AI Video Production Workflow: A Behind-the-Scenes Guide

AI video tools are everywhere now. But turning AI-generated clips into a polished, launch-ready video? That’s still production work and raw creative talent.

When Parcha needed a video to introduce GREP, their AI-powered research engine for compliance teams, we had 27 days to ship something credible, clear, and visually cohesive. 

With 91% of businesses now using video as a marketing tool and 89% of consumers saying video quality impacts their trust in a brand, the stakes for getting it right are high – especially in regulated industries.

No reshoots, no live-action budget, no “generate and pray for the best.”

So, if you’re curious how that turned out, here’s how we approached it, what broke, what worked – all wrapped in a workflow you can adapt for your own AI video projects.

How we approached a 27-Day AI video production for a fintech product launch

Before we begin, here’s a quick summary: 

• Client: Parcha (AI compliance software)
• Product: GREP (AI-powered research engine)
• Timeline: Dec 1, 2025 → Jan 8, 2026 (27 business days)
• Versions: v4 through v8.1
• Production model: VFX-driven / short-film approach
• Creative concept: Matrix-inspired transformation narrative – 90s office chaos to AI-powered clarity
• Visual style: Brutalist + retro-futuristic
• Key deliverable: Launch-ready AI video with open spaces for the client’s own demo reel and product showcase edits
• Target launch: January 15, 2026

Who are Parcha?

Parcha builds AI software for compliance and research workflows in regulated industries – fintech, banking, and financial services. GREP is their AI-powered research engine: A tool that automates deep research on entities, documentation, and risk signals.

When we started working on this, one thing became very clear: The main challenge wasn't explaining the features. It was making an abstract, technical product feel real to an audience that doesn't trust easily.

Compliance buyers truly care about credibility. If they see a video that looks rushed, inconsistent, or gimmicky, it actively undermines their trust. So, our brief was clear from the beginning: The video needs to be launch-ready, visually credible, narratively coherent.

Why AI video made sense for this project

The choice of AI-generated video was strategic.

Parcha's whole premise is that compliance and due diligence processes are stuck in the past. GREP exists to modernize workflows that still rely on endless browser tabs, keyword documents, and manual searches. So, naturally, the video needed to dramatize that transformation: From the 1990s office chaos environment to AI-powered clarity (Matrix style without the dystopia).

Using AI to make the video reinforced the message because the medium truly matched the product. If GREP represents the future of due diligence, the video introducing it should feel like it came from that same future.

The creative concept leaned into this contrast: A Matrix-inspired narrative where the protagonist escapes a cluttered 90s cubicle, clicks into GREP, and emerges in a sleek, modern workspace. Brutalist aesthetics. Retro-futuristic tone. Analog chaos versus digital precision – visualized.

What's the hardest part of AI video production?

If you know anything about AI videos, it’s probably that the biggest issue is staying consistent. Even if you do get all five fingers to show up properly, characters might still drift between frames, environments will shift, and the dreaded “AI look” creeps in when scenes don’t hold together.

To tackle that, we needed to treat AI video more like a VFX-driven production rather than a shortcut. In short, we needed the following: 

• A visual system that could survive iteration.
• A workflow that distinguished between quick fixes and full regeneration.
• A review process that wouldn't reopen scope every round.

How to storyboard for AI video (constraints-first approach)

Why story comes before software in AI video

It’s tempting to jump straight into generation tools once a project kicks off. But for GREP, we started with a story, not the software.

The goal was to position GREP’s function as a coherent narrative rather than a feature list. What does this tool actually do for someone? What’s the emotional arc? Before picking any AI workflow, we needed to know what we were actually trying to say.

This meant defining tone, pacing, and visual language upfront – decisions that would shape every generation prompt downstream.

What to lock down before generating anything

AI video breaks when things drift. So before generating anything, we established visual rules: What must stay consistent across every scene?

For GREP, that list included:

• Character design and appearance.
• UI frames and interface elements.
• Environment style and lighting mood.
• Logo usage and placement.
• Typography and text treatment.

Think of it as a shot bible for AI. The more you lock down early, the less you’re fighting consistency issues later. These anchors gave us something stable to build from – and made feedback rounds far less chaotic.

How to write scripts for AI-generated video

Be comprehensive, not clever 

When you’re explaining an AI compliance product to a skeptical audience, clarity beats cleverness every time.

Our script for GREP prioritized comprehension. Short sentences. Concrete language. No jargon designed to impress – just enough to help viewers understand what the product does and why it matters.

