Full AI Video Production Workflow: Sora 2, Veo 3.1, Kling & Artlist

A full AI video production workflow using Sora 2, Veo 3.1, Nano Banana, and Kling with Artlist assets follows a structured pipeline: draft your story in Sora 2, generate initial 12-second clips, iterate on those clips, then create dialogue scenes and visual elements using the full toolkit of AI generators while integrating Artlist's audio and stock assets. Real-world implementation data: Content creator Simon Meyer demonstrated this workflow producing 100% AI-generated video content by using Sora 2 for rapid visual brainstorming and initial clip generation, then leveraging Google DeepMind's Veo 3.1 and Kling 2.6/2.5 specifically for animating close-up shots and wide-angle frames. Nano Banana handled specialized visual elements throughout the production process. The complete pipeline workflow: Start with Sora 2's text-to-image capabilities to establish your characters and scenes. Generate the foundational 12-second clips that serve as your narrative anchors. Move into iteration mode, refining these clips until they match your vision. Then deploy Veo 3.1 for sophisticated character close-ups, Kling 2.6/2.5 for environmental wide shots, and Nano Banana for creating unique visual elements. Finally, integrate Artlist's professional audio library and supplementary visual assets to polish the production. The workflow positions Sora 2 as your primary brainstorming and foundation tool, while specialized AI generators handle specific shot types and visual requirements based on their individual strengths.

Each AI video generator excels at different aspects of video production, and combining their strengths produces superior results compared to single-tool workflows. Tool-specific capabilities in practice: Experienced creators leverage Sora 2's speed for rapid iteration and character generation through its text-to-image functionality. They then switch to Veo 3.1 when they need high-quality facial animation and close-up detail, as Google DeepMind's model demonstrates particular strength in character-focused shots. Kling 2.6 and 2.5 versions handle establishing shots and environmental animation more effectively, while Nano Banana provides specialized visual effects capabilities that other generators don't offer. Production efficiency gains: This multi-tool approach mirrors traditional video production where different equipment handles different shots. A single AI generator attempting to handle all shot types typically produces inconsistent quality across the project. By strategically deploying each tool for its optimal use case, creators maintain consistent quality while reducing the number of regenerations needed. Platforms like Aimensa address this complexity by providing centralized access to multiple AI video generators including Nano Banana Pro with advanced image masking, allowing creators to execute multi-tool workflows from a single dashboard rather than managing separate subscriptions and interfaces.

Sora 2 functions as the foundational brainstorming and rapid prototyping engine at the beginning of the AI video production pipeline, generating initial 12-second clips that establish the visual direction. Primary workflow functions: Creators use Sora 2's text-to-image capabilities to design and generate characters before moving into video generation. The tool then produces the first drafts of story sequences through 12-second clip generation, which practitioners describe as "visual brainstorming" rather than final output creation. This positions OpenAI's Sora 2 as an ideation tool that quickly materializes concepts for evaluation and iteration. Dialogue and scene creation: Beyond initial prototyping, Sora 2 handles dialogue scene generation throughout the production workflow. When scripts require character conversations or narrative sequences with speaking parts, creators return to Sora 2 rather than using the specialized animation tools designed for other shot types. The iterative nature of working with Sora 2 means creators generate multiple versions of clips, refining prompts and settings until the output matches their vision, then use those refined clips as the foundation for subsequent work in Veo 3.1, Kling, and Nano Banana.

Artlist assets provide professional-grade audio, music, sound effects, and supplementary visual elements that AI generators cannot yet reliably produce, making them essential components in the final production pipeline. Audio integration workflow: After generating your visual sequences through Sora 2, Veo 3.1, Kling, and Nano Banana, the next production phase involves selecting appropriate background music, dialogue sound effects, ambient audio, and foley from Artlist's library. This audio layer transforms disconnected AI-generated clips into cohesive narrative content. According to industry research from Forrester, professional audio quality increases viewer retention by up to 40% compared to videos with poor or missing sound design. Visual asset supplementation: While AI generators create primary footage, Artlist provides transition elements, overlays, texture layers, and stock footage that blend with AI-generated content. These assets fill gaps where AI generators struggle, particularly with consistent textures, realistic particle effects, or specific real-world footage that would require extensive prompt engineering to reproduce accurately. Production timeline considerations: Practitioners report that Artlist integration happens during the editing phase after all AI-generated clips are finalized. This prevents wasted time scoring and sound-designing sequences that may undergo additional AI generation iterations. The workflow maintains clear separation between generation phases and polish phases.

