Questions we hear most often

Ivertiq provides a sovereign Full-Stack AI Harness for regulated verticals: best-fit local models, Harness Control (policies, provenance, human-in-the-loop), and vertical workflow agents — deployed across a range from cost-competitive on-prem Node and high-throughput Station to siloed Private Cloud.

General tools optimize for horizontal productivity and often depend on public LLM APIs. Ivertiq is built for environments that require sovereignty, auditability, deterministic gates, and human accountability — joint optimization of models, control, and workflow apps inside customer-approved environments, not a chat UI bolted onto a cloud API.

Sensitive corporate data and operational systems are designed to stay in customer-approved environments (on-prem or private cloud). Core regulated workloads are not required to call public LLM APIs. Air-gap-capable deployments are supported where the site requires it.

Three jointly optimized layers:

1. Ivertiq Core Foundation — sovereign verticalized LMs, knowledge engine, inference optimization
2. AI Harness Control — orchestration, validation, provenance, human-in-the-loop (HITL), and determinism at the gates
3. Vertical Apps / agents — ERP ops, life sciences, and other regulated workflows

Harness Control is the operating layer; vertical agents are the apps. See how this maps to the industry AI software stack on the Platform page.

Post-training is part of the product, not a one-off science project. We shape open, best-fit local models for system capability — so the agentic layer can complete vertical workflows under customer SOPs — not for chatbot scorecards alone.

In practice that means more vertically specialized judgment and stronger agentic behavior: tool use, verification discipline, and efficient chain-of-thought. Quality and latency model tiers can be routed inside the Harness. Measured gains are workload-specific; illustrative ERP results and method detail are on the Technology page. Full eval packages are shared with design partners and investors under NDA.

Sovereignty is not useful if the workload will not fit the room. Inference-compute optimization is how Ivertiq makes useful agent concurrency run on the customer’s appliance — Node and Station on platform classes such as GB10-class and RTX 6000 / RTX PRO 6000-class — without metering every token to a public LLM API.

It is a product layer, not a prompt overlay: right-sized models and serving so production inference matches on-prem economics, while post-training can still run where GPU is abundant (private cloud). Technique choices are scoped per deployment; the buyer outcome is operable local serving, not a cloud-default model squeezed onto a box. See Technology and Deployment.

No. Owning weights is necessary and not sufficient. A closed-model API has a high floor and a low ceiling — you cannot improve the underlying intelligence with your Cases, SOP language, and reviewer corrections. A local general model without vertical-specific fine-tuning is still a general assistant in the customer’s room. Ivertiq is the third product: vertical-specific fine-tuning, system post-training, and a HITL Harness, served on-prem. The Harness stays; the local engine can be replaced. See Why Ivertiq.

Governed path from document to decision: Ingest → Extract → Align → Govern → Assure → Brief & act.

Uncertain or consequential steps are human-gated. Systems of record (ERP, QMS, lab systems) stay authoritative. See the full path on the Platform page.

No. Ivertiq is a Harness that turns best-fit and vertically post-trained local models into controlled, auditable workflow execution inside the customer environment. Not a chatbot wrapper. Not a rented API.

For Sovereign Enterprise Operational Intelligence, we are an intelligence layer on the ERP they already run — we do not replace or re-platform the ERP. For Life Sciences, we implement existing SOP workflows under HITL; LIMS and QMS stay the system of record.

The human gate is part of the product. Nothing writes until a named person confirms. AI assists the reviewer; it does not replace them. Pilots can start from export/snapshot data before a write API exists.

Not silently. Consequential actions require human confirmation and, for ERP write-back, a controlled connector. Pilots may start with export/snapshot data before a write API exists.

Across a deployment range:

• Ivertiq Node — cost-competitive on-prem (e.g. GB10-class, RTX 6000/RTX PRO 6000-class configs)
• Ivertiq Station — high-throughput on-prem for concurrent sessions
• Ivertiq Private Cloud — highly scalable multi-tenant siloed private cloud

Same Harness stack; sized to economics, concurrency, and operating model. Deployment page →

We focus on regulated verticals. Starting beachheads:

• Life sciences — forensic LC-MS review as current proof; CRO and MAH as peer modules on the same Harness
• Sovereign enterprise operational intelligence — AI assist layer on existing ERP (e.g. invoice verification and exception briefs)

We are defined by problem type (complex workflows, high-stakes decisions, regulatory oversight, explainable AI). Later domains (e.g. banking, legal) follow productization of the first wedges — they are not the current agent catalog lead.

No. Beachheads may start in specific markets (e.g. Taiwan ERP reseller motion; life-sciences design partners), but the guiding constraint is regulated verticals, not geography. The product and deployment model are built for global expansion.

Start a pilot conversation via Contact. Typical first step: scoped design-partner pilot with clear KPIs, HITL gates, and (for ERP) a snapshot-first demo path such as invoice verification.

No. On accounts a partner introduces, the partner owns the customer relationship. Ivertiq does not bypass the channel.

Partners and customers engage Ivertiq in two ways. Both run on the same sovereign Full-Stack AI Harness and the same deployment range (Node, Station, Private Cloud). What changes is packaging and who owns day-to-day customer engagement:

• Design-partner depth — for complex regulated workflows where Validation Pack fit, auditability, and specialist networks (e.g. CRO, MAH, forensic) decide the buy. Typical path: scoped pilot, then an owned or siloed deployment.
• Productized attach — for repeatable agents that attach to systems partners already sell — for example an intelligence layer on top of existing ERP for SMEs (we do not replace or re-platform the ERP). Partners own the customer relationship; we supply a Harness package sized for cost and concurrency, with human-in-the-loop on the high-frequency path.

Details on the Partners page.

An attach-on intelligence layer — starting with Invoice Verification Assist — not a competing ERP. Expand to remittance briefs, exception radar, and related ops agents. Same Harness; your brand relationship with the customer.

You meet customer demand for AI without expensive one-off ERP customization for every micro-difference. Small process quirks can often be absorbed in the AI/Validation layer. You keep renewal ownership; we provide product, demo kit, and deep-tech support.

Yes. Current demo discipline uses export/snapshot data to prove read → match → brief value. Controlled write comes later via Adapter. Do not demo silent auto-posting.

Typically: 3–5 named account candidates willing to explore a pilot; a first demo focused on supplier invoice verification (parse → match → pass/fail checklist); one agreed pilot KPI (for example time-to-checklist or exception rate); and a snapshot-first scope — ERP export/snapshot data under NDA/pilot terms, with no write-back required for the first proof.

Adjacent motion: agents can help flag process-control breaks and assemble evidence packs. You own certification projects; we own continuous enforcement tooling — we do not sell ISO certificates.

Co-sell sovereign assistants under partner delivery models that respect sponsor, principal, and evidence confidentiality. Human review remains mandatory for accountable decisions. For forensic drug analysis, that often means assisting expert review of LC–MS / LC–MS/MS instrument PDFs — where database HIT scores are starting points, not sign-off. See Life Sciences and the forensic case study.

Common path: scoped pilot, then subscription and/or appliance (Node / Station / Private Cloud) packaging. Partner margin and territory terms are set in the partner agreement — directional economics are discussed commercially, not as website price lists.

Yes — enablement is part of the partner motion (demo script, positioning, do-not-demo boundaries, and technical follow-up). Deep architecture is for SE sessions, not the first sales call.