On-premise enterprise AI that multiplies your productivity while your data stays in-house. GDPR and EU AI Act ready, air-gapped, self-hosted LLM and RAG infrastructure.
Your teams already use AI with sensitive data. Innoline delivers that capability on your organization's own servers, in a deployment where data never leaves. Control, audit and data stay in-house; your team accelerates without depending on the cloud. Go live in days with a single point of contact based in Istanbul.
Why in-house AI: not prohibition, but control
Organizations need AI to stay competitive. But cloud and shadow AI carry sensitive data outside the building and break compliance. Across many institutions, employees already use cloud-based services today; sending sensitive data such as core banking records, customer files or policies to those services creates a serious security and compliance risk, because the data leaves the organization's control. The only workable path is to bring AI in-house, with governance and control, without the data ever leaving the building. An on-premise architecture removes this risk at the root and lets the organization capture the full value of AI safely.
"Your employee can't hand a sensitive file to cloud AI, but doesn't want to fall behind on AI either. We solve this dilemma with in-house AI where data never leaves."
The enterprise benefits of on-premise AI
What does on-premise AI deliver to your organization? It isn't just a security choice; it's an investment that delivers concrete results.
Data never leaves the building
Questions, documents and answers stay on the organization's servers. No external API calls. Data sovereignty, audit and control are entirely yours.
In production in days
A pre-configured platform installed with a single command. No months-long integration project; business value starts right away.
Fixed cost, unlimited use
No per-token fees and no usage limits. Cost stays predictable and fixed regardless of how many questions are asked.
Your team focuses on higher-value work
Reading documents, searching and drafting move to the AI; your team focuses on work that needs judgment and decisions.
No vendor lock-in
Run any open model you want and switch whenever you like: Llama, Mistral, Qwen, DeepSeek and more. No risk of being locked to a single provider.
Not a black box
Your compliance team audits every component, your technical team tunes every parameter. Root access and governance stay with your organization.
The on-premise AI platform: not a wrapper, a complete infrastructure
The on-premise AI platform we build runs entirely on your infrastructure, from the GPU layer to the user interface. It reads your company documents, performs document analysis and grounds every answer in the source. It arrives pre-configured, runs within days, and cost is fixed rather than per token. The platform has three layers: the AI Core, the Workspace and the API Gateway. Each is detailed below.
Most AI platforms are a black box; you can't see inside, audit or change them. The on-premise platform we build is the opposite: your compliance team audits every component, your technical team tunes every parameter, from context management to retrieval accuracy and model behavior, and runs any model you choose. The system is delivered optimized; data sovereignty and control stay entirely with you, with no vendor lock-in.
AI Core: self-hosted LLM, GPU orchestration and Agentic RAG
The Core is a self-contained, closed-network infrastructure that runs private AI in-house. Self-hosted language models (LLMs), GPU orchestration, intelligent document processing and agentic retrieval (Agentic RAG) all run on your servers with no external dependencies. It ships as a cryptographically signed, closed bundle: no external repositories, no downloads from the internet at runtime, and the signature is verified before installation. It installs with a single command, is production-ready within hours, and runs any open model you choose: Llama, Mistral, DeepSeek, Qwen, Gemma, Phi and any GGUF-compatible model.
Self-hosted inference
A GPU-optimized, multi-model inference server that runs open-source or private models entirely in-house. Designed for high availability and model isolation.
100+ formats
Processes documents in more than 100 formats with OCR, metadata extraction and smart chunking, turning them into a secure knowledge index.
Cited answers
Produces accurate, auditable answers grounded in institutional knowledge, using multi-step retrieval, hybrid search, citation tracking and hallucination control.
Automatic orchestration
Automatic GPU orchestration with dynamic allocation, failover and efficient model placement across multi-GPU infrastructure.
Hundreds of users
Supports hundreds of concurrent users without performance loss, using distributed queues and workload management.
In-house automation
Build AI workflows and agents with embedded n8n that runs in-house and air-gapped, with no external data leakage.
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Enterprise Workspace: the secure AI interface your team uses every day
The Workspace is a secure interface that replaces cloud AI tools with enterprise search, AI chat, document automation and access control. It all runs on the Core, in-house. Usage is unlimited (no token limits), behavior can be customized per project, and every interaction is recorded in compliance-ready audit logs.
Unified secure workspace
Combines chat, semantic search, document automation and access control in one environment. Real-time collaboration, verifiable cited answers and multi-format file support.
Project-based access
Organize AI activity by team, with isolated knowledge bases and project-level permissions. Role-based access prevents data leaking between projects.
Connects to your systems
Securely indexes documents from SharePoint, file systems, databases and enterprise platforms without moving them to the cloud, and prepares them for RAG.
