Private AI that fits your workload, not just your hardware.
We help teams evaluate, deploy and operate AI on their own devices or infrastructure, with measured quality, performance and cost. Sometimes the right answer is to stay hosted; we'll tell you.
Start with the problem you have
Each route ends in a diagnostic that fits it, not a generic contact form.
Keep sensitive documents inside approved infrastructure
A permission-aware document assistant with a verified data boundary.
Private knowledge →Reduce the cost of an existing inference workload
Hosted vs self-hosted, compared at the same quality, with operations included.
Cost comparison →Make a local AI prototype production-ready
Measured load, the right serving stack, access controls and an owner.
Deployment rescue →Add offline AI to a product
On-device and offline features within memory, latency and distribution limits.
Describe the product →Choose hardware using a tested workload
Size for model, context and concurrency, not headcount, before you buy.
Sizing calculator →When local fits, and when it doesn't
Running a model locally is one way to meet a requirement, not the requirement itself. These benefits hold only under the stated conditions.
Data that must stay in your boundary
Prompts and documents can stay on hardware you control, provided every component, including extraction, embeddings, logging and updates, is configured local-only and verified.
High, steady inference volume
No per-token fees. Hardware, power and admin time remain, so savings depend on volume and on whether a smaller open model passes your quality bar.
Offline or poor connectivity
Works offline once models and dependencies are installed, as long as no component quietly calls out.
Control over models and upgrades
You choose the model, quantisation and upgrade timing, and can fine-tune on your own data.
- Volume is low or bursty, so hardware would sit idle
- Only frontier-model quality passes your tests
- Nobody can own updates, incidents and regression testing
- Your approved provider already meets your contractual and residency requirements
Be precise about where processing happens
On-device
Processing happens on the user's own computer or phone. Nothing is shared between users unless you build that in.
On-premises
Processing happens on infrastructure your organisation owns and operates, in your building or your colocation space.
Private hosted or hybrid
Explicitly approved external infrastructure, or a defined mix, with every external flow named in writing.
Evidence before commitment
Each step is independently useful. A legitimate result of an assessment is "stay where you are".
Private AI Fit Assessment
Find out whether, and how, a specific AI workload can run within your constraints, before you buy hardware or sign an implementation contract.
From $1,500Typically 1–2 weeks STEP 02Workload Proof Pilot
A bounded implementation of one workload, tested against examples and acceptance criteria you agree up front.
From $8,000Typically 3–6 weeks STEP 03Production Deployment
A hardened deployment with integrations, access controls, operational documentation, training and acceptance testing.
From $20,000Typically 6–12 weeks STEP 04Managed Private AI
Defined monitoring, maintenance, regression testing, incident handling and upgrade support for an accepted deployment.
From $750/monthRolling, with a minimum term agreed in the scopeDipankar Sarkar · Engagement owner, Neul Labs Limited
One named person is accountable for scope, commitments and acceptance on every engagement, including work delivered with specialist partners.
Learn and build it yourself
Free, independent guides and a directory of the local AI ecosystem, including tools we don't build.
Your First Local AI in 5 Minutes
Install Ollama and start chatting with an AI model in under 5 minutes.
IntermediateChoose the Right Local LLM
A decision framework for picking a model for your hardware and use case.
IntermediateBuild a Local RAG Chatbot
Chat with your documents using embeddings, vector search and local models.
IntermediateLocal AI Code Assistant
Set up a self-hosted Copilot alternative with Continue and Ollama.
IntermediateLocal AI Hardware Guide
What hardware you need, from laptops to multi-GPU servers.
AdvancedEnterprise Local AI Deployment
Architecture, security and cost for deploying LLMs across an organisation.
Explore the local AI ecosystem
From inference engines to fine-tuning frameworks, find the right tool for the job.
Inference Engines
Run AI models on your hardware
Desktop Apps
Chat with AI on your computer
Web Interfaces
Self-hosted ChatGPT alternatives
Developer Frameworks
Build apps with local AI
Mobile AI
On-device AI for phones
Fine-Tuning
Train your own models
Built by Cognisoc
Open-source tools our team builds and maintains. We test them on the same terms as everyone else's, and never require them in an engagement. Disclosure policy.
Mullama
Local LLM inference engine with bindings for Python, Node.js, Go, Rust, PHP, and C/C++. Ollama-compatible CLI with embedded mode.
Learn more → Mobile AILlamafu
Flutter plugin for on-device AI on Android and iOS. Text generation, chat, vision, function calling, and structured JSON output.
Learn more → EducationalZigLLM
Learn transformer internals from first principles. Model architectures implemented in Zig with an extensive test suite.
Learn more →Frequently Asked Questions
What is a local LLM?
A local LLM is a language model that runs on hardware you control (a laptop, phone, workstation or server) instead of a provider's cloud. Inference can then happen without sending prompts to a third party, with no per-token fees, and offline once the model is downloaded.
Is my data private when using local AI?
It can be. Inference is local when you use a downloaded model, but privacy depends on the whole system: some apps send telemetry or offer optional cloud models and web search, and document pipelines can call external services for OCR or embeddings. Check each component and turn off cloud features to keep everything local.
Is local AI cheaper than an API?
Sometimes. With high, steady volume and a smaller open model that meets your quality bar, self-hosting can pay back within months. At low volume, or against today's low prices for small hosted models, it may never break even once hardware, power and admin time are counted. Our cost calculator shows both cash flow and annualised cost.
Does running AI locally make us GDPR or HIPAA compliant?
Not by itself. Keeping processing in-house can remove a third-party transfer and simplify compliance work, but accountability, lawful basis, security controls and individual rights still apply. Treat local deployment as one control within your compliance programme.
Can I run AI locally without a GPU?
Yes. Tools like Ollama and llama.cpp support CPU-only inference. Smaller models (1B–8B parameters) at 4-bit quantisation run on modern CPUs with 8–16 GB RAM, though more slowly than on a GPU.
What is the easiest way to try a local LLM?
Install Ollama, then run "ollama run llama3.2" in a terminal. You can be chatting with a local model in about five minutes. For a graphical interface, try LM Studio or Open WebUI.