Practical AI in production: RAG, agents, workflow automation.
Not a lab, not a demo shop. We build the AI features that ship inside real products — and we'll tell you when you don't need one.
§01
What we build.
RAG pipelines over your documents and data. LLM agents that execute multi-step work instead of chatting about it. Chatbots that resolve tickets rather than deflecting them. Workflow automation where the model is one component in a system, not the system.
Everything ships inside a product with authentication, billing, monitoring, and a human escape hatch — because an AI feature that can't be operated is a demo.
§02
Where the bar comes from.
Our engineering co-founder led the AI team at UBS building tooling for a global trading desk — systems that wrote, summarised, and validated text where the wrong output cost money. That environment teaches you evaluation, latency budgets, and response-shape correctness, and those habits came with him.
The practical consequence: we write the eval before we write the prompt. If we can't measure whether the output is right, we don't ship the feature.
§03
A worked example.
For LaunchProd — a Carnegie Mellon-founded creator-economy AI startup — we designed and shipped a section-level retrieval pipeline on pgvector with a refuse-to-answer threshold. Then we simplified it: cutting the multi-tier chunking and reranking dropped latency roughly 35 percent without hurting output quality, because the original complexity was fighting itself. The full architecture is written up in our notes, including the three things we'd do differently.
We publish the reasoning because that's the fastest way to check whether ours matches how you think.
§04
What we decline.
AI for the pitch deck. Agents where a cron job and a queue would do. Fine-tuning when prompting solves it. Chat interfaces bolted onto products whose users wanted a button. If your product doesn't need AI, we'll say so and build the boring version that works — that conversation has saved clients real money.
The test is always the same: does the model earn its complexity? If not, it doesn't ship.
Questions founders ask
- What does an AI development agency actually do?
- Builds AI features that live inside production software: RAG pipelines, LLM agents, chatbots, and workflow automation — with the evaluation, monitoring, and fallbacks that keep them working after launch.
- Do you build RAG pipelines?
- Yes — chunking, embeddings, retrieval tuning, and the evaluation harness around them. The architecture we built for LaunchProd is documented publicly in our notes.
- Does my product actually need AI?
- Maybe not, and we'll tell you. If a cron job, a queue, or a search index solves the problem, that's what we'll recommend — the model has to earn its complexity to ship.
- Which models and stack do you use?
- Whatever the eval says. Typically LLM APIs with pgvector for retrieval, Python or TypeScript pipelines, and an evaluation harness from day one. Model choice is a measured decision, not a brand preference.
- What does an AI development project cost?
- A docs-grounded chatbot runs $8,000–$15,000, RAG over live private data $15,000–$40,000, and a full workflow agent $40,000–$80,000. Fixed scope, milestone billing. Email hq@singlebit.xyz and we reply with an estimate within 24 hours.
- Where can I hire RAG developers?
- Hire whoever can show a production retrieval system and its evaluation harness, not a demo. Ours is public — the LaunchProd architecture write-up — and we take these builds directly: hq@singlebit.xyz.
Keep reading
Tell us what you're building.
Two paragraphs is enough. We reply with a pricing estimate within 24 hours — before any call, before any deck.