AI Infrastructure
Own your
intelligence.
Lunvex Labs builds bespoke AI infrastructure: fine-tuned open models, custom server and rack design, and hardware tuned to your workload. Own your models and your compute.
Designed, built, racked, and tuned to the workload.
Flagship build
Private AI for law firms.
Solicitors cannot send privileged client material to a public chatbot. We build a private legal AI that lives inside the firm: it reads contracts, searches your case archives, and drafts the first version of routine documents, with nothing ever leaving your network. One repeatable build, tuned to your practice.
On-premises · Privilege-safe · Tuned to your matters
What the build includes
Legal- Fine-tuned open model trained on legal language, deployed on a box inside the firm
- Contract and clause review with risk flagging
- Precedent and matter search across your own archives
- Drafting support for letters, summaries and attendance notes
- Air-gapped option so privileged data never leaves the building
- Onboarding, hardware, and ongoing tuning handled by us
What we build
End-to-end AI infrastructure, yours to own.
From model selection through racking hardware and running inference in production. Every layer designed for the team that cannot afford a black box.
In-house AI and Llama Builds
We design and build model stacks from the ground up. Llama-family and other open-weight models, configured and deployed on infrastructure you control.
Fine-tuning and Self-hosting
Task-specific fine-tuning on your data. Weights stay yours. Models run in your environment: on-prem, co-located, or air-gapped.
Server and Rack Design
Custom server architecture designed to the compute profile of your workloads. Rack layout, thermal planning, and component selection handled end to end.
Custom Hardware for Efficiency
Compute matched to inference and training patterns. Hardware selected and configured for performance-per-watt. Cost efficiency improves over time at scale.
Deployment and Ops
We stand up your stack, configure inference endpoints, monitoring, and failover. Ongoing support to keep models sharp and hardware healthy.
Other industries
The same architecture, any private workflow.
The legal build is our flagship, but the underlying stack of a tuned open model on hardware you own applies anywhere private data meets a repetitive knowledge task. These are representative examples, scoped individually. No fabricated results.
Quoting, scheduling, and job tracking
An AI system built for builders, electricians, and contractors that drafts quotes from scope notes, schedules jobs across crews, and updates job records as work progresses. Runs on a compact local box, accessible from site via a simple interface.
- Quote generation from rough job descriptions
- Crew scheduling and job calendar management
- Invoice drafting from completed job notes
- Works offline on-site, syncs when connected
Tutoring, marking support, and admin automation
A safeguarding-aware school AI that helps teaching staff with differentiated resource creation, provides structured marking feedback, and automates routine admin such as report drafting and parent communication templates. Student data stays on-site.
- Marking assistance with structured written feedback
- Lesson resource and differentiation support
- Report and parent communication drafts
- Safeguarding-aware: no student data leaves the school
Appointment triage, notes, and patient comms
A private-by-design assistant for GP practices, physio clinics, and dental surgeries that handles appointment triage questions, generates structured consultation notes from audio, and drafts referral letters. Patient data never leaves the practice server.
- Consultation note generation from voice input
- Referral and letter drafting for clinicians
- Appointment triage and FAQ handling
- Fully on-prem: meets clinical data governance requirements
Transaction categorisation and reporting
A fine-tuned model that reads bank exports, categorises transactions, identifies anomalies, and produces draft management accounts. Deployed inside the accountancy firm so client financial data stays under one roof.
- Automated transaction categorisation and coding
- Anomaly and duplicate detection in ledgers
- Draft P&L and management account narrative
- On-prem deployment: client data stays with the firm
These are illustrative configurations, not client case studies. Every engagement is scoped individually. If your industry is not listed, get in touch: the approach applies to any workflow with private data and a repetitive knowledge task.
Hardware builds
Built to spec. Configured to need.
We design and assemble each machine around your workload. The tiers below show representative configurations. Your build will be specified after a scoping conversation. No fixed catalogue. No prices on this page.
Single-operator AI workstation
A high-throughput desktop build for teams running local inference on medium-context models. Suited to professional firms that need a capable private AI without a full server room.
Example config. Quoted per requirement.
Multi-GPU inference server
A rackmount server designed for sustained, high-volume inference or parallel fine-tuning. Configured for teams running 24/7 workloads across multiple users or processes.
Example config. Quoted per requirement.
On-site inference appliance
A small-form-factor, low-power inference box for deployments where rack space or power budget is limited. Runs quantised models efficiently at the edge: on a shop floor, in a clinic, or on a remote site.
Example config. Quoted per requirement.
- Every component is selected against the actual inference workload: batch size, context length, latency budget, and daily request volume.
- Specs listed here are example configurations, not a fixed catalogue. Your build is scoped from your requirements.
- No prices on this page. Every build is quoted per requirement after a scoping call.
- UK-assembled and tested in-house before delivery or racking. Not drop-shipped from a supplier.
Ready to scope a build? Tell us the workload and we will design the stack.
Why owned compute
Renting is a tax on every token.
API access trades control for convenience. For teams running meaningful inference volume, owning the stack pays back quickly and compounds from there.
On-prem or co-located
Deployment flexibility
Run inside your own data center or in a trusted colocation facility.
Open models, owned weights
No black box
Llama and open-weight models you can inspect, fork, and retrain.
Hardware tuned to workload
Built for the job
GPU, CPU, and memory configurations matched to your inference profile.
Cost improves with scale
Economics compound
Fixed infrastructure amortizes over time. Per-inference cost falls as volume grows.
Model ownership
Weights belong to the provider. No insight into what runs.
Full model weights, on your storage, under your control.
Cost curve
Fees scale with every token. Costs rise with usage.
Fixed hardware investment. Unit cost drops as scale increases.
Data privacy
Prompts and completions cross third-party infrastructure.
Data never leaves your environment. Air-gap possible.
Latency
Shared network paths and rate limits affect throughput.
Local inference on dedicated hardware. Predictable performance.
Vendor dependency
Pricing, availability, and model changes are out of your hands.
No lock-in. Switch models, adjust hardware, evolve freely.
The process
Five steps from brief to live.
A build is only as good as the thinking behind it. We go deep on requirements before touching a rack.
Scope the workload
We start with a deep conversation about your use case: inference volume, latency targets, data sensitivity, team size, and budget. The workload defines the build.
Design the stack
Model selection, hardware configuration, and software architecture designed together. Every component justified against your specific requirements.
Build and rack
Servers assembled, racked, cabled, and tested. Thermal and power validated before anything goes live. Built in-house, not drop-shipped.
Tune for efficiency
Model quantization, inference optimization, and hardware tuning to hit performance targets. We measure before and after, not just after.
Deploy and support
Stack goes live with monitoring, alerting, and runbooks in place. Ongoing support to evolve models as your needs change.
Let's build your stack
Intelligence designed to your spec.
Tell us about your workload. We will scope the model stack, design the hardware, and build the infrastructure you need. No vendor lock-in. No rented intelligence.
hello@lunvexlabs.com