Determinism
Key gates must be rule-based, testable, and reproducible.
An optimized sovereign Full-Stack AI Harness for regulated verticals.
Best-fit local models. Total localization. Joint full-stack optimization. Governed agentic control — built where provenance, auditability, determinism, sovereignty, security, and human-in-the-loop are non-negotiable.
The platform, defined
An Optimized Sovereign Full-Stack AI Harness Platform for Regulated Verticals
Start here for the definition — then explore the stack diagrams, beachheads, and case studies below.
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Optimized
LMs: best-fit, post-trained for verticals, and inference-compute optimized — so useful agent workloads fit on-prem and private-cloud hardware.
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Sovereign
Total localization on-prem or private cloud — designed so core regulated corpora and operational data do not depend on public LLM APIs.
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Full-Stack
LMs, knowledge engine, agentic layer, and vertical applications — jointly optimized and customized as one system, not bolted-together tools.
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AI Harness
Provenance, auditability, and determinism in the control plane — with policy, validation rules, and human gates over the agentic layer.
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Regulated Verticals
Where the buyer requirements are strict:
- Require provenance, auditability, determinism, sovereignty, security, and HITL (human-in-the-loop)
- Complicated but rigid workflows → AI embedded in regulated workflows (not free-form chat)
- A rich set of proprietary data and domain knowledge:
- Semi-static, unstructured knowledge and data → context-centric knowledge engine
- Dynamic systems of record → an AI intelligence layer on top of systems of record
Provenance
Material outputs cite sources, packs, and model/version context.
Auditability
Decision and reviewer logs reconstructible under scrutiny.
Sovereignty
Data and models stay in customer-approved environments.
Security
Confidentiality-first serving — not public LLM APIs for core corpora.
Human-in-the-loop
Accountable humans approve before controlled actions.
Then the detail
How the stack and form factors look
Diagrams and layers that implement the definition above — Full-Stack with joint optimization + AI Harness + vertical apps.
Where Ivertiq sits in the AI stack
Diagram 1
One Harness · three form factors
Diagram 2
Positioning
Not foundation models. Not wrappers.
Ivertiq sits where regulated customers feel the gap: between local models and real workflows — with joint optimization across foundation, control, and vertical apps.
Three form factors
Single-GB10, Dual-GB10, or siloed private cloud — same Harness stack.
Illustrative case studies
CRO, MAH, and forensic narratives — with HITL gates. Outcomes depend on each customer’s SOP and validation.
Sovereignty first
Designed so sensitive operational or evidence data can stay in customer-approved environments — without requiring public LLM APIs for that corpus.
Platform at a glance
Three layers. One system.
Vertical Apps
ERP intelligence · CTD/RA · Lab & forensic review · GxP document assist
AI Harness Control
Orchestration · multi-agent · validation rules · provenance · human-in-the-loop
Ivertiq Core Foundation
Sovereign local LLMs/SLMs · RAG/knowledge · compute optimization
Beachheads
Where we land first
Two regulated wedges — same Harness substrate, different Validation Packs and connectors.
Life Sciences
Peer sub-sectors — CRO, MAH, and forensic drug analysis — same Harness, distinct Validation Packs; human review mandatory.
Sovereign enterprise ops
On-prem intelligence on existing ERP — confidential AP/AR, inventory, and production data stay under customer control.
Why not a wrapper?
Structural moats vs frontier LLMs and thin apps — plus post-training aimed at system capability for agentic workflows.
Proof
Case study: forensic lab report review
High-volume, evidence-sensitive document work — exactly where generic cloud chat fails.
Illustrative POC
Faster review cycles
Reference narrative versus manual review — timing depends on SOP, formats, and hardware. Details on the case page.
HITL
Exception-first
Automation stops on conflicting hits; experts own the hard cases.
Local GPU
No public LLM API
Forensic data remains inside the approved environment.
Next step
Design-partner pilots, 60–90 days
Scoped agents, on-prem deployment, human-in-the-loop gates, and measurable KPIs.