- 5 days ago
- 4 min read
What’s New at Brighthive?
Brighthive is a multi-agent harness of autonomous data agents, purpose-built for the upstream grunt work of the data lifecycle
Introducing Unstructured Data Pipelines
Stop Losing Knowledge to a PDF Nobody Reads
Someone asks what a field actually means, and the real answer lives in a PDF from three years ago, an image you can’t read, or a Slack thread you'd have to scroll back six months to find. None of it is in the warehouse, none of it is in the catalog, and none of it is anything your agents can actually use. So the same question gets answered from memory, again, by whoever happens to remember. Sound familiar? It sure does to us and our customers.
Brighthive's new Unstructured Data Pipeline means that knowledge doesn't get lost in a document.
This is built for data engineers who are tired of being the lookup table for institutional knowledge that was never actually captured anywhere structured. If tribal knowledge about your data lives in documents, threads, and folders instead of your catalog, this is for you specifically.
Two benefits to using Brighthive’s unstructured data pipeline feature:
The same governed lifecycle, whether it's a table or a PDF
Documents, PDFs, free text, message threads, images, videos, and links all enter the same governed pipeline structured sources already go through, instead of living in a risky shadow workflow nobody's tracking. Point it at a folder, a Drive, or just upload the files directly, and each one gets embedded for retrieval and folded into the same catalog everything else lives in. A codebook that only ever existed as a PDF becomes something your agents can actually reason from, not just something a person has to go dig up and re-read.
It doesn't just get stored, it builds the context underneath everything else
Every unstructured file you bring in feeds the same enterprise knowledge graph your structured data does. A glossary term that only ever existed in someone's onboarding doc becomes a real, queryable definition. A PII handling policy written up in a Word doc years ago becomes something the governance agent can actually enforce, not just something that exists on paper. The context an agent needs to answer a question correctly often lives outside the warehouse entirely, and this is what actually gets it in.
Why this matters now more than ever
Most of what makes an organization's data actually make sense was never in the warehouse in the first place. It's in the document that explains why a field exists, the thread where someone explained an exception to the rule, the deck that defined a metric before anyone wrote it into dbt. None of that shows up when an agent queries a table directly, so it either gets left out entirely or someone has to keep re-explaining it by hand.
Getting that knowledge into the same governed lifecycle as everything else closes that gap for good, instead of patching it one conversation at a time. We call that "faster time to insight," and that speed is what gives your business users and data stewards a competitive advantage. In the era of AI, AI requires clean data, and businesses need to compete differently.
Real data engineering workflows with the Unstructured Data Pipeline
Bring in a document or file directly: upload a PDF, a doc, an image, or a video, and it enters the same governed lifecycle as a connected database table.
Get it embedded for retrieval automatically: every file that comes in gets embedded, so an agent can actually search and reason over it, not just store it.
Feed the knowledge graph from a document: a definition or a relationship that only ever existed in a file becomes a real, queryable part of the knowledge graph.
Turn a written policy into an enforced one: a PII or handling policy that existed only as text in a document becomes something the governance agent actively enforces.
Catalog a file the same way you'd catalog a table: it gets PII tagged, described, and quality checked the same way a structured asset would, not treated as a second-class citizen.
Try it now for yourself. Bring your first document in
Stop loosing information in unstructured data and make your first document queryable with Brighthive.
What is Brighthive?
Brighthive isn't a data warehouse, a transformation tool, or a BI layer. It's a multi-agent harness of seven autonomous data agents, purpose built to do the upstream grunt work of the data management lifecycle. By grunt work, we mean the type of work that makes data trustworthy for downstream reporting and clean enough before it ever flows into the warehouse. The seven data agents working in unison take on : cleaning, validating, contracting, and monitoring, so everything downstream data product works from clean, governed data. Think of it as a full data engineering and governance team, continuously working together in unison, autonomously, to ingest, quality check, clean, govern, engineer, and transform the data into clean data pipelines. This is the future of data work that delivers clean data to power every data task. And in the era of adopting AI, clean data is a non-negotiable requirement to ensure successful AI adoption.
That’s our mission: Help every company autonomously clean its data and make it AI ready.
Imagine that agentic team of seven data engineering agents working alongside your human data engineering team for you, around the clock, proactively observing and monitoring your data health at the source and ensuring everyone on the team you have is working from clean, functioning data pipelines.
This is not a dream. This is where the world is moving towards. Agentic data workflows. Brighthive's value add to how you should start every day.
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