On-premises AI for manufacturing plants and other operations that can't send data to the cloud. Diagnose equipment faults, capture retiring engineers' know-how, and search every SOP, manual, and maintenance log — air-gapped, on hardware you own.
Onsite AI is on-premises AI infrastructure for manufacturing plants and regulated industries — deployed inside the customer's perimeter, on hardware they own, so data never crosses the network boundary. It ships in three tiers: Local GPT (an air-gapped chatbot on a local open-weight model), Enterprise RAG (a chatbot that reasons over the customer's entire document corpus with cited answers), and fAI Model (a model fine-tuned to the customer's industry with agentic capabilities on a high-availability cluster).
Every deployment is fully air-gap capable, uses signed offline update bundles, and runs alongside an on-prem RAG pipeline that indexes SOPs, manuals, PLC documentation, maintenance logs, and other corporate documents. Onsite AI is a product of Inpactive, serves customers across the United States, and every engagement starts with a two-week Discovery & Scoping Engagement led by a senior technologist delivered on-site.
On-premises AI gives you what cloud LLMs can't: data that never crosses your perimeter, usage that doesn't meter, and a deployment posture you fully own.
Inference runs on your hardware. No logs, no telemetry, no training data leakage to a vendor.
Fixed hardware cost, no per-query billing. Usage doesn't meter as adoption grows.
Open-weight models sized and quantized for your GPUs. Latency tuned to interactive use.
Runs fully offline, or with controlled egress. Update cadence, model choice, and egress posture stay under your operations team. How air-gapped LLM deployment actually works →
Vector and keyword retrieval (RAG) over millions of documents. Answers cite their sources.
Signed model bundles applied offline during your change windows. No forced upgrades.
Deeper on the same axes: read the Onsite AI vs. ChatGPT Enterprise & Microsoft 365 Copilot comparison, including where cloud leads on frontier reasoning and long-context work.
| Commercial cloud AI services (ChatGPT, Copilot, etc.) | On-premises AI | |
|---|---|---|
| Data leaves your network | Yes — prompts and attached documents transit vendor infrastructure | No — inference, retrieval, and storage stay inside your perimeter |
| Per-query cost | Metered per token or per seat; usage cost grows with adoption | Fixed hardware cost; unlimited internal usage once deployed |
| Air-gap / OT-network compatible | No — requires outbound internet to the vendor | Yes — runs fully offline, updated via signed bundles |
| ITAR / CMMC workloads | Not on commercial SaaS AI — requires GovCloud-class environments | Yes — deployable inside accredited enclaves |
| Latency | Round-trip to vendor region; variable under load | Local GPU inference; tuned to interactive use |
| Who controls updates | Vendor — model version and behavior can change without notice | You — model bundles applied on your change window |
The same on-premises stack, applied to plant-floor problems: fault diagnosis, tribal-knowledge capture, and searchable answers over work orders, ladder-logic documentation, and downtime logs — none of it leaving the OT network.
A shift in the life of the system
2:14 AM. A packaging line stops on an Allen-Bradley PLC fault F-52014: servo drive overtemp on axis 2. The technician on shift opens the plant's chat UI on the panel PC, pastes the fault code, and asks whether line 3 has seen it before.
The retrieval pipeline searches the plant's own corpus — the drive manual, four years of CMMS closeouts on that asset, and the alarm history from the historian. The cited answer comes back in seconds: the fault has hit this axis three times in the last two years, and each closeout notes a chafed servo cable inside the drag chain. Two of the closeouts link to a photo of the exact rub point.
She unlocks the cabinet, finds the rub through the same section of drag chain, splices the cable, and clears the fault. The question, the retrieval, the answer, and the closeout note all stayed on the plant's OT VLAN. No cloud round-trip, no vendor telemetry, no data crossing the perimeter — and the closeout she just filed becomes the fourth citation the next technician sees.
The workflow above, generalized: fault codes, equipment manuals, PLC docs, and closeout notes indexed as one corpus. See the full fault-diagnosis workflow →
Retiring engineers' redlines, tuning notes, and shift logs become a searchable corpus with citations. Junior technicians ask the way they'd ask a senior.
Downtime logs, work orders, and OEE loss buckets in the same corpus. Reliability engineers ask across the fleet and get cited answers.
SOPs, vendor manuals, spare-part diagrams, and free-text CMMS notes indexed alongside alarm history — with per-shift summaries for handoff.
Every deployment ships with the full stack your auditors and operators expect, at one price.
Continuous batching and paged attention keep KV-cache memory allocated only to what live requests actually use, so the entry tier sustains its concurrency target through mixed workloads instead of collapsing at peak. This is the difference between the sizing tables working in production and working in a demo.
The familiar chat UI. Teams start on day one with no retraining.
Every prompt, response, and admin action is recorded. Logs stay inside the perimeter and forward one-way to your SIEM on the change window.
LUKS with AES-256 at rest. Encrypted snapshots and backups.
Binds to the customer's Active Directory or FreeIPA, with RBAC on the same groups and MFA via TOTP or FIDO2. No cloud IdP dependency.
SSO (SAML 2.0 / OIDC), document import from SMB and SharePoint, controls mapped to SOC 2 / ISO 27001, backup automation, and per-tenant usage analytics ship in every deployment — full list on request.
