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The fastest way to unify data.

Connect any source. SetMeld generates the integration and keeps one governed knowledge graph in sync.

Request a demo See how it works
Siloed sources SetMeld pipeline Unified knowledge graph PostgreSQLMongoDBREDCapSAP ERPSalesforce + any structured source SetMeld Composer ExtractTransformResolve entitiesLoad Siloed sources SetMeld pipeline Unified knowledge graph PostgreSQLMongoDBREDCap SetMeld Composer ExtractTransformResolveLoad

SetMeld Composer generating the pipeline configuration

The team has delivered data systems for

University of OxfordMITScottish GovernmentMicrosoftNLnet FoundationInternet of Production Alliance
The problem

Every AI, analytics and compliance initiative depends on unified data.

Getting it today means months of bespoke pipelines, built by expensive system integrators or by forward-deployed engineers who walk out when the contract ends.

27%

of the 957 applications the average organization runs are connected to each other

MuleSoft, 2026 Connectivity Benchmark Report, survey of 1,050 IT leaders

60%

of AI projects will be abandoned through 2026 where the data behind them is not AI-ready

Gartner, February 2025

How it works

The four stages

Stage 01

Connect any structured source

Databases, APIs and internal systems. Credentials go in through a guided flow, and SetMeld Composer scans each source to prove extraction works before anything is designed.

1 · Connect Data Sources
SetMeld Composer connecting and scanning two Postgres datasources, with an AI assistant reporting scan status
Stage 02

Let AI design the integration

The AI pipeline determines the ontology, schema and entity resolution rules needed to unify your sources, then reports the data quality problems it found. Your team approves the design before it runs.

2 · Generate Schema
A generated unified schema visualized as an interactive graph of entities and relationships
Stage 03

Sync into one governed model

Approved configurations go to SetMeld Pipeline, which extracts, transforms, resolves entities across systems and loads the knowledge graph. Once, or continuously, with every operation logged.

3 · Sync Data
A live SetMeld Pipeline run showing SPARQL and SQL operations with per-step timings
Stage 04

Query everything from one layer

BI tools, applications, analysts and AI agents all read the same trusted layer. Every answer carries datasource attribution back to the systems that produced it.

4 · Query Data
A natural language question answered across two source systems, with datasource attribution and a chart
Why teams choose SetMeld

What you get

Delivered in weeks

The integration is generated, verified by our experts and approved by your team, so a unified model arrives in weeks rather than over the course of a program.

One subscription, unlimited integrations

Sources arriving through growth or acquisition become additional connections, at no additional project cost.

Runs where your data lives

On-prem, private cloud or SaaS. Your existing systems stay in place, with access control, audit trails and provenance on every transformation.

Compare

Where SetMeld sits against the alternatives

Each of these is the right answer to some problem. The question is what you give up: the labor, the semantics, or the ability to trace an answer back to a record.

ApproachUnified model across sourcesEntity resolution between systemsStays current as sources changeAdding the next source
Manual reconciliationRepeat the work by hand
Integration consultingA new project
ETL and iPaaS toolingA new pipeline to build and own
Search over documentsIndexed automatically
SetMeldA new connection

Approaches rather than named products, because capabilities differ considerably between vendors and between deployments of the same vendor.

Read the full comparison
Use cases

Where SetMeld is used

Clinical & life sciences

Trial, registry and operational systems in one research-ready model.

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Financial services

One client, one exposure, one number, with lineage attached.

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Manufacturing & supply chain

ERP, MES, quality and supplier data joined across plants.

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Public sector

Cross-agency data sharing with sovereignty preserved end to end.

Explore

Mergers & acquisitions

Combined visibility on day one, before systems consolidate.

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AI agents & analytics

A governed substrate with provenance and permissions intact.

Explore
FAQ

Frequently asked questions

Do we have to migrate our data?
No. SetMeld sits on top of your existing IT infrastructure. Source systems remain in place and untouched, and SetMeld reads from them, unifies the result, and loads it into a knowledge graph. There is no cutover and no vendor lock-in.
Where does the data actually live?
Wherever you require. In the self-hosted model every component, including the LLM, runs inside your network, and no data leaves it. In the SetMeld Cloud model, both components run in our cloud, and the authoritative unified graph is still loaded back into your network. See deployment options.
Which LLM does it use?
Yours, if you want. Self-hosted deployments reason using an LLM of your choosing, hosted in the same network, so sensitive schema information and data samples are never transmitted to a third party. SetMeld Cloud uses an LLM operated by SetMeld.
How is this different from RAG or an AI search tool?
Search tools retrieve passages and let a model summarize them, which works for finding a document and fails for producing a number. SetMeld unifies the underlying structured data into a governed model and answers questions by querying it, so results are computed, attributable and reproducible.
What happens when a source system changes?
Rescan it. SetMeld Composer re-derives the affected part of the mapping and shows you what changed before anything is pushed to the SetMeld Pipeline. This is the failure mode that makes hand-built pipelines brittle, and it is the one SetMeld is designed to absorb.
Can we keep our existing data lake?
Yes. In either deployment model the knowledge graph can act as the upstream source of record for a lake. Analytics workflows you have already built keep working, and the data arriving in them has been cleaned, unified and enriched with provenance first.
How long does the first integration take?
Days rather than months for a typical pair of systems, including expert verification and your team’s review. The honest answer depends on how many sources, how bad the data is, and how quickly credentials appear, which is what a scoping call is for.

Deploy one unified knowledge graph of your data in days, not quarters.

Bring us two systems that should agree with each other and do not. We will show you what a unified model of them looks like.

Request a demo Read the technical overview