Self-hosted AI data refinery · Early access
Your company’s data isn’t ready for AI.
We make it ready.
Goldlayer is a data refinery that runs inside your own infrastructure. It connects your fragmented documents, databases, wikis and messages, then refines them into governed, traceable, AI-ready knowledge — for RAG, agents, search and analytics, with any model you choose.
Sources
Fragmented across systems
01 · Bronze
Raw, captured with original context
02 · Silver
Parsed, deduplicated, enriched
Master contract
ownerv3 kept
Account history
entities
Policy pages
refreshed
Support answers
classified
Revenue tables
schema
03 · Gold
Governed, traceable, AI-ready
Contract knowledge
src:docs/contracts
Customer 360
src:crm/records
Company handbook
src:wiki/handbook
Support playbook
src:chat/support
permissions preserved
Your AI stack
Any model, any application
The problem
Enterprise AI fails upstream, before the model is even involved
Most RAG and agent projects don't fail at the model. They fail because the data underneath was never prepared to be retrieved, trusted or governed.
Scattered systems
Knowledge lives in drives, wikis, databases, tickets and chat — none of it built to be read together.
Duplicates and contradictions
Five versions of the same document, and no signal for which one the AI should trust.
Missing context
Ownership, dates, entities and business meaning are absent — so retrieval has nothing to rank on.
Permissions lost in indexing
Naive pipelines flatten access controls, and a chatbot ends up answering from documents users can't open.
Chunking destroys meaning
Generic splitting cuts tables, contracts and procedures mid-thought, and answers inherit the damage.
Unverifiable answers
When retrieval can't be traced back to a source record, wrong answers are impossible to debug.
Indexes drift stale
Sources keep changing after the first ingestion, and yesterday's index quietly stops reflecting reality.
The result: pilots that demo well and can’t be trusted in production. Fixing this is upstream data refinementwork — and it’s exactly what Goldlayer is being built for.
The refinery
From fragmented sources to a governed knowledge layer
Goldlayer is designed around a medallion-style pipeline: deterministic processing plus specialized AI agents, each stage adding structure, trust and traceability.
- 01
Bronze
Preserve the raw truth
- Connect documents, databases, wikis, messages and APIs
- Capture content with its original context and metadata
- No destructive transformation — the source of record stays intact
- 02
Silver
Structure and enrich
- Parse formats content-aware, so tables and contracts keep their meaning
- Clean, normalize, deduplicate and classify at scale
- Enrich with ownership, entities, relationships and business context
- 03
Gold
Govern and serve
- Retrieval-ready indexes and knowledge representations
- Permissions and lineage carried through every transformation
- Served to your models, agents and applications — no lock-in
Every record in the gold layer stays traceable to the bronze original it came from — answers can always cite their source.
The approach
What we're building into the refinery
Goldlayer is in active development with early design partners. These are the capability areas the product is being designed around — not a list of finished features.
Connect fragmented sources
Ingestion connectors for the systems where enterprise knowledge actually lives.
Parse and clean content-aware
Format-aware parsing, normalization and deduplication that respect document structure.
Extract context and relationships
Metadata, entities and cross-source relationships that make retrieval rankable.
Preserve lineage
Every derived record traces back to its source, so answers stay inspectable.
Respect permissions
Identities and access controls carried through the pipeline, not flattened away.
Build retrieval-ready indexes
Hybrid search, vector indexes and knowledge representations from one governed layer.
Keep knowledge fresh
Continuous quality and freshness checks as source systems keep changing.
Serve your AI stack
APIs and MCP-compatible access for the models, agents and tools you choose.
Self-hosted by design
Runs inside your environment, because your data shouldn't leave it
For many organizations — especially regulated and privacy-conscious ones — the blocker to enterprise AI isn't ambition, it's where the data would have to go. Goldlayer is being built self-hosted first.
Your infrastructure, your rules
Designed to install on premises or in your private cloud. Sensitive data is refined where it already lives instead of being shipped to an opaque external knowledge service.
Permissions survive the pipeline
Identities and access controls are treated as first-class data, carried from source systems through every stage — so retrieval can respect who is asking.
Inspectable by design
Transformations, quality checks and lineage are meant to be auditable. You should be able to see why a record looks the way it does and where it came from.
Bring your own models
Model-agnostic by architecture: local or hosted models for the AI steps, and your choice of embedding and generation models downstream.
No application lock-in
The gold layer serves your RAG systems, agents, search and analytics through open interfaces. Goldlayer is the refinery, not another walled garden.
Your infrastructure
Internal data sources
docs · db · wiki · chat
Goldlayer refinery
bronze → silver → gold
Governed knowledge layer
indexed · permission-aware
Your AI applications
rag · agents · search
What it unlocks
One governed layer, every AI use case on top
When the knowledge layer is trustworthy, everything built on it inherits that trust.
RAG you can defend
Answers grounded in deduplicated, current records — with citations that trace back to the original source.
Assistants that know your company
Internal copilots grounded in governed knowledge instead of a raw document dump.
Search across every silo
One retrieval layer over documents, databases, wikis and messages — ranked with real metadata.
Agents with guardrails
Autonomous workflows that only see the context they're permitted to see.
Reusable data products
AI-ready knowledge assets built once and consumed by every downstream team.
Faster experiments
Try new models and applications without rebuilding ingestion for each use case.
Design partners
Built with early organizations, not in a vacuum
Goldlayer is at the design-partner stage. We're working with a small group of organizations that feel this problem acutely, to make sure the refinery is shaped by real enterprise data — in exchange for real influence.
Shape the roadmap
Your data landscape and constraints directly influence what gets built first.
Work with the builders
Architecture discussions about your sources, permissions and retrieval needs.
Priority onboarding
First in line when private pilots open.
Early pilot access
Evaluate the refinery on your own infrastructure before general availability.
Early access
Tell us about your data, and let's talk
A few questions so we can understand your landscape before a conversation. We read every application and reply personally — this is a qualification form, not a mailing list.
FAQ
Questions worth asking
Is Goldlayer available today?
Not yet. Goldlayer is in active development and we're validating the design with early partners. This site describes what we're building and invites organizations that feel the problem to get involved early.
Is it really self-hosted?
Self-hosted deployment — on-premise or in your private cloud — is the primary design goal, because the organizations that need this most are the ones that can't ship internal data to an external service.
Does it replace our vector database or data warehouse?
No. Goldlayer sits upstream of them: it refines raw enterprise content into governed, traceable knowledge, then feeds the indexes, warehouses and retrieval systems you already use or plan to adopt.
Which data sources will it support?
We're prioritizing where enterprise knowledge actually lives: document stores and file shares, databases, wikis and intranets, messaging platforms, ticketing and CRM systems, and internal APIs. Design-partner needs will drive the connector order.
Can it work with different models and AI applications?
That's the point of the architecture. The gold layer is model- and application-agnostic — designed to serve RAG systems, agents, enterprise search and analytics through open interfaces, with the models you choose, including local ones.
What does becoming a design partner involve?
A short discovery conversation about your data landscape, then ongoing input: architecture discussions, feedback on early builds, and priority access to pilots. No payment or contractual commitment is required to start the conversation.