AI tools can help with phrasing variants or tightening transitions, but the core messaging? That stayed human. Tone and positioning are too important to outsource to a prompt.

Write for modular scenes

One thing AI video forces you to learn very fast is that clips break all the time. Current tools tend to lose coherence after a few seconds, and regeneration is expensive.

So we wrote the script in modular, self-contained scenes. Each segment could stand alone, which meant if one scene needed regeneration, we weren’t dragging the whole timeline with it.

This also made post-production far easier. When scenes are designed as independent blocks, you can swap, reorder, or cut without unraveling the narrative.

How to build a visual system that survives AI regeneration

Visual system design (rules, not vibes)

Once the script was locked, we moved into building the visual universe. Not just “what looks cool” – but what rules will keep this thing coherent across dozens of generations?

For GREP, we established clear guidelines for:

• Environments: Style, lighting mood, color palette.
• Characters: Appearance, proportions, clothing.
• Interfaces: UI frames, typography, how screens appear in-scene.
• Temporal references: Period-accurate details where needed (the project included legacy OS environments like Windows 98 for certain scenes).

What the shot bible actually looked like

To give you a concrete example, here's what we locked down for the GREP video:

Character consistency:

• Same protagonist throughout (we called him "Compliance Collin" internally).
• 90s look in the opening beats: period-appropriate clothing, hairstyle, environment.
• Modern look in the final beat: updated wardrobe, calm and efficient.
• No aging transformations or visual discontinuity between scenes.

Color story by scene:

• 90s office scenes: Beige and teal CRT cast, fluorescent lighting.
• Transition: Full white bloom.
• Data cathedral/servers: White space with subtle purple GREP accent lighting.
• Modern office: Fintech neutrals, soft warm highlights.

Cinematography rules:

• Smooth dolly movements or minimal pans only.
• No handheld – too risky for AI consistency.
• Clean continuity across all eras and environments.

Example: The consistency rules we used for GREP

Another way in which AI video production differs from traditional workflows is that the assets you create have to survive regeneration.

That meant building:

• Anchor frames: Start and end frames for key scenes that give AI tools something stable to build from.
• Style templates: Reusable visual references that keep generations on-brand.
• UI layers: Interface elements that could be composited consistently across scenes.
• 3D backdrops: Environments built for visual cohesion, not just single-shot use.

The goal was a library of assets we could feed into generation tools repeatedly – without losing the visual language every time we needed a new take.

How to direct AI video generation without losing control

Technical architecture planning (the part most teams skip)

Before generating a single clip, we mapped out which AI workflows and tools would handle which scenes. This isn’t glamorous work, but it’s the part that prevents downstream rework.

Different scenes have different requirements. Some need photorealism. Some need stylized motion. Some need tight control over UI elements. Planning these decisions per-scene – rather than figuring it out mid-project – kept the production on track.

The critical distinction: Edit-safe changes vs. true AI regeneration

This is the production lever that controls whether your project stays on schedule or blows up.

Edit-safe changes can be handled in post without regenerating footage:

• Color correction and grading.
• Audio swaps or adjustments.
• Text overlays and lower thirds.
• Minor compositing tweaks.

True regeneration means going back to the AI tools and generating new footage:

• Character movement or positioning changes.
• Environmental alterations.
• New scenes or significant scene modifications.
• Fixing consistency issues baked into the generation.

The difference matters because regeneration is expensive – in time, in iterations, and in risk of introducing new inconsistencies. When feedback came in, we categorized every request: Is this edit-safe, or does it require regeneration? That question shaped our response time and kept scope from spiraling.

Tooling reality check – what AI still struggles with

AI video tools have improved dramatically, but they’re not magic. Current limitations we designed around:

• Object permanence: Things disappear or transform unexpectedly between frames.
• Causal reasoning: Effects sometimes happen before their cause (a door opening before the handle moves).
• Consistency over time: The longer the clip, the more drift creeps in.
• Fine motor details: Hands, fingers, and precise movements remain unreliable.

Runway’s Gen-4.5 and similar tools are getting better at physical accuracy, but these issues haven't disappeared. The truth is that we can’t work with the idea that a better tool will come around eventually. Instead, we need to always design scenes that work around the limitations: Short clips, stable anchor frames, and a modular structure.

How sound design builds credibility in AI video

Build the animatic early (rhythm beats polish)

Once we had raw footage from generation, we didn’t jump straight into polish. First, we assembled an animatic – a rough, working cut of the full video.

The animatic isn’t meant to look good. It’s meant to feel right. Does the pacing work? Do scenes flow into each other? Where does the rhythm drag or feel rushed?