Document-grounded chat
Cited answers grounded in institutional knowledge. Understands documents, tables and images, and analyzes structured data. All with in-house models.
Natural language over content
Search in natural language across a document's full content, not just its filename. Understands intent, filters by project and source, and returns contextual results.
Audit-ready output
Automates document workflows such as summarization, tender and proposal responses, draft generation and structured data extraction, producing audit-ready output.
1. Technical Approach
2. Compliance Matrix (point by point)
3. Reference Projects
4. Timeline
5. Risk Management
# source: template + 3 winning bidstemplate_v4.docx · 3 winning bids · projectAPI Gateway: OpenAI-compatible, governed integration layer
The API Gateway is a governance layer that safely extends in-house AI. Every request passes through authentication, authorization, model access control, guardrails and rate limiting, all recorded to the audit log; the developer moves fast while security and compliance move with them. The platform follows the OpenAI and Anthropic API standards: to move from cloud to in-house you usually only change the base URL and API key, and your existing tools and environments like LangChain keep working.
In-house AI agents
Production-grade agents that reason over multiple steps and run entirely in-house, working with your in-house models, internal knowledge bases and governed tools.
Automation
Trigger AI from internal systems; multi-step automation with enforced policies, asynchronous processing and full observability.
Connects to internal systems
Connect CRM, databases and enterprise systems without moving data to the cloud. Governed access lets AI run safely on your existing infrastructure.
Token-scoped permissions
Every request passes through authentication, authorization, model access, guardrails and rate limiting, recorded to the audit log. Token-scoped permissions give developers freedom within defined limits.
Application and management APIs
Application layer: OpenAI- and Anthropic-compatible endpoints (chat, streaming, RAG, embeddings, tool use, Model Context Protocol / MCP). Management layer: automation for organization, project, knowledge base and user management.
Ready-made capabilities
CSV numeric analysis, chart generation, natural-language-to-SQL queries, optional web search, and adding your own tools via Model Context Protocol (MCP).
client = OpenAI( base_url="https://ai.company.local/v1", api_key=COMPANY_TOKEN ) # OpenAI-compatible, data stays in-houseOpenAI-compatible endpoint
Where is in-house AI used?
The biggest gain is reclaiming the time employees spend every day reading documents and searching for information. The scenarios below are where on-premise AI is most often used in regulated institutions.
Document Q&A
Ask policies, contracts and procedures questions in natural language and get cited answers. Responses are grounded in the documents; no content is generated outside the sources.
Document and proposal drafting
Produce proposals, reports and drafts in your organization's voice in seconds, keeping them consistent.
Compliance and audit checks
Scan documents against rules and regulations, speed up manual compliance checks and reduce errors.
Contract and loan file analysis
Summarize long contracts and loan files and quickly find risky clauses and gaps.
In-house knowledge assistant
A secure in-house AI assistant that gives employees instant access to institutional knowledge.
Process automation with AI agents
Automate repetitive, operational knowledge work with multi-step in-house AI agents, keeping the output auditable.
AI that connects to your enterprise systems
The on-premise AI platform connects to your data where it already lives, without moving it to the cloud. It safely uses institutional knowledge in your core banking, ERP, CRM, content and database systems.
Core banking
Secure integration with your core banking systems and financial applications, without moving data out.
ERP and CRM systems
Connect the knowledge in your enterprise resource planning and customer relationship systems to in-house AI.
Content and collaboration
Make documents in your SharePoint, Confluence and file systems securely accessible.
Enterprise databases
Work directly with enterprise databases such as SQL Server, Oracle and PostgreSQL, leaving the data in place.
Automation and AI agents
Build multi-step workflows and AI agents with n8n in an air-gapped environment to automate processes.
Standard API
Connect your existing software and development environments to the in-house model via an OpenAI- and Anthropic-compatible API.
Deployment options: on-premise, air-gapped and private cloud
Four options based on your security and regulatory needs. In each, data stays in-house and deployment completes within days.
Private Cloud
Deployment in your own private cloud environment with full isolation.
On-Premise
Runs on your own servers and in your data center with full control.
Air-Gapped
A secure deployment fully isolated from the internet, running on a closed network.
Turnkey
A pre-configured server, ready to run on-site.
Model and GPU hardware: run the model you want on the right hardware
In on-premise AI, the question of which model and which hardware depends on the organization's needs. Innoline makes this choice correctly for you; the platform is built on model independence, not tied to a specific model.
Run model-independent
The platform is not locked to a specific model. It runs Llama, Mistral, Qwen, DeepSeek, Gemma, Phi and any open model compatible with Hugging Face and GGUF, in-house. When a newer, more capable model is released you can migrate to it; there is no vendor lock-in.
Right-sized GPU
Hardware is sized to your number of users, scaling from a single modern GPU server to a multi-node, high-availability cluster.
