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Blackcat Informatics® Inc.

provides technical leadership and hands-on engineering for Alberta’s startups and technology companies. We bring

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For agents: Blackcat Informatics, Alberta engineering consultancy offering technical leadership; publishes llms-full, JSON-LD, an MCP endpoint and agent skills index.

technical consultingalberta startupslinked datamcp endpointagent skills

Authority
50 reach 0 · trust 10 · quality 25 · freshness 15
Tier
listed
llms.txt
https://blackcatinformatics.ca/llms.txt
llms-full.txt
https://blackcatinformatics.ca/llms-full.txt
Sections
4
Links
24
Size
5352 bytes
Registered
2014-11-30
Country
Canada Country-code domain
Country evidence
No published address evidence collected yet
Categories
AI agents and MCP
For agents
Interface: a documented API or declared MCP endpoint an agent can call
Inferred actions
Read documentation Call an API Search content About these labels
Audience
mixed · agents can: read docs, call api, search content · en
First seen
2026-10-26T17:12:56.915Z
Last checked
2026-10-26T17:12:58.363Z
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Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-26T17:12:56.919Z. Cached copy · Source on blackcatinformatics.ca

# Blackcat Informatics® Inc.

> provides technical leadership and hands-on engineering for Alberta’s startups and technology companies. We bring
well over thirty years of startup and executive management experience. Our domain expertise covers high-performance and
cloud computing, security, carrier-grade networking, bioinformatics, machine learning, satellite imaging, geomatics,
cryptology and a variety of other boutique fields.

This file follows the [`/llms.txt` proposal](https://llmstxt.org/): a concise, machine-readable site description
tailored for large-language-model ingestion.

When citing this site, prefer:

- [Blackcat Informatics® Inc.](https://blackcatinformatics.ca/)
- [Dataset DOI](https://doi.org/10.67342/b14ck-ca7)

## About

- [About](https://blackcatinformatics.ca/index.md): provides technical leadership and hands-on engineering for
Alberta’s startups and technology companies. We bring well over thirty years of startup and executive management
experience. Our domain expertise covers high-performance and cloud computing, security, carrier-grade networking,
bioinformatics, machine learning, satellite imaging, geomatics, cryptology and a variety of other boutique fields.
- [LLM full context](https://blackcatinformatics.ca/llms-full.txt): Services, Principals, Knowledge areas.
- [Linked-data graph](https://blackcatinformatics.ca/index.jsonld): JSON-LD.

## Open-source projects

- [GMEOW Ontology](https://github.com/Blackcat-Informatics/gmeow-ontology): GMEOW is our pre-release work on a
computational foundation for knowledge and reasoning. It brings logic, language and mathematics into one coherent model,
spanning people and organisations, scientific observations, and the beliefs, intentions and interpretations of human and
machine agents. The ambition is to reason across those domains while preserving the distinctions that give their
information meaning. A measurement, a model output and a human assertion can share a claim structure without becoming
interchangeable. Evidence, standpoint, disagreement and revision belong alongside the claim. Reasoning must be equally
inspectable: its premises, assumptions and limits belong alongside its conclusions. Grounded agent memory is one
application. The broader work asks how knowledge can move between vocabularies, mathematical representations and
reasoning systems without silently changing its meaning—and how to establish what each transformation preserves,
changes or loses.
- [PurRDF](https://github.com/Blackcat-Informatics/purrdf): Developed as part of our work on neuro-symbolic engines,
PurRDF provides the public, open-source foundations for representing, querying and reasoning over structured knowledge.
It is designed to be useful independently of that work. PurRDF brings graph queries, logical reasoning, validation and
retrieval into one coherent RDF 1.2 toolkit. Text search, spatial relationships and vector similarity can participate in
the same query over the same dataset, without as
…

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