This is where we auditioned music tracks, tested voiceover timing, and figured out which scenes needed to linger and which needed to move faster. Getting rhythm right early saved us from expensive rework later – because fixing pacing problems after polish is painful.

Why audio quality matters more than you think in B2B video

For B2B audiences – especially in compliance and fintech – production quality matters. A video can have stunning visuals, but if the audio feels thin or off, the whole thing reads as amateur. And amateur doesn’t build trust.

For GREP, we invested in proper sound design:

• Sound VFX: Audio cues that reinforce narrative beats and transitions.
• Audio layering: Building depth so the soundscape feels intentional, not flat.
• Music and VO balance: Making sure nothing competes or drowns out the message.

Post-production – VFX, compositing, color pipeline, finishing

VFX-driven production model

From the start, we treated this project like VFX work – not like traditional video editing.

That meant greenscreen-style compositing: Layering AI-generated footage with additional visual elements, integrating effects, and building scenes from multiple sources rather than relying on single generations to carry everything.

This approach gave us more control. When a generation was 80% right but had issues in one area, we could composite around the problem rather than regenerating the whole scene.

Color pipeline and LUT consistency

AI-generated footage doesn’t come out looking consistent. Different generations have different color temperatures, contrast levels, and overall feel – even when using the same prompts.

The color pipeline was essential for unifying everything:

• LUT selection: Choosing a look-up table based on the original brief, then applying it across all scenes.
• Consistency pass: Reviewing every scene to ensure color, lighting, and mood matched throughout.
• Brand alignment: Making sure the final look felt true to Parcha’s identity.

Without this step, the video would have felt like a patchwork of different sources. With it, scenes that were generated weeks apart look like they belong together.

What makes an AI video brand-safe? (Hint: It's post-production)

Just like with normal videos, raw output isn't launch-ready, and post-production is something no one can escape from. For AI videos, this means: 

• Cleaning up visual artifacts.
• Tightening transitions.
• Adding film grain or texture to avoid the overly-clean AI look.
• Final audio mix and mastering.
• Quality checks for anything that feels off.

Only after these are complete, an AI video becomes brand-safe. Teams that skip or rush this step end up with videos that look like AI demos, not professional productions.

Versioned reviews – How we got to approval without reopening scope

The reality: Briefing and execution in parallel

Here's a reality of fast-moving projects: Sometimes the brief evolves as the work takes shape. Especially with AI video – a relatively new medium – clients and teams are often figuring out what's possible while production is already underway.

For GREP, the creative vision was sharpened through collaboration. Early production explored directions that informed the final approach, and alignment deepened across multiple conversations. This is normal. The key is having a structure that turns evolving direction into progress, not chaos.

That meant the project moved through three distinct phases:

Phase 1: Exploration + early production. Initial concepts were tested visually. Feedback from these early outputs helped clarify what the final video should look and feel like. The brief became stronger because the team had real footage to react to.

Phase 2: Creative lock + system stabilization. Once the direction was clear, we locked the brief. This established a stable framework: what the visual system looked like, what the narrative arc was, what could change, and what couldn't.

Phase 3: Controlled iteration + finishing. With the foundation set, iteration focused on refinement and quality. The version jumps in this phase were polish, not pivots.

How version control kept our AI video project from spiraling (v4 → v8.1)

The GREP project went through versions v4 to v8.1. That's a lot of iteration – but it was structured, not chaotic.

Early versions reflected the exploration phase: Testing creative directions, refining the visual language, and aligning on tone. Later versions were about execution and polish within an agreed framework.

Each version had a clear purpose: What was being addressed, what was locked, and what was still open for discussion. This discipline is what kept the project moving forward instead of circling.

How to filter feedback so it improves the video (not derails it)

Not all feedback is equal. Some notes improve the video. Others unravel it.

Throughout the project, we filtered incoming feedback through a simple framework:

• Must-fix: Issues that break the narrative, undermine credibility, or block launch.
• Should-fix: Improvements that add value if time and budget allow.
• Won't-fix: Requests that would reopen scope, compromise cohesion, or introduce new risks.

Client input is important, but we still needed to protect the work. AI video is fragile – regenerating one scene can introduce inconsistencies that ripple through others. Every change needs to be weighed against what it might destabilize.

Closure mechanics – how to “lock” final delivery

Getting to approval requires explicit closure. For GREP, we framed the final pass as exactly that: The last opportunity for changes before lock.

This meant:

• Clear communication that we were entering the final review.
• A defined window for the last feedback.
• Agreement on what “approved” actually meant.

