Most generative AI pilots impress in a demo and die before production. Billow builds generative AI solutions that make it copilots, assistants and LLM apps grounded in your data through RAG, and governed for accuracy, privacy and safety from day one.
Generative AI solutions are applications built on large language models that create useful output — answers, drafts, summaries, code — grounded in your own data and embedded in real workflows. The point isn’t a clever chatbot; it’s AI that does real work, accurately and safely.
The gap between a generative AI demo and a production system is enormous, and it’s where most efforts stall. A demo can look magical on curated inputs; production has to be accurate on messy real data, keep your information private, resist prompt injection, and be trusted by the people using it. Getting there takes grounding, governance and engineering — not just a good prompt.
Billow builds generative AI that reaches production. We find the use cases worth doing, ground the model in your data with retrieval-augmented generation (RAG) so answers are accurate and current, build copilots and LLM applications into the tools your people already use, and wrap it all in the privacy, safety and monitoring an enterprise needs. Less hype, more shipped.
Our generative AI services cover the whole path — from finding the right use case to shipping a grounded, governed solution.
Find the use cases where generative AI delivers real, measurable value — not just novelty.
Retrieval-augmented generation that anchors the model in your data — accurate, current, and far less prone to making things up.
Assistants embedded in your workflows and apps that help people work faster — grounded and governed.
Full generative AI applications — not prototypes — engineered, integrated and production-ready.
Summarize, extract, classify and generate across your documents and content at scale.
Privacy, prompt-injection defense, evaluation and monitoring — so the AI stays accurate, safe and trusted.
A demo proves it’s possible; production proves it’s useful. The hard parts — accuracy, privacy, safety, trust — are exactly what we engineer in from the start.
RAG anchors answers in your verified data, so output is accurate and current — not confident fiction.
Your data stays yours — no training public models on it, with enterprise privacy controls throughout.
Copilots live where people already work, so the value is used — not stranded in a separate tool.
Prompt-injection defense, evaluation and monitoring keep it trustworthy in the real world.
We build on enterprise-grade models and a solid RAG architecture — chosen for capability, cost and data control.
Enterprise LLMs
Grounding & retrieval
Assistants & agents
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We identify the highest-value use cases and define what “good” and “safe” look like.
We build a working prototype fast, tested on real data — to prove value before scaling.
We add RAG, privacy and guardrails so the solution is accurate, safe and enterprise-ready.
We ship it into your workflows and monitor it — keeping it accurate and trusted over time.
Generative AI connects to the rest of your data and AI stack. These work most closely with it.
The full practice this sits within generative AI, machine learning, data engineering and analytics.
Beyond generation, predictive ML models handle the forecasting and classification GenAI doesn’t.
When generative AI needs to act, not just answer, agents connected via MCP take it further.
Tell us the workflow you’d love to put AI on and we’ll show you a grounded, governed path to production, not another pilot that stalls.
