopters.
- Measuring the impact in terms that mattered to the business: delivery speed, turnaround time, and engineering effort.
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VOLO worked directly with Renderforest's engineering and product teams to identify high-value workflows and translate them into practical, Claude-enabled working practices. Rather than treating AI adoption as a one-time rollout of prompting guidelines, the two teams built a reusable enablement layer tailored specifically to how Renderforest builds software.
Custom Claude Skills and Plugins
VOLO helped Renderforest encode its own engineering practices, recurring development tasks, research processes, and operational workflows into custom Claude Skills, then packaged reusable capabilities as Claude Code plugins so they were available consistently across engineering teams rather than living in one engineer's local setup.
Project and Repository-Level Context
The teams configured Claude at the project and repository level with Renderforest's architectural context, development conventions, and engineering standards, giving Claude the grounding it needed to work like a team member who already knows the codebase.
Specialized Agents and Integrations
VOLO and Renderforest built specialized agents and subagents for specific research, development, review, and analysis activities, and connected Claude to the development tools, repositories, documentation, and internal knowledge sources those workflows depend on.
Standardized Workflows Across the Delivery Lifecycle
The engagement produced standardized, Claude-assisted workflows for code analysis, implementation, refactoring, debugging, testing, documentation, and code review, along with workflows supporting product research, requirements analysis, technical design, and solution exploration earlier in the lifecycle.
The Impact
The engagement moved Claude from an individual productivity tool into a structured engineering capability embedded in how Renderforest works, measured across selected workflows in engineering, product, and operations.
Before → After
Before the engagement, Claude adoption at Renderforest depended on individual habit: engineers who had picked up AI coding tools on their own used them well, but that fluency was not shared, repeatable, or connected to the company's specific codebase, conventions, or delivery process. Research, documentation, and code review remained largely manual, and the value of AI assistance varied from one engineer to the next.
After the engagement, Renderforest had a reusable enablement layer, Skills, plugins, project-level configurations, and specialized agents, giving every engineer access to the same Claude capability, grounded in Renderforest's own architecture and standards. Selected workflows moved measurably faster: teams progressed from product research and technical exploration into implementation sooner, and turnaround on analysis, documentation, and operational tasks dropped by more than half.
Improvement in engineering productivity across selected workflows
Reduction in turnaround time for selected analysis, documentation, and operational workflows
Reduction in time spent on codebase exploration and technical investigation
Reduction in engineering effort required for repetitive implementation, refactoring, testing, and documentation tasks.
Key Takeaways
Structure unlocks the value individual AI use leaves on the table
Ad hoc prompting by individual engineers captures only a fraction of what a well-configured, organization-wide Claude implementation can deliver.
Context is what separates a general assistant from an embedded capability
Giving Claude repository-level context, coding conventions, and engineering standards made it useful across the full delivery lifecycle, not just for isolated tasks.
AI enablement works best when it augments strength rather than fixes weakness
Renderforest's engineering organization was already strong. The gains came from removing repetitive, low-judgment work, not from replacing engineering expertise.
Reusable Skills and plugins compound over time
Because the enablement layer was built to be reusable rather than project-specific, it continues to evolve with Renderforest's organization well beyond the initial engagement.
"Working with VOLO gave our engineering and product teams a structured, practical way to bring Claude into our everyday workflows, not just as a tool individuals experimented with, but as a capability built around how we actually work. The result has been a meaningful, measurable shift in how quickly our teams move from idea to implementation."
Narek Safaryan
CEO, Founder at Renderforest
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