Your whole data estate, ready for AI — without moving a byte.
Connect any database, warehouse, object store, SaaS app, stream, or file. Data Pipe leaves the data where it lives, indexes it into your graph with relevance weights, and lets every AI session pull exactly the slice it needs — backed by a self-growing hot cache.
No credit card required · Your data never leaves its source · Sovereign by design
Connect a source and the graph indexes it — the data never moves.
Getting your data ready for AI shouldn't mean copying all of it
The standard playbook is to drag every system into one proprietary store, pay to keep it there, pay a vendor to keep feeding it, and bolt AI on at the end. Data Pipe takes a different path.
Paying to store all of it, forever
Warehouses bill you to copy your data in and to keep it there — a storage-and-compute meter that compounds with every source and every terabyte.
A second vendor just to move it
ETL platforms charge per row and per connector to keep shuttling your data into the warehouse — costs that sprawl across dozens of low-volume sources.
Your data living under someone else's terms
Once it's copied into a vendor's cloud, your residency, your gravity, and your control are negotiable — and the AI layer is still a bolt-on.
Connect anything, leave it in place, and let the graph do the work
Pick a connector, grant least-privilege access, declare a tier. Done.
Connect virtually any source — and skip the migration entirely
Databases, warehouses, object stores, SaaS APIs, message streams, and files all connect from one extensible catalog. There's no warehouse to fill and no pipelines to build: point Data Pipe at a source and it's discoverable and retrievable in minutes, with the data staying exactly where it lives.
- One catalog for databases, warehouses, object stores, SaaS, streams, and files
- No data movement — your source stays the single system of record
- Credentials held by a secrets broker, injected only at fetch time
- Declare an access tier per source so AI can never overreach
The graph ranks candidates and returns the right slice — with provenance.
Your AI gets exactly the right data — ranked, joined, and cited
Data Pipe writes pointers and relevance weights into your graph, so retrieval is a ranked pull, not a dump. When a session asks a question, the graph scores every candidate on meaning, recency, freshness, and context, resolves the top ones, joins across sources on demand, and answers with a clear trail back to each source.
- Weighted graph indexing decides what's worth pulling — and from where
- Cross-source joins happen on demand, never by duplicating data
- Every answer carries provenance back to the originating system
- Agent-native: your AI reaches it all through one governed surface
You set the cache limit; the engine never crosses 60% of the drive.
A self-growing hot cache makes repeat retrieval feel instant
The data you use often is materialized onto fast storage for sub-millisecond repeat reads, while a context-aware job quietly pre-warms what your organization is about to need. You set the limit and the engine enforces a hard 60%-of-the-drive ceiling, so the cache accelerates without ever crowding out everything else running on your bRRAIn.
- Fast-storage cache for instant repeat retrieval
- You own the space and set the limit — capped at 60% of the drive
- A daily, context-aware job grows and consolidates it within budget
- Evictable by design — the source is always the system of record
A per-source report shows exactly what is and isn't bRRAIn's to fix.
See exactly where every millisecond goes — and what to do about it
When you connect a source, Data Pipe profiles the link and the host on the other side and hands you a plain-language report: how fast it is, where the time goes, and concrete recommendations. Then it keeps measuring — with high-end dashboards for latency attribution, cache hit-ratio, freshness, field-level lineage, and cost.
- A Source Speed Report at setup, with a grade and clear next steps
- Latency attributed across source, network, connector, and engine
- Cache hit-ratio, freshness, and field-level lineage you can trace
- Cost per source and the realized savings vs. the legacy stack
From a connection to an answer in three steps
Connect a source
Pick a connector, grant least-privilege access, and declare an access tier. Data Pipe discovers what's there — without copying a single row.
The graph indexes it
Pointers and relevance weights land in your graph, so your master-context now knows what exists, where it lives, and how to fetch it.
Ask in any session
Your AI asks, the graph ranks and resolves, and the answer comes back from the hot cache or a live pull — with provenance, every time.
Snowflake-class reach, without the Snowflake-class bill
Skip the data-migration project
Make your whole estate available to AI without an ETL program. Point Data Pipe at a source and it's indexed and retrievable in minutes — no pipelines to build, no warehouse to fill, no data to move.
Cut the centralize-everything bill
Replace the storage-plus-compute-plus-connector spend of a warehouse-and-ETL stack with a bounded hot cache and on-demand federation — typically a fraction of the comparable annual cost.