Pre-configured
Document processing, model parameters and GPU settings arrive pre-optimized. You do not need to be an AI engineer to operate it.
The deployment arrives optimized and ready, but it is not a black box: you tune every parameter, audit every step, and intervene with full root access to your infrastructure whenever needed. So you start fast and stay flexible over the long term.
Cloud AI vs on-premise AI comparison
The core difference between cloud-based AI services, building from scratch, and us.
| Capability | Cloud AI | Build from Scratch | Innoline / On-Premise |
|---|---|---|---|
| Data stays in-house | No | Yes | Yes |
| Data residency | No | Yes | Yes |
| Air-gapped deployment | No | Partial | Yes |
| Full audit and governance | Limited | Yes | Yes |
| Predictable cost | Per token | Variable | Fixed |
| Deployment time | None | 12-18 months | Days |
| Run any model you want | Provider only | Yes | Yes |
| Connects to enterprise systems | Limited | Your own team | Ready connectors |
| Maintenance and updates | Provider | Internal team | Innoline |
AI solutions for regulated industries
We focus on regulated industries where on-premise AI is often not a preference but a compliance necessity.
On-premise AI for banking and finance
Instant answers for front-line staff, policy and contract Q&A, loan file analysis, compliance checks and an internal knowledge assistant. All without data leaving the building, at a predictable cost.
Secure AI for insurance
Policy and procedure analysis, claims file review, fast access to internal knowledge. Sensitive customer data stays in-house.
AI for the public sector
Application and case processing, audit, regulatory research and report generation. Data sovereignty and on-site control come first.
Data security for healthcare
AI support for administrative and procedural documents, record analysis and patient-level data separation. Without sensitive data leaving.
Air-gapped AI for defense
Technical document Q&A in internet-isolated environments, tender and RFP responses, IP protection and project-based isolation. Fully under control.
For critical infrastructure and energy
Secure access to operational and regulatory documents, multi-node and controlled deployment. Runs without interruption.
Compliance-ready by design for GDPR and the EU AI Act
The on-premise platform we build is compliance-ready by design. Every question and document access is logged with an audit trail, authorized through access control, and data sovereignty stays with the organization. The same architecture, with audit trail, access control and data residency, gives you a concrete, demonstrable technical foundation for GDPR, the EU AI Act and local regulatory frameworks.
Frequently asked questions about on-premise AI
How do you deploy on-premise AI?
Is there a GDPR-compliant enterprise AI solution?
Is there an in-house alternative to cloud AI services?
How do you use AI safely with sensitive data?
What is air-gapped AI?
What is on-premise RAG?
Can AI be built to work with our company documents?
Where does the AI store my data?
Why is on-premise AI more secure than cloud solutions?
Can we run the model we want?
How long does on-premise AI take to deploy?
What hardware does on-premise AI require?
Do we need an AI engineer to run it?
Is the cost predictable?
Can the system be updated when new AI models arrive?
We have existing AI tools, will it integrate?
Which file formats can it read?
Is training required to use it?
How do I know the answers are correct?
We deploy on-premise AI end to end
From assessment to support, all six steps are on us, with a fixed scope. You don't have to manage separate vendors and integrators.
Needs and architecture
Which use case, which model, which GPU server. We analyze your organization's needs and existing infrastructure and design the right on-premise architecture.
On-premise and air-gapped deployment
We install the system in your own data center or in an air-gapped environment with no internet, and make it secure and auditable.
Integration and configuration
We connect it to your existing systems (file systems, databases, content tools) and configure the parameters for your organization.
GDPR and EU AI Act compliance
We package audit trail, access control and regulatory documentation, and deliver the deployment regulator-ready.
Right GPU hardware
We size the GPU and server correctly for your number of users and model, and procure it through distribution.
Local support
We stay with you after deployment. Updates, maintenance and support are local, with Istanbul-based Innoline as your single point of contact.
Why Innoline? We deliver responsibility, not a product
You're choosing the partner you'll entrust with critical infrastructure. Four reasons that set us apart in on-premise AI:
A single point of contact
Assessment, deployment, integration, compliance, hardware and support from one place. Istanbul-based, we are the one number to call if something goes wrong.
Regulated-industry experience
We know the expectations, procurement and audit processes of banking, insurance and defense institutions closely.
Compliance and localization expertise
We take on the organization-specific configuration and compliance packaging; the deployment is delivered ready for GDPR and EU AI Act audits.
Honest assessment
We won't sell you a solution that doesn't fit. Not open-ended consulting, but fixed-scope, outcome-focused delivery.
To deploy in-house AI, get in touch
Let's start with an on-premise AI assessment tailored to your organization. We'll talk through your needs, use case and infrastructure together.
Beybi Giz Plaza, Maslak, 34485 Istanbul