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aplexioCopilots

Private AI for SAP

SAP Copilots deployed into your environment.

Focused AI agents for SAP day-to-day. In production, aplexio deploys each Copilot into your cloud tenant or on-prem environment, on open-weight models under your control — aplexio never sees your SAP data, and there is no aplexio backend between you and the answer. The free version runs in Claude Code with Anthropic's cloud model; try it on sample or redacted data.

Available now

SAP Logistics Copilot

Paste an SAP export. Get the root cause in seconds.

Five skills for SAP logistics teams: IDoc errors (WE02, E1EDL20) live today; delivery blocks (VL06O), stock discrepancies, freight-cost variances, and open-invoice issues planned. For production we install it in your environment and hand you a running system.

Roadmap

Four more Copilots planned, not yet shipped.

Planned

SAP FI Copilot

Month-end close, faster.

Resolve open items, GR/IR clearing, intercompany reconciliations.

Planned

SAP SD Copilot

Order-to-cash sanity checks.

Pricing inconsistencies, credit blocks, delivery-to-invoice gaps.

Planned

SAP MM Copilot

Purchasing & inventory answers.

PO release strategy, stock variance, vendor-master cleanup.

Planned

SAP EWM Copilot

Warehouse-management signal over noise.

Wave planning, putaway errors, goods-receipt exceptions.

Private AI

Your data never leaves your control.

aplexio deploys every Copilot into your environment — your cloud tenant, your on-prem, your rules. Open-weight models (Llama, Qwen, DeepSeek) run under your control, with no phone-home and no telemetry.

We never receive your SAP exports. We never store them. We never train a model on them. No aplexio backend sits between you and the answer. That's the whole supply chain.

The stack

What we deploy. No mystery box.

The components are proven open source — the value is making them run on your workflows, your data, your hardware.

Models

Llama, Qwen, DeepSeek, GLM, Mistral — selected per use case, hardware budget, and license. Vendor-independent.

Inference

vLLM as the production serving layer — the same engine the large deployments run, on your GPU.

Interface

Open WebUI for people, an API for your systems — or straight into the tools your team already uses.

Hardware

Compact models run on a single GPU; a 70B-class model at 4-bit quantization needs roughly 35–40 GB of VRAM. Sized in scoping.

Put a Copilot in your environment.

We deploy to your cloud tenant or on-prem. Try the free version first, on sample or redacted data.