When regulation, classification, or contractual obligation rules out cloud AI, on-premises is the only path to the same capability.
Plant data stays inside the air gap.
Plant-floor OT networks are isolated from IT by design, and PLC code, schematics, and SOPs are core process IP. On-prem AI diagnoses faults, captures retiring engineers' knowledge, and bridges shifts without anything leaving the air gap. Common deployments: PLC fault diagnosis, tribal-knowledge capture, SOP search, and CMMS analysis — see what plants run on Onsite AI.
ITAR, CMMC, and classified programs — inside the enclave.
Sending ITAR/EAR-controlled technical data to a commercial cloud AI service is generally treated as an export. Run inference inside accredited, air-gapped enclaves while teams query controlled drawings, requirements, and test data under CMMC. How on-premises AI supports a CMMC / ITAR posture →
PHI stays in the hospital network.
HIPAA-protected records, imaging, and clinical notes carry real liability the moment they leave your premises. Keep PHI in-building while clinicians and researchers query the full corpus with a complete audit trail.
Behind the NERC CIP boundary.
NERC CIP and SCADA isolation keep critical-infrastructure systems off the public internet. On-prem AI surfaces operational and compliance knowledge without breaching the security perimeter.
CUI and classified workloads, on accredited networks.
CUI and classified workloads require data sovereignty that public cloud cannot guarantee. Deploy on accredited, isolated networks with controls aligned to NIST 800-53 High and DoD SRG IL5–6.
Other regulated environments — legal & professional services, financial services, pharmaceutical R&D — follow the same pattern: keep the controlled corpus inside the perimeter and query it there.
Every engagement begins with a two-week discovery led by a senior technologist. The output is a written solution architecture and tiered pricing. From there you pick a tier, run an on-site proof of concept on your own hardware, and deploy — nothing ever leaves your perimeter.
For the sizing rationale behind these configurations — what drives VRAM, why the reference architecture picks RTX PRO 6000 Blackwell over H100, and how to size for concurrent users at your target context length — see the buyer's guide to LLM hardware requirements.
| Specification | Local GPT | Enterprise RAG | fAI Model |
|---|---|---|---|
| Form factor | Single inference server | Multi-model single server | Dual-node HA cluster |
| GPU | 2× NVIDIA RTX PRO 6000 Blackwell | 3× NVIDIA RTX PRO 6000 Blackwell | 4× NVIDIA RTX PRO 6000 Blackwell |
| Total VRAM | 192 GB | 288 GB | 384 GB |
| System RAM | 512 GB ECC | 768 GB ECC | 1 TB ECC across nodes |
| CPU | Dual AMD EPYC 9355 | Dual AMD EPYC 9355 | Dual AMD EPYC 9355 per node |
| Storage | 8 TB NVMe + 32 TB SAS | 16 TB NVMe + 48 TB SAS | 16 TB NVMe + 64 TB SAS |
| Interconnect | — | — | InfiniBand HDR 200 Gb/s |
| Concurrency | 40+ engineering users | 40+ engineering users | Cluster-scale |
| Warranty | 3-year parts & labor | 3-year parts & labor | 3-year parts & labor |
The entry tier is two RTX PRO 6000 Blackwell cards in a single 4U server: 192 GB of VRAM, dual EPYC 9355 CPUs, 512 GB of ECC RAM. Once it's racked, adoption doesn't meter and updates land on your change window.
The questions plant managers, engineers, and compliance leads ask us most.
It gives technicians and engineers a searchable, cited answer engine over equipment manuals, PLC documentation, fault histories, and CMMS notes. When a line goes down, staff query the fault code, recent work orders, and vendor manuals in one place — cutting time to diagnosis and shortening mean time to repair.
SOPs, redlines, shift logs, and unstructured notes become a searchable corpus with citations. New technicians ask questions the way they would ask a senior engineer, and the system answers with references to the plant's own documentation — turning tribal knowledge into a durable, queryable asset.
Yes. Onsite AI runs fully offline inside the plant's OT network — inference, retrieval, and storage all stay behind the air gap. Model and software updates arrive as signed bundles applied on your change window, so nothing calls out to the internet.
A single inference server with 2× NVIDIA RTX PRO 6000 Blackwell GPUs and 192 GB of VRAM supports 40+ concurrent users for the entry tier. Multi-model and clustered deployments scale up to 4 GPUs and 384 GB of VRAM across two nodes. Full reference specifications are on this page.
Yes. On-premises AI runs entirely inside the plant's air-gapped OT network, so PLC code, schematics, and SOPs never leave the building. It diagnoses faults, captures retiring engineers' knowledge, and bridges shifts while keeping process IP fully private.
No cloud PoC. Every engagement begins with a two-week Discovery & Scoping Engagement led by a senior technologist, delivered on-site. The output is a written solution architecture, deployment plan, and tiered pricing across three offerings: Local GPT (an air-gapped chatbot on a local model), Enterprise RAG (chatbot that reasons over your corporate document corpus), and fAI Model (a model fine-tuned to your industry with agentic capabilities). Any proof of concept that follows runs on customer-owned hardware inside your perimeter — not in the cloud.
Tell us about your environment and we'll scope a deployment that fits.