Questions, answered.

An AI-native design and media firm builds its entire production engine around AI, rather than bolting AI onto a traditional agency. A small senior team owns strategy, taste and quality; AI systems generate the bulk of the design and media output — brand assets, content, video, ad variants, interfaces. Srushta is an AI-native firm: we automate creative and media production across every channel and turn AI prototypes into production products.

A traditional agency staffs people to do the work and uses AI as a helper. Srushta inverts that: AI does 70–90% of the production and senior humans do direction and QA. That means faster turnarounds, far more output per brief, and a pipeline you own instead of an open-ended retainer. We also cover engineering — we ship products, not just decks.

Yes — this is one of our core services. We audit AI-generated prototypes from Lovable, v0, Bolt, Replit and Cursor, pick the right one to build on, and rewrite it to production standard: clean architecture, real authentication, tests, CI/CD, accessibility and observability. We can also maintain a whole fleet of prototypes so your team keeps shipping.

It means one connected system rather than a tool here and there. From a single brief we generate on-brand output across social, video and motion, advertising, editorial and long-form — localized and versioned — with human review gates. Brand, content, product and media run on the same AI-native operating system.

Primarily founders and startups — from pre-seed through Series A — plus scale-ups and funds that need design and media to move at AI speed. Our roots are in boutique startup consulting, and we have rebuilt the firm AI-native for that same audience.

Most engagements start with a paid scoping sprint, then move into either a fixed-scope build (for example, prototype-to-production) or a monthly fractional model where we embed as your design and media team. You own the systems, pipelines and assets we build.

Both — that is the point. Senior designers and directors own taste, strategy and the final cut; AI provides the production leverage. Nothing ships without a human review gate. You get agency-level craft at a speed and volume no human-only team can match.

Book an intro call. We will talk through what you are building, where design and media are the bottleneck, and whether a build, a fractional engagement or a prototype rescue is the right first step.

Almost never everything. The honest pattern, and the one we follow, is a partial rewrite: the interface is largely reusable, so most of the UI survives; authentication is almost always replaced, because generated auth tends to be a mock with the shape of a login; the database is redesigned around a real data model; and the app structure is refactored. You keep the shape, not the system. We decide harden-versus-rebuild per layer during the audit rather than as one all-or-nothing call, which is what keeps this a weeks-long project instead of a year-long one.

We treat prototypes as a portfolio with rules rather than a pile of experiments. Every prototype gets a registry entry — an ID, an owner, the goal, the target user, its status and a live URL — and a promotion path with an explicit bar at each step: demo, validated, hardened, launched. Nothing advances without meeting the next bar, and nothing lingers: an archive policy tears down dormant deployments, revokes their credentials and archives the repository read-only. That last part matters more than it sounds, because an abandoned prototype still holds live API keys and customer data for as long as it stays reachable.

Observability, added before launch rather than after the first incident. That means structured logs, error tracking, uptime and performance monitoring, and alerts that reach a human — so you learn about an outage from your monitoring, not from a customer. AI generators essentially never produce any of this, because a demo has no operational life to observe. Along with a staging environment where changes are verified before real users see them, it is usually among the first things we add.

All three, in the right layers. Keep the in-house owner accountable for brand strategy, priorities, approvals and final quality control — that judgement should never leave the building. Use external specialists for execution. And put systems underneath both: brief templates, asset libraries, generation pipelines and review gates that carry the volume. The failure mode we see most is a team hiring for execution capacity when the actual constraint is intake and approvals, which no amount of headcount fixes.

It publishes. A pipeline that generates beautifully and then hands off through manual upload has not removed the bottleneck — it has relocated it. We build the orchestration layer that runs the whole route (brief, draft, generation, format variants, QA gate, schedule and publish) and the connectors into the systems that actually distribute: CMS, email platform, ad manager, social scheduler, DAM, CRM. The point of an AI-native pipeline is that a brief comes in one end and on-brand assets land in the channel at the other.

Most engagements open with a paid scoping sprint, which is deliberately small and produces something useful whether or not you continue. From there it is either a fixed-scope build — prototype-to-production work is usually quoted this way, because the work is knowable after the audit — or a monthly fractional engagement priced on capacity rather than hours. We do not sell an open-ended retainer, and we would rather scope one sharp piece of work than sell you a subscription you have to justify each quarter. Exact numbers depend on scope; the intro call is where that gets specific.

You own what we build for you: the pipelines, the prompt and asset libraries, the design system, the code and the output. On copyright the honest answer is that the law is still unsettled: in several jurisdictions, including the US, registration generally requires human authorship, so purely machine-generated output may be weakly protected or unprotectable. This is one practical reason our work keeps humans at the direction and selection decisions rather than only at the end. We will tell you plainly which parts of a deliverable carry that uncertainty rather than pretend the question is settled.

Your material is used to do your work, and not to train models. We run on commercial model APIs under terms that exclude training on submitted content, we keep client material in per-client workspaces rather than a shared pool, and we scope access to the people on your engagement. When an engagement ends we hand over the assets and pipelines and remove what you ask us to remove. If you have specific requirements — data residency, a signed data processing agreement, retention limits — raise them on the intro call and we will tell you straight whether we can meet them.

Because the traditional agency model prices labour, and AI is deflating the cost of the labour it prices. Creative production, asset variation and reporting are increasingly software-driven, which compresses cycle times and lets one idea become many channel-specific variants cheaply. The thesis is that firms whose margin comes from systems and relationships rather than billable hours capture that shift, while firms whose revenue scales with headcount are exposed to it. We are built for the first case — and we would note that this is an argument about structure, not a promise about any particular firm.