01
You don't need to learn AI
LLMs, agents, embeddings — you don't need to know these terms. Lawyers know cases. Researchers know their fields. We translate between technology and domain. That's our work.
AxiomLab — Domain-First AI System Design
AI systems, designed for
law offices and research labs.
Not a general-purpose AI tool.
A system built from scratch — for your work, in your domain.
You don't need to learn AI. We only need you to know your practice.
No trend-chasing required. Your domain comes first — the system follows.
02 — Methodology
Why we start from the axioms of your domain
General-purpose AI is built for the average.
Professional work is not average.
We design every system from scratch — for your practice alone.
02
A system that compounds
Not a tool that decays with use. A year of one lawyer's casework becomes a searchable, reusable asset — embedded in the system itself.
03
We stay until it lands
Design and build are not the end. We stay with you until the system is in your team's hands and embedded in real workflows.
03 — Solutions
Three domains. Three distinct systems.
Not one system marketed three ways.
Each is designed from scratch for its domain.
LAW OFFICES
BETA — Live
JurisCorpus
AI automation system for law offices — from case memo drafts to precedent analysis
- Automatic de-identification of client information at intake
- Drafts for case memos, review reports, and judgment outlines
- Case search and similar-precedent matching
- Built for small to mid-sized law firms
NATURAL SCIENCES
Internal / External onboarding in prep.
Empirica
Research lab AI system — literature, protocol drafts, report scaffolding
- Automated PubMed / Web of Science search and synthesis
- Draft experimental protocols grounded in domain patterns
- Grant reports, presentation drafts, paper sections
- For PIs across medical, life, physical, and earth sciences
HUMANITIES & SOCIAL SCIENCES
Internal / External onboarding in prep.
Humanis
Research AI for humanities and social sciences — from interview coding to SSCI manuscripts
- Qualitative interview coding and theme extraction
- Literature synthesis matrices and critical review drafts
- Evaluation reports and SSCI-style manuscript scaffolds
- For humanities and social science researchers
04 — Engagement
Five stages, from working session to system that compounds
Stages 01–04 take 6–10 weeks. Stage 05 is the ongoing relationship.
01 / DIAGNOSE
Diagnose
30-minute working session. We map your current workflows and bottlenecks together.
02 / DESIGN
Design
Domain axioms documented. We write down the core reasoning structure of your practice.
03 / BUILD
Build
Knowledge base, RAG pipeline, and agents — built with you, grounded in your axioms.
04 / DEPLOY
Deploy
Real-world use. Until it's in your team's hands. And after.
05 / COMPOUND
Compound
Every cycle adds to your knowledge base. The system gets stronger with use, not weaker.
05 — In Operation
Systems in operation
Not demos. Running in actual practice today.
BETA — Live
DomainLaw Firm A
Size~20 attorneys
SolutionJurisCorpus
First-draft case memo workflow runs alongside the attorneys.
BETA — Live
DomainLaw Firm B
Size4 attorneys
SolutionJurisCorpus
Small firm carrying caseload capacity of a mid-sized firm.
DomainS University Medical School Research Lab
SolutionEmpirica
Literature search and synthesis handled by the system.
DomainHumanities Researcher A
FieldSociology
SolutionHumanis
Evaluation reports, research assistance, and admin tasks systematised.
06 — Principles
What we believe
“A good AI system starts from the axioms of its domain.”
“A well-built system gets stronger the longer it runs.”
“AI does not replace experts. It extends how experts already work.”
“A good system starts from necessity, not fear.”
07 — Trust & Security
Client data and research data — protected from intake to output
01
Automatic de-identification
JurisCorpus de-identifies all client information at the moment of intake — before any processing begins.
02
Local and on-premise options
Sensitive data can be processed entirely on-premise, without ever touching external APIs.
03
No training data leakage
Systems are configured so that operational data is not fed back into external model training.
04
Not a model handoff
We do not throw client data into a general-purpose model. We build a domain-specific processing pipeline, every time.