Every decision auditable. Every reasoning chain reproducible.
We don't build AI that guesses legal answers. We build infrastructure where legal expertise becomes deterministic code, auditable logic, and reproducible proof.
Your LLM gives you a legal answer. You ask: how did you reach that conclusion? It can't tell you. It's not broken — it's probabilistic, that's what neural networks do. But in a regulated market, where every decision must be defensible, auditable, and compliant, probabilistic is a liability.
We used AI to interpret EUDR compliance for our supply chain. Six months into export, the auditor rejected our reasoning. We had no documented justification — just "the model said so." Result: $1.2M in renegotiated contracts.
Our legal AI recommended contract terms without flagging that provincial labor law contradicted national tariff policy. The contradiction existed in our own data — the AI just missed it. We found out during litigation.
We're scaling to 5 new provinces, each with different environmental regulations. Do we have a documented, auditable method for ingesting local laws? No. Each jurisdiction is manual. Each decision is invisible. Each audit is a surprise.
Argentina — environmental laws across 24 jurisdictions with zero interoperability.
Brazil — the standard exists, but no unified governance layer sits on top of it.
Mexico — regulatory cascade creates combinatorial chaos across levels.
Entire region — lost annually to regulatory friction (Inter-American Development Bank).
The region doesn't need more laws. It needs a system that finally understands the laws it has.
These aren't distant policy ideas. They're active today, and your compliance proof must exist before audit finds gaps.
Ulpianus AI is not a better chatbot. It's not RAG over case law. It's a governance infrastructure that separates reasoning logic from linguistic output. Most legal AI: LLM reads cases → makes inferences → decides → database stores data. Ulpianus AI: expert legal reasoning → coded as deterministic governance rules → database executes the rules → decision is computed → LLM narrates the output. The edges are deterministic. The output is linguistic.
Your legal experts read provincial ordinances, tribunal rulings, contradictory regulatory frameworks and temporal changes in legislation — then model the logical structure: conditions, exceptions, conflicts, and when rules changed. This becomes your Semantic Matrix — pure logic, no LLM, no vectors, no embeddings.
A query enters in natural language. The engine routes it to the correct legal domains, queries your Semantic Matrix deterministically, checks for contradictions (fail-closed if ambiguity exists), computes the answer with certainty — not probability — and generates a proof chain. Same query → same answer → same hash. Always. This is the MSM-IAR layer (Multidimensional Semantic Matrix — Inferencia, Activación y Razonamiento).
Every decision is signed with SHA-256 and timestamped in the PoI Ledger (Proof of Inference). You can prove when a decision was made, how it was computed, and why it was correct according to the rules your experts defined. Auditors see the full chain. Regulators see reproducibility. You own the proof.
Cognitive Uncertainty and Confidence Orchestrator. For every inference, CUCO generates a confidence vector: source credibility, logical consistency, bias detection, completeness. It ensures the system never hallucinates — it knows what it doesn't know.
Logical Understanding Through Heuristic Orchestrated Reasoning. Performs dialectical analysis: Thesis (extract the normative claim), Antithesis (identify contradictory regulations), Synthesis (harmonized interpretation with legal grounding).
Consensus Understanding Through Hybrid Orchestrated Reasoning. Merges CUCO and LUTHOR outputs into compliance certificates, legislative impact assessments, and regulatory gap analyses — decision-ready intelligence.
| Competitor Class | Problem |
|---|---|
| Generic LLM + Legal Data | Does not handle contradiction. Does not audit. Does not prove compliance. Hallucinates jurisprudence. |
| Case Law RAG | Finds relevant cases. Doesn't reason from first principles. Doesn't scale to local ordinances. No auditability. |
| Consultant-Driven SaaS | Opaque reasoning. Vendor lock-in. You don't own your legal models. Auditors can't reproduce decisions. |
| Watson Orchestrate (IBM) | Externalizes governance to third-party runtime. Vendor controls reasoning. Not sovereign. |
Ulpianus: your experts define the logic. The database governs the reasoning. You own the proof. Auditors see the chain. Regulators see reproducibility. Zero vendor dependency.
