AI video and ad generation for campaigns
We build pipelines for teams that need many video and ad variants without turning the brand into a mess. The work covers scripts, creative generation, avatar, voice and video APIs, review workflow, tone rules, and handoff to production.
What can be automated in video and ads
We start with the campaign: which formats are needed, who approves creative, where brand rules live, and how finished assets reach performance or SMM teams.
Scripts and hooks
We generate drafts for segments, offers, formats, and video lengths, then separate usable production options from noise.
Ad creative variants
We prepare copy, frame structure, CTA, subtitles, and adaptations for TikTok, Reels, Shorts, or performance channels.
Avatar, voice, and video APIs
We connect voice, avatar, video, image, and post-production generation through the right APIs for the job.
Review workflow
We add statuses, comments, versions, approval roles, and a clear path from idea to final file.
Brand guardrails
We encode tone, banned claims, legal limits, visual rules, and required disclaimers.
Production handoff
We prepare asset export, metadata, naming, version history, and integrations with working storage.
When a custom pipeline is worth it
Off-the-shelf AI tools are fine for a one-off video. A custom system starts to matter when video and ads become a repeated production process.
You need dozens of variants across offers, languages, audiences, and channels.
The brand has rules that cannot live only in an art director's head.
Creative needs approval from marketing, legal, a client, or production.
Avatar, voice, video, image, and LLM APIs need to work inside one managed flow.
What the build includes
Task and data audit
We inspect real tickets, documents, spreadsheets, and access rules.
Scenario design
We define where AI replies, where it acts, and where a human stays in the loop.
Prototype
We build a working first version against samples from your actual workflow.
Integrations
We connect CRM, messengers, databases, documents, or internal APIs.
Testing
We test on real dialogs, questions, and files, not just friendly demo prompts.
Launch
We put the system into work with clear roles, logs, and control points.
Quality monitoring
We review wrong answers, edge cases, escalations, and user behavior.
Support and iteration
We improve scenarios after launch, once real usage starts showing the truth.
Relevant work
The hard part is not only generation. It is the production flow around it: drafts, selection, versions, quality checks, and team handoff.
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Integrations
We connect LLMs, image generation, video APIs, voice APIs, avatar APIs, storage, spreadsheets, CRM, task trackers, and internal panels.
Brand and rights control
In an ad pipeline, a mistake rarely looks like a normal software bug. It is more often the wrong tone, a risky claim, a missing disclaimer, or a creative the brand should not publish.
Brand guardrails become part of the system: tone of voice, banned topics, claims, visual rules, and required warnings.
Sensitive creatives can require human review before export or publication.
Script versions, prompts, assets, and approval decisions are logged.
Drafts, approved materials, and production assets stay separated.
Timeline and working format
Fast audit
2-3 business days when sample data and a process owner are available.
Prototype
1-2 weeks for a narrow scenario with a limited integration set.
MVP
3-6 weeks when the system needs real integrations and team access.
Production
Timeline depends on integrations, data quality, and security requirements.
Pricing
Pricing depends on integrations, data quality, access roles, testing scope, and infrastructure requirements. Each stage is paid separately.
Discovery
A paid review of the task, data, risks, and first sensible scope.
Prototype
We test the scenario on a small data set before debating it in theory.
MVP
We build a working version with UI, integrations, and basic quality control.
Production system
We harden the system for access control, logs, operations, and support.
Support
We monitor quality, fix issues, and add new scenarios after launch.
Why azamat.ai
We connect AI generation to a real product and production workflow.
Our work covers LLMs, images, voice, video APIs, interfaces, and internal tools.
We think about versions, roles, approvals, and team handoff from the start.
The founder stays involved in architecture and first-release scope.
We have experience with AI products, creative generators, SMM tooling, and MVP launches.
FAQ
Technically yes, but brands usually need review before export or publication. AI can prepare variants quickly, while a person approves sensitive claims, tone, and final assets.
The choice depends on format, language, rights, voice quality, generation speed, and budget. During discovery we compare options and define a fallback in case a provider changes limits or quality.
Yes. It is better to design this as local adaptation, not direct translation: phrase length, tone, cultural limits, subtitles, and voice pronunciation all matter.
We formalize brand guardrails, add checks, approval roles, and version history. Important campaigns can keep mandatory human review.
Past campaign examples, brand guidelines, platform requirements, required formats, the current approval process, and a few offers we can use to test the pipeline.
Tell us what you're building.
Start with a few detailsWe reply within one business day. Then Azamat joins every first call personally, so you get an honest scope, budget, and fit from the person responsible for delivery.