{"slug": "why-were-building-gabbyai-another-ai-assistant-built-differently", "title": "Why We’re Building GabbyAI: Another AI Assistant, Built Differently", "summary": "A developer is building GabbyAI, an AI assistant platform designed around engineering principles of infrastructure control, a clean API, and model abstraction. The platform sits above a SWAI model layer and a Swish API handling authentication, conversations, usage limits, billing, and model routing, with PostgreSQL as the primary database. The stated goal is to decouple the product from any single model implementation so underlying AI infrastructure can evolve without forcing application migrations.", "body_md": "There are already plenty of AI assistants.\n\nSo why build another one?\n\nThat was one of the first questions we had to answer when we started building GabbyAI.\n\nThe answer wasn't that the world needed another chat interface with a different logo. It was that we wanted to build an AI platform around a set of engineering principles that mattered to us: infrastructure control, a clean API, predictable products, privacy, and the ability to evolve the technology underneath without rebuilding everything around it.\n\nGabbyAI is our attempt to do that.\n\nThe assistant is only the visible part\n\nWhen most people interact with an AI product, they see a text box.\n\nThey type something.\n\nThe assistant responds.\n\nBehind that simple interaction is an increasingly complicated collection of systems.\n\nAuthentication. Conversation storage. inference infrastructure. Usage metering. Rate limits. subscriptions. API keys. Model management. Monitoring. Billing. Email verification. Session management. Security controls.\n\nAnd, eventually, multiple models and modalities.\n\nWe decided early on that GabbyAI shouldn't be designed as a website that happens to call an AI model.\n\nIt should be designed as a platform.\n\nThat distinction has influenced almost every architectural decision we've made.\n\nSeparating the product from the model\n\nOne principle has been particularly important:\n\nThe product should not be the model.\n\nModels change.\n\nHardware changes.\n\nInference engines improve.\n\nContext windows grow.\n\nNew capabilities appear.\n\nIf the entire application is tightly coupled to one model implementation, every major model change risks becoming an application migration.\n\nInstead, GabbyAI sits above our SWAI model layer.\n\nFrom the application's perspective, the important contract is the inference API — not the implementation behind it.\n\nConceptually:\n\nUser\n\n  │\n\n  ▼\n\nGabbyAI\n\n  │\n\n  ▼\n\nSwish API\n\n  │\n\n  ├── Authentication\n\n  ├── Conversations\n\n  ├── Usage & Limits\n\n  ├── Billing\n\n  ├── API Keys\n\n  └── Model Routing\n\n          │\n\n          ▼\n\n      SWAI Inference\n\n          │\n\n          ▼\n\n    Compute Infrastructure\n\nThat abstraction gives us room to improve the underlying AI infrastructure without forcing the rest of the platform to understand every change.\n\nIt's a fairly ordinary software-engineering principle.\n\nBut ordinary principles become surprisingly valuable when applied to AI systems.\n\nInfrastructure matters\n\nAnother decision was to retain meaningful control over the infrastructure running the service.\n\nGabbyAI runs on Swish-controlled infrastructure, with the surrounding platform built around services we operate.\n\nThat creates more work.\n\nWe have to think about deployment, monitoring, capacity, databases, inference performance, failures and upgrades ourselves.\n\nBut it also gives us something valuable: control.\n\nWe can see how the platform behaves from the API request through to inference.\n\nWe can decide how services communicate.\n\nWe can change components independently.\n\nAnd as the platform grows, we aren't forced to treat the most important part of the product as a black box.\n\nPostgreSQL at the centre\n\nWe're using PostgreSQL as the primary database platform.\n\nThis wasn't a particularly exotic decision — and that's exactly the point.\n\nAI projects have a habit of becoming unnecessarily complicated because every component is treated as an opportunity to introduce another specialised technology.\n\nSometimes that's justified.\n\nOften it isn't.\n\nFor the core application, we wanted something boring, powerful and extremely well understood.\n\nPostgreSQL gives us relational data for users, subscriptions, sessions, conversations and operational data while also leaving room for AI-specific functionality where we need it.\n\nOur approach has generally been:\n\nIntroduce complexity when the requirement demands it, not because the architecture diagram looks more impressive with another box.\n\nThat philosophy has saved us quite a few headaches already.\n\nThe API is a first-class product\n\nGabbyAI isn't intended to exist solely through gabbyai.online.