Model Selection & Training
In LLM land, every week is Christmas — new models, large and small, arrive constantly. We cut through the noise, select the right one for your use case, and fine-tune it for your purpose as needed.
AI that delivers value, not issues.
We build production-grade AI systems that survive contact with the real world. From model selection to RAG and agentic orchestration, bitcrowd's extra-friendly humans get you there safely — and keep you running.
We were doing ML before it was a buzzword
We've been building with Elixir since 2016 and working with machine learning since 2020 — long before everyone called it "AI." Our team brings over 15 years of development and consulting experience across hundreds of client projects.
We prioritise correctness, observability, and simplicity as much as model quality. We design systems to fail gracefully, because predictable failure behaviour beats applications that avoid crashing at all costs, only to fail catastrophically when they finally do.
What we do
RAG & Agentic Systems
Retrieval-Augmented Generation is one of the most powerful tools in the AI toolbelt. We design and build RAG and agentic RAG systems that actually work in production — and we help you get started fast.
RAG Performance Evaluation
Recall and precision only mean something if you know all the relevant matches. That's easy on a test dataset. We help you measure whether your production results are genuinely relevant — not just plausible.
AI Consulting
Need to add AI to your product but unsure where to begin? We bring our expertise in model selection, RAG, and agentic systems to your team, so you harness AI as a sustainable competitive advantage rather than a one-off prototype that never leaves the lab.
Python for prototyping. Elixir for production.
Most AI systems start as a Python prototype on LangChain or a similar framework — and that's great for getting going. But Python's architecture complicates scalable, fault-tolerant deployment: framework overhead adds latency on top of already-slow LLM calls, and managing the state of long-running agentic workflows stays genuinely hard.
That's why we often pair the two. Python remains the workhorse for model training, while Elixir acts as the robust, real-time AI backend — orchestrating workflows, queues, and user interfaces on the battle-tested BEAM VM.
-
Massive concurrency
Lightweight, isolated processes handle hundreds of thousands of concurrent tasks. No shared-memory locks, no GIL.
-
Fault tolerance
When a third-party API times out or an LLM endpoint goes down, only one tiny process crashes. A supervisor logs it and restarts cleanly, without touching any other operation.
-
Real-time feedback
With Phoenix LiveView, users see exactly what the agent is doing — "Fetching data… Analysing tone… Drafting summary…" — and can pause, guide, or cancel in real time. No more staring at a spinner for 30 seconds.
-
Great observability
Built-in telemetry instruments every step of an agent's journey: timing calls, counting tokens, tracking decision paths. You can even attach to a running production system and inspect its state live.
We build in the open
Wherever possible, knowledge should be shared so no effort is wasted. We contribute open-source tooling, write deeply technical posts, and give talks — so you can see exactly how we work before we write a single line of code for you.
RAG
Our open-source, Elixir-native library for orchestrating Retrieval-Augmented Generation systems.
Surveyor
A tool that extracts verifiable specifications from legacy code, letting you reclaim ownership of software you'd lost confidence in.
Grammar Constrained Decoding in Bumblebee
Forcing open-source LLMs to comply with a schema using logits processing, so you get reliable JSON without endless resubmission — including things ChatGPT can't reliably do.
What working with us actually means
We know both worlds — the Elixir ecosystem and the machine-learning/AI landscape — which gives us the skillset to build modern AI-enhanced applications that scale. Our goal is simple: to help you harness AI as a sustainable competitive advantage.
Let's build something reliable.