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Last reviewed: 25 April 2026 · Spot something wrong? Tell us and we'll fix it.
Deep dive · for sceptical readers

The architecture behind LEASE-iQ.

Four design choices that make LEASE-iQ accurate where general AI fails. Written for engineering reviewers, lawyers, and anyone evaluating LEASE-iQ on substance rather than slogans.

The four design choices

Each one closes a failure mode that breaks generic AI on legal text.

Hallucination cannot be eliminated at the base model. It can be detected, outvoted, and flagged. The architecture below is what makes that real.

1Context engineering, not training

LEASE-iQ never trains on your lease. Documents are ingested via open-source OCR plus multimodal LLM parsing, then converted into a proprietary graph structure that captures hierarchical relationships between clauses, schedules, and cross-references. UK legislation is pre-processed into LLM-compatible rules before any user interaction. At query time, only the relevant clauses and statutory rules are assembled into context. The model answers only from that supplied context, never from training data.

Three-level reference chaining. A clause like "subject to Schedule 2 paragraph 5(b)" is automatically resolved to (1) the schedule, (2) the definition that governs the term used in the schedule, and (3) any deed of variation that overrides the original wording. Generic AI flattens all three into prose and loses the precedence.

Closes: the autocomplete failure mode where LLMs invent text across page breaks, columns, stamps, and redactions; and the flat-prose failure mode where generic AI cannot tell which clause overrides which.

2The Juror consensus model

For every question, 10 independent responses are generated in parallel. A semantic similarity step (NLP, not LLM) scores each response for meaning. Responses below average similarity are discarded as outliers. The remaining responses go to 3 LLM agents that vote on the best answer. If no consensus forms, notes are passed back and the cycle repeats up to 3 iterations. If still uncertain, LEASE-iQ tells you it is uncertain rather than guessing.

Closes: the confident-wrong failure mode. Disagreement between models is surfaced, not hidden. If three independent agents cannot converge on the same answer to your question, that fact is itself the answer.

3The regulatory overlay (and how it stays current)

UK statute and case law are pre-processed into a machine-readable rules layer that sits over the lease ontology. The Landlord and Tenant Act 1985, CLRA 2002, BSA 2022, LAFRA 2024, the Renters' Rights Act 2026, and the leading tribunal authorities are applied at query time, not relied on from foundation-model training data.

Currency is maintained by a human review process: when a new Act, statutory instrument, or material tribunal decision affects how a lease should be read, the regulatory overlay is updated. Page-level "Last reviewed" dates throughout the site show the live state. Human approval is always required before anything ships into the live overlay. We do not run an automated cadence; updates land when the law moves.

Closes: the stale-training failure mode. New legislation is added through a defined human-in-the-loop process, not waiting for the next foundation-model release.

4Data residency and KC-level honesty

Data residency and training position. All processing happens on GCP Europe West 2 (London). Your lease is contractually prohibited and architecturally isolated from model training, with the contractual position covering Google's models, our own, and any third party.

KC-level honesty on edge cases. Some lease clauses are genuinely ambiguous. Even a King's Counsel might give different answers on different days. LEASE-iQ flags these honestly instead of guessing. A risk-rating system is shipping in the next release that will grade clause certainty so you know when to seek professional advice.

Closes: the data-leakage and accountability failure modes. Your lease never trains a model. When the answer is genuinely uncertain, you are told it is uncertain.

Hallucination cannot be eliminated. It can be detected, outvoted, and flagged.

All large language models require randomness (temperature) to function. Hallucination cannot be fully eliminated at the base model level. Any tool that tells you otherwise is overclaiming.

What LEASE-iQ does instead: generates 10 responses in parallel, removes outliers through semantic analysis, has 3 independent agents vote, and flags uncertainty rather than guessing. LEASE-iQ also answers only from context supplied at query time (your lease plus the pre-processed regulatory overlay), not from training data. This removes the category of hallucination where a model "fills in gaps" from general knowledge. The remaining residual risk is managed through consensus and honest uncertainty.

This is the position we hold publicly because it is the position that survives a hostile read. The page you are on is the page we want a sceptical lawyer, a sceptical engineer, or a sceptical journalist to find when they look for the catch.

Built by

Sean Gilley, Building Trust CTO

14 years in AI and data engineering (from large engineering consultancies to NLP-powered regtech startups) and 4 years at Google Cloud as Director of Engineering at Onix (a Google Cloud partner). LEASE-iQ is not a ChatGPT wrapper. It is a purpose-built legal intelligence system that uses no vendor-locked infrastructure and runs on a portable, multi-provider architecture.

SG

Sean Gilley

Co-founder and CTO, Building Trust. Former Director of Engineering at Onix.

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