Without closure mechanics, projects drag, because there’s always one more tweak or one more idea. Framing the last pass as a closure point – not just another round – helped everyone align on getting things done.

What shipped – Deliverables designed for launch reuse

Deliverable specs that support product marketing

The final video was specifically built to support Parcha’s broader launch.

So, we had one specific request from the client: Leave open spaces in the cut for their own edits – places where they could insert demo reels, product showcases, or updated UI footage without re-editing the whole video.

To have this kind of flexibility, you need to plan for it from the script stage and preserve it through post. The result was a launch video that worked on its own, but still served as a foundation for Parcha’s ongoing marketing.

Repurposing plan (hero → cutdowns)

A single hero video is a starting point, not an endpoint. According to recent data, 71% of marketers believe videos between 30 seconds and two minutes are most effective, which means the hero cut needs to be broken down into shorter formats.

For GREP, we planned for repurposing from the start:

• Hero video: The full narrative cut for launch.
• Cutdowns: Shorter versions for social, ads, and different contexts.
• Format variants: Aspect ratios and lengths optimized for specific channels.

Building with repurposing in mind means the hero video goes from a one-and-done asset to a system that keeps delivering value across channels and campaigns.

The playbook – A repeatable AI video workflow for startup teams

If you’re planning your own AI video project, here’s a streamlined version of the workflow we used for GREP. Adapt it to your needs, but don’t skip the discipline.

Pre-flight checklist

Before generating anything, confirm you have:

• Clear positioning and messaging (what are you actually trying to say?).
• Defined audience and credibility requirements.
• Visual references and brand guidelines.
• Stakeholder alignment on scope and timeline.
• Agreement on who approves what, and when.

Shot bible template

Lock down your visual rules before generation begins:

• Character specs (appearance, proportions, clothing).
• Environment guidelines (style, lighting, color palette).
• UI/interface standards (typography, frames, logo usage).
• Temporal references (period-accurate details if needed).
• Mood and tone references (sample frames, moodboards).

Prompt library and asset management conventions

As you generate, you’ll build a library of prompts that work. Document them:

• Save successful prompts with notes on what they produced.
• Use consistent naming conventions for assets and versions.
• Keep source files organized by scene and version.
• Track which tools produced which outputs.

Review cadence and decision rules

Set expectations early:

• How often will you share updates? (Daily? Per milestone?)
• Who gives feedback, and who has final approval?
• What's the process for categorizing feedback (must-fix, should-fix, won’t-fix)?
• When is the final review, and what does “approved” mean?

Why Awesomic for AI Video Production

AI video still needs a team

AI handles generation. But everything around it – creative direction, visual system design, motion work, VFX, sound design, color, finishing – still needs humans.

For the GREP project, that meant:

• Creative direction and narrative alignment.
• Motion design and scene-level generation.
• Greenscreen-style compositing and VFX integration.
• Sound design and audio layering.
• Color pipeline and finishing passes.
• Versioned reviews and feedback management.

AI video isn't a solo tool. It's a production method that requires a coordinated team with real expertise.

The subscription advantage

Awesomic is a talent-matching app that connects you with vetted video professionals – editors, motion designers, VFX artists – through a subscription model.

What that means in practice:

• Predictable pricing: You know your costs upfront. No hourly rates piling up, no surprise fees for extra formats or revisions.
• Fast async iteration: Daily updates on active tasks. You’re not waiting weeks for a first draft.
• Unlimited revisions: Refine until it’s right, without watching a meter run.
• Flexible capacity: Scale up for launches, scale down when you don’t need it.

For AI video specifically, subscription-based talent matching solves the coordination problem. Instead of juggling freelancers across scripting, generation, VFX, and sound, you get a team that works together – with Awesomic handling the matching and handoffs.

If you want to see more examples of what’s possible, check out our Awesomic case studies for a closer look at real projects. For more on video production strategy and workflow, browse our video production insights on the blog.

What to ask for when requesting AI video production

If you’re briefing an AI video project, come prepared with:

• Your positioning: What's the core message? What should viewers understand and feel?
• Your constraints: Timeline, budget, technical requirements, brand guidelines
• Your must-stay-stable list: What absolutely cannot drift across scenes?
• Your deliverables: Hero video? Cutdowns? Format variants? Plan for repurposing upfront.

The clearer your brief, the faster and smoother production goes. AI video rewards preparation.

Ready to talk through your project? Book a demo, and we’ll help you figure out whether AI video is the right fit – and how to approach it.