by huhbai | 2025-07-13 17:09:24+00:00
What If You Could Build a Product With AI — Not Just On AI? Earlier this year, I set out to explore a simple but ambitious question: Could I co-develop a real, production-grade platform with AI as a partner?

The result of that experiment is now something more: it’s called Huhb.

Where It Started I’ve been building web platforms for over 20 years — as a developer, a technical product manager, and someone who cares about turning complex systems into clean developer experiences. My work with companies like Leaf Agriculture and Hapi Cloud showed me the deep challenges in integration. Tools like Twilio proved that powerful platforms don’t need to be complicated to use.

So I asked:

What if we unified access to AI providers the way Twilio unified messaging?

That question became the foundation of Huhb — a platform that abstracts the complexity of AI provider integrations and gives developers tools to build smarter, faster, and with more confidence.

What We’re Building At its core, Huhb is an AI orchestration platform. But under the hood, it’s designed to solve some very real developer pain points — backed by research, interviews, and lived experience.

Here’s what we’re addressing:

Prompting shouldn't feel like alchemy. We replace guesswork with structure — through intent-aware tasks, strategy-driven templates, and prompt planning that adapts based on task type, tone, and constraints.

Provider selection shouldn't be manual. Huhb routes every request through a planning engine + multi-armed bandit (MAB) system that optimizes for cost, speed, and quality — and learns from past usage.

Semantic drift is real. The same prompt can produce wildly different results across models. Huhb standardizes task execution so outputs are more consistent, versioned, and traceable — especially important in enterprise settings.

AI tools lack an “intent layer.” Most APIs treat input like just another string. We treat it as a goal. Huhb workflows are structured around what you’re trying to accomplish — summarization, translation, classification, etc. — not just the provider’s preferred format.

Tracking AI costs gets messy fast. We’ve centralized usage tracking and added per-request cost estimation. Developers see what every task costs, how different models perform, and where money is going.

Prompt reuse, validation, and benchmarking are missing. Huhb supports dynamic, reusable task templates that can be previewed, validated, and optimized — and tied to real-world metrics about token use, latency, fallback rates, and cost.

Why Build It This Way? Because most AI tooling today is either:

Too abstracted, hiding important control from developers

Or too raw, requiring you to reinvent routing, retries, and cost logic every time

I wanted to build something that respected developer intent and product realities.

That meant building:

A GraphQL-first API

SDKs and a CLI to streamline usage

Support for bulk file processing and multimodal inputs (PDF, DOCX, CSV, etc.)

Execution guardrails based on file type and workflow mode

A plugin architecture for preprocessors, postprocessors, and smart fallback logic

All of this co-developed — with AI in the loop — through deliberate, small iterations.

What Comes Next? We’re planning a small Alpha launch later this year. This post isn’t a launch — it’s a toe in the water.

But if any of this resonates with you — if you’ve ever tried to wrangle OpenAI + Claude + Mistral + whatever comes next into a single product, or if you’re just tired of prompt tweaking and cost surprises — then we’d love to connect.

We’re building Huhb to make AI predictable, programmable, and product-ready.

Let’s see where it goes.

#ai #developerexperience #productmanagement #graphql #startups #openai #twilio #huhb