Large language models generate confident but fabricated outputs. In legal, medical and financial contexts, a single hallucinated citation can result in professional liability, regulatory penalties and irreversible reputational damage.
Regulated industries cannot act on AI outputs they cannot audit or explain. Legal teams, compliance officers and government decision makers require full reasoning transparency, not just a ranked list of search results.
Many regulated organisations cannot send sensitive data to external AI APIs. On premise small language models provide the full AI intelligence capability without any sensitive data leaving the organisation perimeter.
| Product | What It Does | Business Value Delivered |
|---|---|---|
LexIntel RAGCore Platform |
Neuro symbolic multi hop reasoning engine for querying case law, regulatory databases and contract corpora. Every conclusion is grounded in a retrieved source and validated by a symbolic logic step — eliminating hallucinations at every inference hop. | Enables legal teams to perform deep precedent research and regulatory interpretation in minutes with every conclusion attributable to a specific verified source rather than model inference alone. |
Adaptive Compliance RAGSelf Correcting Agent |
Self correcting RAG agent that learns from legal and compliance expert corrections in real time — improving accuracy with every review cycle and adapting to evolving regulatory frameworks without requiring retraining of the underlying model. | Deployed by compliance teams who need an AI system that continuously improves from their own domain expertise, not just from pre training data that may lag behind current regulatory developments. |
PrivacyImpact Intelligence SLMOffline SLM |
Fine tuned offline small language model automating privacy impact assessments — analysing data flows, identifying high risk processing activities and generating detailed PIA reports with attorney review ready output, entirely within the organisation perimeter. | Automates the most labour intensive compliance workflow in data handling organisations, compressing assessment cycles from weeks to hours without sending sensitive operational data to external APIs. |
DataDiscovery Intelligence SLMOffline SLM |
On premise small language model for automated sensitive data discovery across structured and unstructured enterprise repositories — identifying personal data, financial records, health data and trade secrets across hybrid cloud environments. | Enables privacy officers and information security teams to map sensitive data holdings accurately and respond to data subject requests with AI assisted precision across complex multi cloud environments. |
Unlike pure neural LLMs, LexIntel combines symbolic logic with neural retrieval — every conclusion is grounded in a retrieved source and validated by a logical rule, not generated from model weights alone where fabrication is possible.
PrivacyImpact SLM and DataDiscovery SLM run entirely within your infrastructure — sensitive legal, health and financial data never leaves your perimeter, satisfying the strictest data residency requirements in any jurisdiction.
Adaptive Compliance RAG improves measurably from expert corrections — making it uniquely suited to regulated environments where accuracy compounds in value and where regulatory frameworks evolve continuously.
| FaceOff AI — Agentic RAG Intelligence | Dimension | Legacy and Single Modality Platforms |
|---|---|---|
Neuro symbolic reasoning with zero hallucinations — every output is source attributedSymbolic validation · citation chains · no model fabrication possible |
Accuracy | Pure neural LLM outputs susceptible to confident hallucination with no verifiable source attribution |
On premise SLM deployment options with full data residency — sensitive data never leaves perimeterPrivacyImpact SLM · DataDiscovery SLM · air gapped deployment available |
Data Residency | Cloud API only — all sensitive data leaves the organisation perimeter for external model inference |
Self correcting agent that improves from expert feedback without retrainingReal time correction learning · regulatory adaptation · domain specialisation |
Improvement | Static models requiring costly retraining cycles to incorporate new regulatory or domain changes |
Organisations fully committed to an incumbent vendor workflow may prefer extending existing contracts before adopting a new platform.
Organisations without banking, government, healthcare, legal or media threat profiles may not yet face the vectors this platform is designed to counter.
For EDR and SIEM requirements without AI content authentication or identity proofing, a dedicated endpoint security vendor may suffice.