Respect data residency and gravity
Keep regulated and sovereign data exactly where it must live. Data Pipe queries it in place under its owner's control instead of dragging it across borders into a vendor's cloud.
Ask across every system at once
Let a single AI session reason over your CRM, your warehouse, your object store, and your operational databases together — joined on demand, ranked by relevance, answered with provenance.
Make repeat retrieval instant
A context-aware daily job pre-warms the cache with what your organization is about to ask for, so the data you actually use returns in milliseconds and the slow source link is paid once, off-peak.
Govern what AI can see
Declare an access tier per source so no session can pull data above its clearance — field-level masking and sealed handling included — with every access audited.
The same reach — for under a quarter of the cost
Warehouse-and-ETL stacks charge you to copy your data in, to store it, to keep feeding it, and to query it — a bill that compounds with every source and every terabyte. Data Pipe indexes your data where it already lives and accelerates only what you actually use — for under a quarter of the comparable annual spend, with nothing leaving your control.
| Compared on | Data Pipe on bRRAIn | Warehouse + ETL stack |
|---|---|---|
| Core model | Index where it lives, federate on demand, cache the hot subset | Centralize everything into proprietary storage, then query |
| Where your data lives | At its origin — under your residency and control | Copied into the vendor's storage, under the vendor's terms |
| Storage cost | Pay only for a bounded hot cache | Pay to store all of your data, always |
| Ingestion | Connectors included — pull on demand, no per-row meter | A separate ETL vendor, billed per row / per connector |
| AI access | Native — the graph is the retrieval surface | A bolt-on layer added at the end |
| 5-year cost | Under 25% of the comparable stack | Baseline (100%) — and rising with every source + terabyte |
"We were about to sign for a warehouse and a separate ETL vendor to get our data into AI. Instead we connected eight systems to Data Pipe in an afternoon — nothing moved, nothing was copied — and our assistant can now answer across all of them. Our data-stack budget dropped by roughly 80%."
Included with your bRRAIn — you pay only for the cache you use
No warehouse storage bill, no per-row ETL meter, no separate connector vendor. The whole index-federate-cache lifecycle comes with your bRRAIn — for under a quarter of the comparable stack.
Connect & Index
- Connect virtually any source
- Weighted graph indexing & retrieval
- On-demand federation — no data moved
- Per-source access tiers & full audit
Cache & Accelerate
- Everything in Connect & Index
- Self-growing hot cache (capped at 60% of the drive)
- Speed Optimizer + advanced observability
- Context-aware daily pre-warming
- Under 25% of a comparable warehouse + ETL stack
Sovereign Enterprise
- Everything in Cache & Accelerate
- Field-level masking & sealed handling
- Agent-native access for every app
- Pipelines published to your registry
A comparable cloud warehouse plus a separate ETL connector platform typically costs several times more over the same period.
The honest answers
Does Data Pipe copy my data into bRRAIn?
No. Data Pipe writes only pointers, descriptors, and relevance weights into your graph — never your bulk data. Your rows stay at their source, which remains the single system of record. When an AI session needs data, it's pulled on demand or served from a bounded hot cache, then governed and audited.
What about data residency and sovereignty?
Because the data never leaves its origin, your residency and gravity rules are respected by default. Regulated and sovereign data is queried where it must live, under its owner's control. Credentials are held by a secrets broker and injected only at fetch time — never written into the graph or your vault.
What can I connect?
Databases, warehouses, object stores, SaaS APIs, message streams, and files — from one extensible connector catalog, with generic bridges so almost anything can be reached. Each connector discovers what's there, describes it, and pulls only the slices you ask for, pushing filters down to the source so it never over-fetches.
How is it cheaper than a warehouse plus an ETL vendor?
You stop paying to store all of your data and to keep shuttling it in. You pay only for a bounded hot cache and for the data you actually pull, while everything else stays in place and is federated on demand — typically under a quarter of the comparable annual spend.
Can my AI see data it shouldn't?
No. Every source declares an access tier, and the graph enforces it as a hard gate — no session can pull data above its clearance. Restricted data supports field-level masking, and the most sensitive tier requires explicit per-request authorization and is never cached in the clear. Every access is written to your audit log.
Create your bRRAIn — add Data Pipe in minutes
Spin up your sovereign AI memory, then install Data Pipe from the marketplace the moment your pod is online.
No credit card required · Your data stays in your bRRAIn · Set up in minutes
Stop copying your data. Start indexing it.
Connect your first source, leave every byte where it lives, and let your AI reason across all of it — inside your bRRAIn.
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