MSM-IAR operates across 9 independent dimensions of legal reasoning. Law doesn't live in 1D — EUDR compliance interacts with local tariff policy, which interacts with labor contracts, which interacts with environmental zoning. The 9-dimensional space handles n-dimensional reasoning without combinatorial explosion.
| Dim | What It Tracks | Why It Matters |
|---|---|---|
| D1 | Semantic Meaning | What does this law actually say? |
| D2 | Epistemic Certainty | How sure are we of this interpretation? |
| D3 | Ethical Alignment | Is this interpretation ethically sound? |
| D4 | Temporal Context | Was this law in effect on this date? |
| D5 | Jurisdictional Scope | Does this law apply in Jujuy but not Córdoba? |
| D6 | Actor Attribution | Who issued this rule? Are they authorized? |
| D7 | Relational Topology | Does this rule depend on, or contradict, another? |
| D8 | Traceability Vector | Proof-of-Inference ledger entry. |
| D9 | Physical Integration | Real-world compliance verification (IoT, sensors, custody chain). |
Critical innovation: Ulpianus sits above the LLM layer, not within it. LLMs (GPT-4, Claude, Gemini, LLaMA) serve as linguistic translators — converting semantic vectors into human-readable text. The reasoning logic resides in the metacognitive orchestration layer. This is not a preference. It's a structural moat.
Swap foundation models without architectural redesign.
Route queries to the most cost-effective model that satisfies accuracy.
Use specialized models per task within one workflow.
As LLMs evolve, Ulpianus adapts without retraining the core system.
External orchestration enables end-to-end auditability.
Parlamento Federal del Clima + Tajamar
Proves: multi-jurisdiction ingestion is reproducible, AKN4AR works at scale, curators can model tacit expertise. Foundation for national-scale Digesto Ambiental.
Retail Legacy Migration
Proves: SETNET's DBOS philosophy works for legacy systems with 100% data integrity.
TYC Industrial Vertical
Proves: the architecture works in capital-intensive, high-risk industrial contexts, at real-world complexity.
Timeline reflects realistic operational velocity. Initial phases compressed vs. early roadmap due to focus prioritization and institutional validation sequencing.
Establish scientific credibility and reproducibility.
Demonstrate production-grade deployment in government context.
Commercialize to private legal sector and expand institutional users.
Establish Ulpianus as the regional RegTech standard for ESG/compliance.
| Segment | Avg Contract Value | Gross Margin | Payback | LTV/CAC |
|---|---|---|---|---|
| Government | $165K | 60–70% | 5.0 mo | 29.8x |
| Judicial | $130K | 55–65% | 5.4 mo | 22.3x |
| Corporate | $47.5K | 70–80% | 2.7 mo | 29.7x |
| Legal Firms | $18K | 75–85% | 2.9 mo | 20.6x |
| Academic | $10K | 70–80% | 4.0 mo | 25.0x |
All segments exceed the 3:1 LTV/CAC benchmark considered healthy for SaaS.
EBITDA 48.7% (2025) → 67.3% (2030) · Break-even 2025 Q1
EBITDA 57.6% (2025) → 70.0% (2030) · Break-even 2025 Q1
EBITDA 32.5% (2025) → 63.5% (2030) · Break-even 2025 Q1
All scenarios cash-flow positive from Year 1.
Valuation range, Jan 2026 → Dec 2028 base case.
Series A needed for acceleration, not survival — company self-funds on base case.
EBITDA trajectory, 2026 → 2028.
Non-dilutive funding projected from grants (BID, CAF, EU programs).
12-page deep dive on MSM-IAR architecture and reproducibility guarantees.
Download Technical Brief →Digesto expansion, EUDR compliance, regional deployment.
Partnership Inquiry →From Black Box to Crystal Box™
Artificial intelligence will transform legal systems. The question isn't if — it's how. Will that transformation be opaque and unaccountable? Or transparent, deterministic, and verifiable? Ulpianus AI chooses transparency. We convert legal expertise into auditable code. We make reasoning reproducible. We build infrastructure where governance lives in the edges, not the nodes.
This is not a feature. This is architecture.
The regulatory window is open for 18–24 months. The first provider to achieve scale in deterministic legal reasoning will set the LatAm standard for compliance infrastructure. We're building that provider.