Multiply your operational efficiency with end-to-end process automation, enterprise LLM integrations, intelligent RAG assistants and predictive analytics models — without ever compromising on in-house data privacy.
[INIT] Model Gateway: vLLM Cluster v0.6.2TPU/GPU: 4x H100 80GB
> QUERY: "2024-Q3 shipment cost deviations & supplier risk score"
↳ Matched 520,000 document embeddingsCosine Sim: 0.984
Enforce Local Vault: PII anonymized • Outbound Internet: Blocked
Synthesized verified response (41 tokens/sec)TTFT: 48ms
Not just theoretical models — we build production-grade AI engines that integrate directly into your enterprise architecture via gRPC and REST APIs.
ERP/CRM-integrated, multilingual support bots and intranet assistants that instantly search your internal knowledge base and remember conversation history 24/7.
Error-free extraction of structured JSON data from scanned waybills, invoices, contracts and customs declarations, with direct transfer to your accounting system.
Minimize stock waste with regression and deep learning models that process historical sales, warehouse stock, weather and seasonal trends.
Camera-based defect detection on production lines, shelf layout verification for sales reps, barcode-free parcel sorting and industrial security camera analytics.
In-house open-source models trained with LoRA/QLoRA on your industry's terminology (legal, finance, healthcare, retail) and your internal documentation.
Financial fraud and suspicious transaction detection, route and shipment optimization in logistics, and an automatic summarization engine that synthesizes KPI data for senior management.
Unlike the standard chatbot packages on the market, we offer an engineering approach that delivers data ownership, hardware optimization and ERP depth.
Your trade secrets, customer records and accounting documents never end up in the training data pools of third-party public models. Everything runs entirely on your company's own local servers or in your isolated private cloud VPC.
Zero Cloud Exfiltration Guarantee
Instead of superficial general-purpose models, we build a living corporate memory fed by your ERP records, technical specifications, dealer contracts and departmental rules. The risk of hallucination is driven to zero.
Vector Indexing & Semantic Matching
Connects to Bilnet S4B, the CostCloud FinOps infrastructure, SAP, Logo or your own in-house custom software through a single interface via REST API, high-speed gRPC and webhooks — no system changes required.
gRPC / Kafka / RESTful API Endpoints
We move forward with measurable goals instead of open-ended experiments, reporting every step from PoC to live production with verified metrics.
Your data warehouse, PDF archive or databases are cleaned, gaps are analyzed and a clear ROI is calculated.
Feasibility Report & Scope
Open-source models (Llama 3/Mistral) are benchmarked against enterprise APIs, and accuracy tests are simulated on a small dataset.
Live Prototype & Benchmark
Vector databases (pgvector/Qdrant) are set up, and enterprise authorization (RBAC) and firewall filters are connected.
Fully Integrated gRPC/REST API
Fast inference with vLLM, GPU hardware optimization and 24/7 telemetry monitoring of model drift and output quality.
99.9% SLA & Telemetry
We semantically indexed the entire archive of our enterprise client, active in international finance and law, using our RAG architecture running on local servers fully disconnected from the internet. Cross-contract risk analysis was cut down to minutes.
“Thanks to the on-premise RAG architecture Bilnet built, we identify risks and inconsistencies in our contracts within minutes while being one hundred percent confident in the security of our client data. Our operational workload has become dramatically lighter.”
Details on data privacy, hardware requirements and project processes in enterprise AI projects.
Absolutely not. Our primary approach at Bilnet Software is to deploy models in your company's own data center (on-premise) or in an isolated private cloud network dedicated to you (private VPC). None of your internal data, customer records or financial information is transferred to third-party servers, and it can never be used to train public LLMs.
Yes. We provide complete deployments that run in air-gapped environments, physically disconnected from the external internet. Llama 3.3, Mistral, DeepSeek or custom-trained models are hosted locally on your organization's GPU servers, and all data exchange takes place over your local network (LAN).
Classic search only offers keyword matching, while a RAG architecture turns the meaning of your documents and ERP data into vectors. When you ask a question, it finds the relevant pieces of information semantically and provides them to the LLM as references. The model generates answers using only verified information from your own source documents, eliminating the risk of hallucination.
We deliver a typical enterprise PoC within 2 to 3 weeks. During this time, we demonstrate concrete success metrics through data cleaning, selection of the right open-source model and a working interactive interface.
Let's review your dataset and processes together, and clarify the right model architecture, hardware requirements and ROI projection for your company in the very first meeting.
Mutual NDA (Non-Disclosure Agreement) guaranteed before the project
Response from an AI Solutions Architect within 24 hours
Free feasibility study and PoC roadmap
Our engineering team will prepare a preliminary architecture draft tailored to your requirements and get in touch.