Practice ASoftware
Custom AI Development
Agents, copilots, search and extraction built around one real workflow in your business, measured against an evaluation set we write with your team.
Explore custom AI →LLM.co · Private LLM
Private LLM & Custom AI Development
AI that never leaves the building.
LLM.co builds private LLMs and the custom AI systems that run on them, on infrastructure you control: your rack, your cloud account, or a network with no way out. Your data stays home.
Reel 01 · IBM & SAGE computer rooms, 1956–5700:00:00:00
Two practices, one team
We build it. Then we run it where you say.
Most firms do one half. Consultancies build on public APIs and leave. Infrastructure vendors sell boxes with nothing useful on them. LLM.co does the software and the deployment, so the system that passes the demo is the one that passes the security review.
Practice BInfrastructure
Private AI
A private LLM on open-weight models, served on hardware you own or a cloud tenancy you control, with access control and audit logs your compliance team already understands.
- On-PremisesPRM
- Private CloudVPC
- Air-GappedGAP
- GovernanceGOV
What is a private LLM?
A private LLM is a large language model that runs on infrastructure you control. The model weights, your prompts, your documents and the logs stay on your own servers, in your own cloud account or on an air-gapped network. Nothing is sent to an outside model provider, and nothing is used to train someone else's model.
LLM.co builds every private LLM on open-weight models such as Llama, Qwen, Mistral and gpt-oss. You hold a copy of the weights, you choose when to upgrade, and the model keeps working if a vendor changes its terms, its prices or its catalog.
Private LLM development and custom AI, under one team
A private LLM on your own hardware is only useful once it is connected to your data and your workflow. Custom AI development is the work of building that system around one specific job in your business. It reads your records, follows your rules and writes its output into the systems your staff already use, such as the ERP, CRM, claims platform or document management system.
A custom system built on a public API often fails the security review before launch. A private LLM with nothing built on it sits idle. Doing both in one engagement avoids each problem: the system that wins the demo is the same private LLM deployment that clears your security review.
Who needs a private LLM
A private LLM suits organizations whose most useful data is also their most sensitive. That includes law firms and legal departments, healthcare providers, banks and insurers, government and defense contractors, manufacturers, utilities and professional services firms. It also suits any company whose contracts or policies forbid sending client data to a third party.
It makes financial sense once usage is broad and steady. Per-seat and per-token fees grow with every user. A private LLM on dedicated hardware has a fixed cost that does not.
Where your private LLM can run
The same private LLM deployment ships to three places. On-premises, it runs on GPU servers in your own data center. In your cloud account, it runs on dedicated GPUs in AWS, Azure or GCP under your keys and network rules. Air-gapped, it runs with no internet route at all and takes updates on signed media.
Every private LLM we deploy ships with single sign-on, document-level permissions and audit logging to your SIEM before wide release, so private AI fits the controls your security team already runs.
How LLM.co works with you
Engagements start with a two-week discovery sprint to rank use cases, check the data and model the cost of a private LLM against your current API spend. A focused first system usually reaches production in eight to twelve weeks. Every phase ends with a go / no-go decision, and you own the code, prompts, evaluation sets and any trained weights.
Reel 02 · SAGE air-defense console, 1956Every console in the room answered to the people in it.
What we build
Six kinds of system. All of them yours.
You own the code, the prompts, the evaluation sets and any weights we train. No platform fee, no per-seat license, no lock-in to us.
AGTAgents
AI agents & automation
Multi-step agents that read, decide and act inside your systems, with a human approval step wherever you want one.
e.g. AP agent matches invoices to POs in the ERP and flags exceptions
RAGSearch
Knowledge & search
Ask questions across contracts, SOPs, tickets and drives. Every answer cites the source page and respects document permissions.
e.g. a policy library searchable by plain-English question
APPApps
Custom LLM applications
Purpose-built copilots and internal tools with the UI, permissions and data connections one team actually needs.
e.g. underwriting copilot that drafts the memo from the file
FTModels
Fine-tuning & training
Open-weight models adapted to your terminology, formats and tone, then distilled to run on smaller hardware.
e.g. a small model tuned on years of engineering change orders
DOCVision
Document & vision AI
Extraction, classification and checking for scans, handwriting, forms, drawings and photos, with confidence scores per field.
e.g. intake packets to structured records
INTSystems
AI integrations
Connectors into the systems of record you already run, so AI output lands where work happens instead of in a chat window.
e.g. NetSuite · Salesforce · Epic · iManage · SharePoint · Snowflake
Why private
Computing used to live in the building.
In 1958 nobody shipped NASA's data to someone else's computer. The machine was in the room. Public AI reversed that: prompts, files and customer records now travel to a vendor's GPUs. A private LLM puts the computer back where your data is.
1958
FIG. 01 IBM 704, NASA Ames
The computer is a room you can walk into. Every byte stays inside it.
2023
FIG. 07 Shared GPU fleet
AI arrives as an API. Your data leaves to reach it, and you rent the model by the token.
Now
FIG. 08 Your rack
Open-weight models handle most business work well. The computer comes back inside.
Same rule as the mainframe era: the data stays with the people responsible for it.
See where it can run →Process
From scoping call to running in your rack.
