Hero Releases SDK That Autocompletes AI Prompts

Hero, a productivity startup founded by ex-Meta engineers, announced an invite-only software development kit that can autocomplete AI suggestions using context—thereby converting vague intentions into structured queries ready to run.

The idea is to reduce the back-and-forth that defines chat-based interfaces and to move more interactions into a one-shot execution.

What the SDK Does: Turning Prompts Into Structured Tasks

“Rather than trying to remember every parameter an AI action requires, our SDK takes these fields and brings them to the surface as you type,” explained Hero co-founder Nicholas Tannahui. Write “Book a flight” and the SDK starts building a full command that includes slots for origin, destination, date of flight, time of day, airline selection, and return legs. You can press the Tab key to iterate through the suggestions, add or overwrite values, and execute whenever it’s ready.

The same applies to creative tools: an image or video generator can ask for style, subject, location, lighting, lens—anything—and refine the command step by step so only one token hits the model. It’s similar to the cue-rich prompt panels that tools such as Adobe’s Firefly use for soundtracks and visuals, but available to everybody by way of a drop-in component.

Why Prompt Autocomplete Matters for Faster AI Workflows

Prompting is the biggest bottleneck in a lot of AI workflows. Users almost always know what they are supposed to have at the end but may not know which words, which parameters are missing, or which steps need to be in order. That friction manifests in usage data. Teams tell us that they have to exchange long chains of conversation just to get a usable result, which inflates latency and costs.

Hero claims that its autocomplete reduces multi-turn flows to a fraction of the messages, and has seen up to 10x faster completion in internal testing. That’s plausible given the history of autocomplete elsewhere. Gmail’s Smart Compose has reportedly saved billions of characters typed every week; and in 2023 a study from GitHub found that developers worked up to 55% faster with AI pair programming. Like speed, it comes from a reduction of cognitive load and a reduction in the number of keystrokes that one would otherwise be typing; structured prompts reduce the cognitive load for AI actions similarly.

There’s also a cost angle. The use of tokens increases with conversation depth. Fewer rounds, of course, mean fewer tokens, which leads to lower inference bills for apps at scale. For high-traffic assistants, reducing token consumption by 10–20% can shift unit economics significantly.

How Developers Can Use It in Apps and Assistants

Thus, Hero describes the SDK as a context-aware layer that exists between the user input box and the call to the model. Developers can also define action schemas—think “book travel,” “file a ticket,” “generate ad creative,” or “summarize contract”—and the SDK will prompt users to fill in any mandatory or optional fields they’ve defined using natural language. At its core, this involves a mixture of intent detection, slot filling, and function calling that yields a nicely formed payload.

The product is invite-only; Hero is currently working with early partners to optimize domain-specific schemas. That co-development trajectory is not uncommon for AI infrastructure at the moment; teams want drop-in UX that doesn’t break their data models, compliance needs, and latency budgets.

AR Roots and Interface Limitations Shaping the Design

Hero’s founders applied lessons learned from developing augmented reality features at Meta, where the devices’ small screens forced ruthless simplicity. AR forced them to learn that users aren’t going to wade through nested menus and elaborate prompts; they’re going to respond to smart defaults and lightweight parameters. Taking that mindset to AI chat converts freeform text into a guided workflow without feeling like a form.

Use Cases and Early Partners Testing the SDK in the Wild

Hero illustrates use cases in travel booking, commerce, advertising, and customer support. For example, in customer service, the user might type “Fix my refund,” and see the SDK surface order number, item, reason, and amount, as well as a preferred resolution already pre-populated from account context when possible. For ads, setup of a campaign can be expressed in one pass, with objective, budget, audience, and placements added along with creative specs.

The company is also testing autocomplete within its own app to help users find time for meetings and arrange catch-ups (automatically pulling calendars, time zones, and locations). Meanwhile, Hero says it is also considering ad experiences with Koah Labs in which “brands can show up as contextually relevant suggestions within autocomplete,” an idea that would require delicate guardrails to maintain trust.

The Money and Market Behind Hero’s SDK Launch

Hero previously raised $4 million in seed funding and nabbed another $3 million led by Forerunner Ventures to power the SDK’s launch. The company says that additional, larger rounds may follow, as developer adoption and usage scale.

The timing is in line with growing corporate interest in generative AI. The use of generative AI has overtaken that which is used purely for processing, as found in the company’s 2024 research, with organizations’ usage remaining static year over year and emphasizing more developer-ready tooling for teams that want measurable productivity gains, not just novelty features.

The Competitive Landscape for Prompt Autocomplete SDKs

There are plenty of apps that sprinkle suggestion chips (or “try this” prompts), and workflow builders that wrap models with forms. Hero’s bet is that autocomplete belongs at the input layer, where it can act as a reusable SDK and doesn’t need to be totally reinvented for every app. The reference point is code autocompletion: once developers got used to IDE-quality suggestions in any context, it was impossible to go back to the wilderness.

If the SDK truly reduces the number of prompt turns and costs, and improves completion rates, this could become a dominant way users talk to AI—structured when it counts, free form when it doesn’t. It’s a small UX shift that has an outsized impact on how long it will take for AI to start doing real work.