Data, knowledge and AI infrastructure

You already own the information.
We make it work.

It sits in PDFs nobody opens, in systems that do not talk to each other, on web pages that change weekly, and in the heads of people about to retire. Jantia turns it into working assets: clean structured data, a connected knowledge base, and assistants that answer with a citation to where the answer came from. Connected to the systems you already run, and kept running.

Nothing invented · Everything traceable · Every step checked

JantiaOnelive
you

What is installed at the Borrowdale site, and is any of it still under warranty?

searchingCloseout PDFsCRM assetsWarranty register
Daikin VRV IV · roof plant · 3 unitswarranty to Apr 2028
Grundfos CR 32 · pump room · 2 unitsexpired Nov 2025
Schneider MCC panel · basementnot stated
5 records · 2 sources · every row cites document and page
01

Nothing is invented.

Every fact the system produces traces to a source: a document and page, an API record, a web page and timestamp. When the sources are silent, the answer is “not stated.”

02

Everything is traceable.

You see counts before and after every stage, the dry run before every write, and the audit log of any assistant conversation: which sources it read, which tools it called, what it answered.

03

Every step is checked before the next begins.

We count first, sample before we scale, agree the structure in writing, dry-run before any write, and verify from the target system. Nothing is a one-off.

The situation we walk into

The model is rarely the problem. The plumbing is.

Across industries the same pattern repeats. Most of what an organisation knows is unstructured. People pay for that every week. Records decay quietly. AI projects fail for data reasons, not model reasons. Jantia exists to do the unglamorous part well: get the data out, get it right, get it connected, and keep it that way.

up to 0%of enterprise information lives in documents, images, email and free text, not database tables
up to 0%of a knowledge worker’s time is spent searching for information that already exists
0%of enterprise generative AI pilots show no measurable return, citing data quality and no path to daily workflow
under 0%of large enterprises piloting knowledge graphs get past the pilot

Figures from published industry research. Sources available on request.

How we work

The method is the product.

Every engagement follows the same shape, because the shape is what prevents the failures the research describes.

01

Count first

We inventory what you have and confirm the numbers with you. “500 documents” is often 380 unique ones and 120 copies.

02

Sample before scale

We process a representative sample and show you extraction quality, the records produced, and the gaps.

03

Define the structure with you

The ontology and field lists are agreed in writing before extraction starts. Nothing is guessed to make a table look complete.

04

Dry run before any write

No record reaches your systems until you have seen exactly what will be written, to which parents, and which rows could not be matched.

05

Verify from the target

After a write, we query the target system and report its counts, not ours.

06

Report the failures

What could not be processed, what was flagged, what was skipped. In the summary, not in an appendix.

07

Leave it runnable

Everything is a repeatable, documented pipeline. Nothing is a one-off.

Our promise

Six things you can hold us to.

Nothing invented

Every fact traces to a source. When the sources are silent, the answer is “not stated.”

Nothing leaked

Rules about what must never be shown are enforced in code, tested with adversarial questions, and logged when triggered.

Nothing duplicated

Every record we write carries a stable key. Run it twice, get the same records, updated.

Nothing hidden

Counts before and after every stage. The dry run before every write. The audit log of every assistant conversation.

Nothing locked in

Your data, ontology, graph and indexes live in accounts you control and export in open formats. If we disappear, your systems keep running.

No promises we cannot keep

If a document type carries no usable data, or a source is off limits, we say so and propose the alternative.

Engagement models

Start small. Fixed price. Useful on its own.

Assessment

1 to 2 weeks · Fixed price

We inventory your sources, sample them, and return a written report: what is there, what can be extracted with what confidence, which questions it could answer, what it would take. Useful on its own even if nothing follows.

Pilot

3 to 6 weeks · Fixed price

A bounded slice: a few hundred documents, one or two API sources, one target system. You get the extraction quality report, a working assistant over the sample, a dry run against a sandbox, and a proposal with real numbers.

Build

Staged · Quoted after the pilot

The full scope, delivered in stages, each accepted before the next starts. Priced on volume and complexity.

Operate

Monthly · Fixed allowance of refinements

New sources processed as they arrive, assistants kept current, connectors maintained through upstream changes, monitoring, and a monthly summary of what changed.

Advisory

As needed · Day rate

For teams building this themselves: ontology design, pipeline review, evaluation design, and an honest opinion on what will and will not work.

Start with an assessment.

One to two weeks. Fixed price. A written report of what you have, what can be extracted with what confidence, and what it would take. Useful on its own even if nothing follows.