\n\nWe're building the API as a first-class interface to the platform.\n\nThat means thinking about things such as API keys, authentication, usage accounting, model selection, streaming, limits and error behaviour as actual product features rather than implementation details required by the website.\n\nIt also forces better architecture.\n\nWhen your own frontend consumes the same properly defined services that other applications can use, shortcuts become much more obvious.\n\nA feature isn't really finished because a button works.\n\nIt's finished when the underlying service behaves correctly.\n\nStreaming changes the experience\n\nOne deceptively important part of an AI interface is streaming.\n\nWithout it, an application can technically work perfectly while still feeling painfully slow.\n\nA request might take ten seconds and return a complete answer.\n\nFunctionally: success.\n\nFrom the user's perspective: something might be broken.\n\nStreaming changes that perception because generation becomes visible as it happens.\n\nFor GabbyAI, our chat API therefore supports streamed completions.\n\nIt creates its own engineering problems — interrupted connections, partial responses, persistence, client state and error handling all become more interesting — but the resulting experience is worth the additional complexity.\n\nDesigning for failure\n\nOne principle we use elsewhere within Swish has carried over particularly well to GabbyAI:\n\nServers can fail. Services must not.\n\nIndividual machines will eventually disappear.\n\nProcesses will crash.\n\nDeployments will go wrong.\n\nNetworks will have bad days.\n\nAn AI service adds another failure domain because inference itself can become constrained by GPU memory, generation load or model availability.\n\nThe goal therefore isn't to create infrastructure that never fails.\n\nThat's unrealistic.\n\nThe goal is to design the service so individual failures don't automatically become product failures.\n\nWe're still building toward that architecture, and there is plenty left to do.\n\nBut designing for it now is considerably easier than attempting to bolt resilience onto the platform later.\n\nWhat we're building next\n\nGabbyAI is already usable, but we're treating the current platform as the beginning rather than the finished product.\n\nThe roadmap includes continued improvements to the SWAI model family, developer tooling, API capabilities, infrastructure resilience and eventually additional AI modalities.\n\nThere are also plenty of less glamorous engineering projects ahead.\n\nThose are often the interesting ones.\n\nHow do you meter AI usage accurately?\n\nHow should conversations be stored?\n\nHow do you deploy a new model without breaking existing clients?\n\nHow do you handle inference capacity?\n\nWhat happens when a generation stops halfway through?\n\nHow do you structure authentication when both humans and applications consume the platform?\n\nThose are the kinds of subjects we'll be writing about here.\n\nBuilding in public — selectively\n\nWe won't publish every internal implementation detail.\n\nSecurity-sensitive infrastructure and proprietary parts of the SWAI technology obviously need to remain internal.\n\nBut there is a large amount we can discuss.\n\nArchitecture decisions.\n\nMistakes.\n\nAPI design.\n\nInfrastructure lessons.\n\nPerformance improvements.\n\nFrontend development.\n\nDeployment problems.\n\nAnd the inevitable occasions where something that looked simple turned into several hours of debugging.\n\nThat's what this DEV account will primarily be about.\n\nGabbyAI is available globally at gabbyai.online, but this blog won't simply be a stream of product announcements.\n\nWe want to document what it actually takes to build the platform behind it.\n\nBecause building an AI assistant is easy to describe.\n\nBuilding one that you can operate, evolve and depend on is considerably more interesting.\n\nWelcome to GabbyAI.\n\nGabbyAI is a Swish World Group company, powered by SWAI.", "url": "https://wpnews.pro/news/why-were-building-gabbyai-another-ai-assistant-built-differently", "canonical_source": "https://dev.to/gabbyai/why-were-building-gabbyai-another-ai-assistant-built-differently-180d", "published_at": "2026-09-21 22:14:12+00:00", "updated_at": "2026-09-21 22:54:38.010092+00:00", "lang": "en", "topics": ["ai-products", "ai-infrastructure", "ai-tools", "developer-tools"], "entities": ["GabbyAI", "SWAI", "Swish API", "PostgreSQL"], "alternates": {"html": "https://wpnews.pro/news/why-were-building-gabbyai-another-ai-assistant-built-differently", "markdown": "https://wpnews.pro/news/why-were-building-gabbyai-another-ai-assistant-built-differently.md", "text": "https://wpnews.pro/news/why-were-building-gabbyai-another-ai-assistant-built-differently.txt", "jsonld": "https://wpnews.pro/news/why-were-building-gabbyai-another-ai-assistant-built-differently.jsonld"}}