AI engineering
AI Integrations
Primary Automative Tech capability: we embed LLM features, assistants, RAG search, and automation into the web or mobile products your users already use — designed for latency, cost, evaluation, and safe fallbacks, not one-off demos.
Outcomes
- Assistants and copilots grounded in your product data
- RAG search and Q&A with citation / source controls
- Workflow automation that cuts operator hours
- Cost and latency budgets that hold under real traffic
- Eval suites and regression gates before every prompt change
- Observable pipelines with fallbacks when models fail
How we approach it
Start from the user workflow
We pick AI where it improves a core job — support, search, drafting, triage — not where a model is merely impressive in a slide deck.
Reliability over demos
Models are treated like any other dependency: timeouts, caching, rate limits, degradation paths, and clear UX when the model is down or uncertain.
Grounding before fine-tuning
Most products win with strong retrieval, tools, and prompting first. We fine-tune only when data quality and task specificity clearly justify it.
Measure quality continuously
Golden datasets and automated evals run on prompt, retrieval, and model changes so quality does not silently regress after launch.
Related services
High-performance web apps with React, Next.js, TypeScript, and modern full-stack architectures. Primary Automative Tech service — often with AI integrations from day one.
Native-quality iOS and Android apps using React Native and Flutter. Primary Automative Tech service — one codebase, two platforms, with AI integrations when the product needs them.
Related capability when needed: cross-platform desktop clients with Electron or Tauri that share logic with your web product — not Automative Tech’s primary focus.
Secondary Automative Tech service: smart contracts, wallet flows, and on-chain features when your product requires blockchain capabilities.
Let's build something
remarkable
Whether you need a web or mobile app with AI integrations, blockchain work, or a conversation about our AI products — tell us what you're building and we'll respond fast.
AI questions
Straight answers about how we work, what to expect, and what happens next.
Production features inside your web or mobile product — LLM assistants, RAG over your docs or data, classification, summarization, and automation — with auth, logging, cost controls, and a path your team can operate.
Both. Client work usually embeds AI into your existing product. Separately we ship our own products (Stampvio, MarqueeDesk). For most clients, AI belongs in the app users already open.
When data quality and task specificity justify it. Many products win with strong retrieval, tool use, and prompting first — we recommend fine-tuning only after that baseline is solid.
We set budgets for tokens and response time early, cache where it is safe, stream where UX needs it, and keep sensitive data out of prompts or behind approved processors. Architecture choices follow your compliance constraints.
When a deterministic rule, search, or form flow is clearer, cheaper, and easier to test. We will say so — AI should earn its place in the roadmap, not decorate it.