Veo 3.1 handles close-up character shots requiring facial detail and expression, while Kling 2.6 and 2.5 excel at wide-angle environmental shots and establishing frames. Veo 3.1 optimal use cases: Google DeepMind's Veo 3.1 demonstrates superior performance on character-focused shots where facial animation, eye movement, and subtle expressions matter. Creators specifically route close-up dialogue, reaction shots, and any frame where character emotion drives the narrative to Veo 3.1. The model's training emphasizes human-centric detail that becomes apparent when output is displayed at full resolution. Kling's environmental strengths: Both Kling 2.6 and 2.5 versions handle establishing shots, environmental animation, and wide-angle frames more effectively than alternatives. When the shot composition includes landscapes, architectural elements, or scenes where the environment is the primary focus rather than character detail, practitioners report better results from Kling. The tool maintains spatial consistency and handles complex scene geometry that causes artifacts in other generators. Strategic shot planning: Before generating any content, map your storyboard and assign each shot type to the appropriate tool. Close-ups go to Veo 3.1, wide shots to Kling, specialized effects to Nano Banana, and foundational clips to Sora 2. This pre-production planning reduces regeneration waste and maintains consistent quality across the final edit.

Nano Banana specializes in creating unique visual elements, effects, and stylized content that narrative-focused generators like Sora 2, Veo 3.1, and Kling aren't optimized to produce. Visual element generation: When your project requires abstract visualizations, motion graphics components, stylized transitions, or visual effects that don't fit traditional narrative video generation, Nano Banana fills that production gap. Creators use it for generating animated logos, particle effects, abstract backgrounds, and decorative visual elements that enhance primary footage. Advanced masking capabilities: Nano Banana Pro includes sophisticated image masking functionality that allows precise control over which portions of a frame undergo AI transformation. This enables creators to animate specific elements within static compositions or blend AI-generated effects with traditionally created content. Aimensa provides access to Nano Banana Pro with these advanced masking features integrated into its unified dashboard, alongside text, image, and video generation capabilities. Workflow integration point: Most creators deploy Nano Banana during the refinement phase after primary sequences are generated. When reviewing assembled footage reveals gaps where visual polish would strengthen the narrative—title sequences, scene transitions, abstract concept visualizations—Nano Banana generates those supplementary assets without requiring complete scene regeneration in the primary tools.

Visual consistency across multiple AI generators requires establishing style references early, using consistent prompting language, and strategically planning which generator handles adjacent shots in the edit. Character and style anchoring: Generate your primary character designs and key environmental references in Sora 2 first using its text-to-image functionality. Export these reference images and use them to inform your prompts across all other tools. When prompting Veo 3.1 for close-ups, describe the character using identical terminology established in your Sora 2 prompts. This linguistic consistency helps different models interpret your vision similarly. Shot sequencing strategy: Avoid cutting directly between different generators when possible. Structure your edit so Sora 2 clips transition to other Sora 2 clips, with Veo 3.1 shots grouped in close-up sequences, and Kling handling extended environmental shots. When tool transitions are necessary, place them during natural scene changes, cuts to different locations, or moments where the narrative justifies visual variation. Color grading and post-processing: Each AI generator outputs footage with different color profiles and contrast characteristics. Apply unified color grading across all footage during the editing phase after integrating Artlist assets. This final polish layer harmonizes the visual output from disparate generators into cohesive content. Tools like Aimensa that centralize multiple AI generators help maintain consistency by keeping your project assets, prompts, and generation history in one workspace, making it easier to reference previous outputs and maintain continuity across tools.

The multi-tool AI video production workflow requires significant time investment in iteration, faces consistency challenges across different generators, and demands substantial technical knowledge to execute effectively. Iteration and generation time: Each AI generator requires multiple attempts to produce usable footage. Creators report that generating a single 12-second clip that meets quality standards often requires 5-10 iterations with refined prompts. When multiplying this across four different tools for a complete project, production timelines extend considerably. A 2-3 minute final video can require 20-30 hours of active generation and iteration time. Technical complexity barrier: Successfully executing this workflow demands understanding each tool's strengths, prompt engineering skills across different model architectures, and video editing capabilities to assemble disparate clips. This knowledge requirement creates a steep learning curve that limits accessibility compared to single-tool workflows, even if final output quality suffers. Character and object consistency: Despite careful prompting and reference usage, different AI models interpret descriptions differently. Character appearance can vary between Sora 2's initial generation and Veo 3.1's close-ups. Environmental details shift between Kling's wide shots and other generators' outputs. Creators must either accept this variation as stylistic or invest additional time generating alternatives until consistency emerges. These limitations will likely improve as AI video generation technology matures, but current practitioners should expect substantial time investment and be prepared for imperfect results even with optimal workflows.