GBIF
Fixed upstreamthe Global Biodiversity Information Facility
Both endpoints serving the ZooBank checklist returned 404 and five consecutive crawls had failed since 21 July, with nothing on the dataset page to say so.
Enterprise data interoperability
Your systems do not speakthe same language.
Every merger, regulator and partner wants your information in a different format. Material detail is lost in the conversion, and almost nobody measures how much. We do the conversion, and we tell you exactly what did not carry across.
68.8% to 78.0%
More right answers
When an AI answered questions about company filings, checking each answer against the document it cited took it from 68.8 per cent right to 78.0 per cent.
202
Facts lost in a handover
Moving one defence data standard into a major software vendor format silently dropped 202 links between terms. Nobody involved was told.
7 of 22
Fields with nowhere to go
Seven of that vendor 22 data types have no equivalent anywhere in the open standards, including the one that marks information as classified.
773 of 981,355
Records carrying the required ID
Weeks before a US regulator made one company identifier mandatory across nine agencies, only 773 of its own 981,355 company records had one.
Limited availability
A one month evaluation at no cost, for the next ten enterprise teams.
Assess it against your own data before any commitment is made.
Trusted by
Open source
Used by engineers at
Stargazers, contributors and forkers of our open source repositories.
The package
A single engagement, not a four vendor integration programme.
Most organisations buy four separate capabilities and integrate them themselves. We deliver the layer underneath all four as one engagement, and your team can evaluate it before contract.
01
We establish what your data means
Today that knowledge sits in spreadsheets and with a handful of experts. We formalise it, so the organisation keeps it through personnel changes and restructuring.
02
We map it to the required format
Regulators, partners and acquirers each want the same information a different way. We build the mapping once, removing the rework every new counterparty triggers.
03
We quantify what is lost
Conversion is rarely lossless and suppliers rarely say so. We measure it and report it, so the exposure is known before commitment, not after a regulatory finding.
04
We provide the audit trail
Every step is recorded with its basis. When regulators, the board or a buyer in due diligence ask, it is already documented rather than reconstructed under pressure.
Runs entirely in your own environment. Data never leaves the organisation, and the software is open source, so your security team can review it before production.
Where it is already working
Sectors where misalignment carries material cost.
Regulated registers and compliance
Registers checked against what they claim to hold, before the deadline turns a gap into a finding.
Defence and government interoperability
Standards crossed and measured, so a programme knows what it loses before it commits the budget.
Recycling and producer compliance
Obligations evidenced rather than estimated, and reported the same way twice.
Food and agriculture data
Loss and waste made comparable across organisations, so a sector target means the same thing to everyone.
Case studies
Delivered engagements, and the outcome in each.
Testing Land Valuation Methods: Welsh Government
Produced a comprehensive comparative analysis across all five methodologies, published March 2026. Findings directly inform Welsh Government local government finance policy.
Read the full case studyAI Ontology Extension Generator: National Digital Twin Programme
Delivered a production-ready Streamlit web application with a four-step wizard workflow, built-in validation and visualisation tools, and iterative refinement capabilities. Published on GitHub under National-Digital-Twin organisation.
Read the full case studyClient confidential
Verified graph RAG for a multinational crop science company
Scope was retrieval across a large technical document estate with access control and encryption. We built the verification layer: every document becomes its own graph, and contradictions surface with citations rather than being averaged away. It found a classification asserted one way in four documents and the opposite in two others.
Client anonymised at their request.Explore
Proof you can check yourself.
We test the public data standards your industry relies on and publish the faults we find. Every row is a real defect with a link to the thread where the maintainer replied. Competitors show testimonials. We show receipts.
Semantica
Merged upstreama knowledge graph library with an RDF export path
Audited and then repaired the whole export path: seventeen issues filed and ten pull requests merged, covering deterministic identifier minting, a declared vocabulary, JSON-LD payloads that a plain parser can actually read, typed literals, timezone-qualified timestamps, and SHACL shapes that had never once matched the data they were written to validate..
See every standard we have tested
22 merged pull requests and 65 issues filed across 44 organisations.
How this differs
Three procurement routes. One of them reaches production.
A graph database
You get storage and a query language. The modelling, the validation and the question of whether the model is right stay with you, which is where the cost and the risk live.
A consultancy
You get a model and a slide deck. When the people leave, the reasoning behind the model leaves with them, and nothing in the build stops it drifting afterwards.
This
You get the model as infrastructure, the validation running in your pipeline, and the evidence trail. When something breaks the ontology, your build fails rather than your users finding out.
Talk to us
Tell us what your AI has to get right.
Outline the requirement in a few lines. We will tell you if this is the right fit, including when it is not.
Or email fabio@thetesseractacademy.com