Fixed-scope phases with a go / no-go at the end of each one. You can stop after any phase and keep everything built so far. How we work
012 wks
Scope
Pick the workflow worth automating and prove the numbers.
- Use-case ranking
- Data audit
- Cost model
023–4 wks
Prototype
A working build on your real data, scored against an eval set.
- Eval set v1
- Model bake-off
- Pilot UI
034–6 wks
Harden
Make it safe to give to hundreds of people and an auditor.
- Red-team
- SSO + RBAC
- Audit logging
041–2 wks
Deploy
Installed on your hardware or in your cloud account.
- GPU sizing
- Runbooks
- Handover
05Ongoing
Operate
Monitoring, model upgrades and retraining as the work changes.
- Drift alerts
- Model swaps
- Quarterly evals
Public API vs. private LLM
Know where your prompts go.
Public model APIs are a fine place to experiment. For regulated data, client files and anything you would not email to a stranger, here is what changes.
| Public AI API | LLM.co private LLM | |
|---|---|---|
| Where prompts go | The vendor's servers | Your network. Nowhere else. |
| Who holds the weights | The vendor | You. Copied to your storage. |
| Model choice | The vendor's catalog, changed on their schedule | Any open-weight model, pinned until you upgrade |
| Cost shape | Per token, rises with every new user | Fixed hardware or reserved GPU, flat at scale |
| Audit trail | The vendor's logs, on request | Every call in your SIEM, retained by your policy |
| Works offline | No | Yes. Air-gap ready. |
Deployment models
Pick the room it runs in.
The same private LLM ships to any of these targets. Start in your cloud account, move on-prem when volume justifies the hardware.
FIG. 04 Rack aisleRaised floor, cold aisle
Models we deploy
LlamaQwenMistralDeepSeekGemmagpt-ossPhiWhisper
Hardware we size for
NVIDIA H200H100L40SRTX 6000AMD MI300XApple Silicon
Industries
For teams with something to protect.
A private LLM matters most where data is regulated, privileged or simply too valuable to hand over. That is most of our work.
Privilege · Ethical walls · Client confidentiality
Legal
Contract review and clause search on the firm's own precedent.
HIPAA · PHI · BAAHealthcare
Chart summaries and prior-auth packets that stay on the network.
GLBA · SOX · FINRAFinancial Services
Loan file extraction, KYC review, research on internal data.
CMMC · ITAR · FedRAMPGovernment & Defense
Air-gapped assistants for controlled environments.
Trade secrets · OT · ITARManufacturing
Maintenance and quality assistants trained on your manuals.
NAIC · State DOI · PIIInsurance
Claims intake, triage and underwriting memos from the full file.
NERC CIP · OT · Critical infrastructureEnergy & Utilities
Field procedure search and outage reporting at the edge.
Client NDAs · Independence · ConfidentialityProfessional Services
Firm knowledge bases that respect client walls.
Questions
Asked on every scoping call.
What is a private LLM?
A private LLM is a large language model that runs entirely on infrastructure you control: your own servers, your own cloud account or an air-gapped network. Your prompts and documents never reach a third-party model provider, and no outside company can use your data to train its models.
How is a private LLM different from ChatGPT or a public AI API?
With a public API, every prompt and file travels to the vendor's servers and you rent the model by the token. With a private LLM, the model runs inside your network, you hold the weights, every call is logged to your own SIEM, and the cost is fixed hardware or reserved GPUs instead of per-token fees.
What is custom AI development?
It is the design and build of an AI system for one specific workflow in your business, running on your private LLM. Examples include agents that act inside your ERP, search across your contracts and policies, and models fine-tuned on your own records. Each system is scored against an evaluation set built from your real past cases.
Are open-weight models good enough for a private LLM?
For most business work, yes. Extraction, search, summarization, drafting and agent tasks on your own data run well on current open-weight models. We run a bake-off against your evaluation set in the Prototype phase, so you see the scores for each candidate before you commit to hardware.
Do we have to buy GPUs to run a private LLM?
No. Most clients start with a private LLM on dedicated GPUs in their own cloud account and move on-premises once usage is steady. During discovery we size both options and show the month when owned hardware breaks even against cloud rental.
Who owns what you build?
You do. Source code, prompts, evaluation sets, fine-tuned weights and infrastructure code are delivered to your repositories. There is no platform license and no per-seat fee. If you stop working with us after any phase, you keep everything built up to that point.
What does a first project cost?
The discovery sprint is a fixed fee. Builds are quoted one phase at a time after discovery, so you never sign for more than the next step. Cost depends mostly on the number of integrations, the condition of your data, your approval and audit requirements, and where the private LLM will run.
How long until a private LLM is in production?
The discovery sprint takes two weeks. A focused first system usually reaches production in eight to twelve weeks after that. The build runs as Prototype in three to four weeks, Harden in four to six, and Deploy in one to two. Integrations and internal review cycles affect where a project lands.
Is a private LLM secure enough for regulated data?
It is built to fit your existing controls. Users sign in through your identity provider, permissions mirror your source systems, data stays on infrastructure you control and every action is logged to your SIEM. We document data flows so your private LLM can support your HIPAA, GLBA, SOC 2 or CMMC program.
Start here
Submit a job card.
Tell us the workflow and where the data lives. An engineer, not a salesperson, replies within one business day with a first take on architecture and cost.