00/digital solutions
Privacy-First Local AI
01/quick answer
What you get
On-Prem Inference Stack
Local model hosting, hardware planning, access control, and the setup needed for staff tools or internal apps to use it safely.
Private Retrieval Layer
Private document search so the AI answers from your own files, policies, manuals, or records instead of public sources.
Secure Network Design
LAN-only or tightly controlled access, internal Wi-Fi or VPN rules, and clear boundaries around who can reach what.
Price guide
AED 35,000 to 140,000+
Each tier says what puts a project in it. Prices are in AED; clients in Saudi Arabia pay the same figure in SAR.
Private AI pilot
AED 35,000 to 70,000. A local assistant, private retrieval and controlled staff access.
Private AI team deployment
AED 70,000 to 140,000. Stronger permissions, larger data, monitoring and several workflows.
Secure AI infrastructure
from AED 140,000. GPU planning, air-gapped or segmented networks and enterprise governance.
Hardware, cloud GPU, and ongoing model operations are quoted separately when needed.
02/fit
Good fit when
- Organizations that cannot send sensitive documents or prompts to public APIs
- Teams that need predictable in-house AI usage without per-request external dependency
- Environments that require LAN-only, air-gapped, or tightly segmented deployments
03/deliverables
What we build
On-Prem Inference Stack
Local model hosting, hardware planning, access control, and the setup needed for staff tools or internal apps to use it safely.
Private Retrieval Layer
Private document search so the AI answers from your own files, policies, manuals, or records instead of public sources.
Secure Network Design
LAN-only or tightly controlled access, internal Wi-Fi or VPN rules, and clear boundaries around who can reach what.
Operational Governance
Rules for model choice, logging, retention, permissions, updates, and review so the system stays maintainable and compliant.
04/method
How we approach it
Classify The Data Boundary
We start by separating what is public, internal, restricted, and highly sensitive so the setup matches the real risk.
Choose The Right Runtime
Small teams may only need a lightweight local server. Larger teams may need dedicated GPUs, queues, and stronger access control.
Ground Answers In Local Sources
We connect the AI to approved internal sources so answers come from your documents, and only the right people can reach the right collections.
Harden The System
Authentication, secrets, network restrictions, and audit visibility are treated as core requirements from day one.
05/stack
Tools we use
06/use cases
Common use cases
Air-Gapped Knowledge Assistant
An internal assistant for policy, legal, or case files running on a local server with wired or private access points and no internet path for document traffic.
Private Developer Copilot
Local code generation, repository Q&A, and structured internal tooling without exposing source code to external providers.
OpenClaw Business Assistant
A self-hosted chat gateway for internal teams that can route messages to local models, approved tools, and business automations inside the company boundary.
Sensitive Document Desk
OCR, extraction, summarization, and translation for contracts, medical files, or government paperwork inside the organization boundary.
07/setup
How it is set up
Edge Or LAN Client Layer
Browser, desktop, kiosk, or mobile clients connect only to approved internal endpoints or private Wi-Fi / access points.
Model Serving Layer
Ollama is useful for lightweight local deployments, while vLLM is a strong fit for higher-throughput OpenAI-compatible serving. llama.cpp covers highly portable CPU or edge scenarios.
Retrieval & Memory Layer
Embeddings, vector search, and metadata filters keep retrieval tied to approved sources and user permissions.
Security & Audit Layer
SSO, VPN segmentation, encrypted storage, request logging, sandboxed agent permissions, and controlled update paths keep the deployment reviewable and maintainable.
08/models
Model options
Qwen 3 / Qwen 3 Coder
Good fit when
Strong multilingual assistants, agent workflows, code reasoning, and Arabic-heavy internal deployments.
Ollama currently exposes Qwen 3 in sizes from sub-1B to 235B and highlights tool use plus multilingual support; Qwen 3 Coder adds a dedicated coding track for local developer workflows.
Llama 3.1
Good fit when
General-purpose private assistants, RAG interfaces, and multilingual enterprise chat with broad ecosystem support.
Meta's Llama 3.1 line ships in 8B, 70B, and 405B variants with a 128K context window, making it a strong baseline for on-prem assistants when the license fits the use case.
Gemma 3
Good fit when
Single-GPU or workstation deployments that need efficient reasoning, long context, and optional vision support.
Google positions Gemma 3 as an open model family that can run on a single accelerator, with 128K context and support for 140+ languages.
Phi-3.5 Mini
Good fit when
Resource-constrained or latency-sensitive environments where a smaller local model is still useful.
Microsoft's Phi-3.5 Mini is designed for constrained environments and supports a 128K context window, which makes it a practical edge option.
Nomic Embed
Good fit when
Private embedding generation for local RAG, semantic search, and document retrieval.
Nomic publishes a reproducible open embedding model; Ollama exposes it as a local embedding endpoint, and Nomic reports strong short- and long-context retrieval performance.
09/faq
Frequently asked questions
Can a private AI system run without sending prompts or files to the internet?+
Yes. A local or on-prem setup can be designed so inference, retrieval, and storage stay entirely inside your own infrastructure. That may mean a workstation, an internal GPU server, a private VLAN, or even a more tightly controlled air-gapped environment depending on sensitivity.
What open-source models can be deployed locally today?+
Current practical options include families like Qwen 3, Llama 3.1, Gemma 3, Phi-3.5, and code-oriented variants such as Qwen 3 Coder. The right choice depends on whether the priority is multilingual support, coding, latency, hardware limits, or structured tool use.
Is local AI automatically secure just because it is on-prem?+
No. Local deployment improves control, but security still depends on network design, access control, logging, update policy, secrets handling, and how documents move through the system. We treat privacy and system hardening as separate design decisions.
What hardware do we need for a privacy-first local AI deployment?+
That depends on throughput, model size, context length, and how many people will use the system at once. Smaller pilots can run on a single capable workstation or server, while higher-concurrency deployments may need dedicated GPUs, queueing, and a more formal serving layer.
Can you implement OpenClaw inside a business environment?+
Yes, when it fits the operating model. OpenClaw can be used as a self-hosted chat and agent gateway, but in a business rollout we scope permissions, workspace boundaries, tool access, logging, and network placement carefully so it behaves like controlled internal infrastructure rather than an unsupervised personal assistant.
What does a private RAG stack usually include?+
A private RAG stack usually includes document ingestion, chunking, embeddings, a vector index, metadata and access controls, a retriever layer, and a local or on-prem model that answers only from approved internal sources. We also design permissions so users only retrieve the documents they are allowed to see.
Can a private AI setup mix local models, internal automations, and business chat interfaces?+
Yes. A common pattern is a local inference server paired with private retrieval, approved workflow automation, and an internal chat or portal interface. That can support helpdesk, developer, legal, operations, or executive use cases while keeping the data boundary under your control.
The freebie
AI Setup Session
60 minutes, one to one, online or in Dubai
Pick one job you do by hand every week. In the hour, we build the AI agent that does it, in your own tools, and you keep it.
FOR JOBS LIKE
- Quotes you retype for every enquiry
- WhatsApp enquiries answered after hours
- Chasing invoices and payment reminders
- Following up leads you never get back to
- The weekly report nobody wants to write
What you leave with
- 01One agent, working, running in your own account
- 02Yours after the call, logins included, depending on nothing of ours
- 03A one page list of the exact tools, named, with what each one costs you monthly
OUR GUARANTEE
If the agent is not running by the end of the hour, we book a second one and keep going until it is. Still free, still no obligation.
For owners and operators of a business that is already running.