LLM.co · Private AI & Custom AI DevelopmentPrivate AI for regulated industries
Teams that already answer to an auditor, a regulator or a client's confidentiality clause. We design for the review before the demo.
Where private AI matters most.
Legal
Contract review and clause search on the firm's own precedent.
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Healthcare
Chart summaries and prior-auth packets that stay on the network.
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Financial Services
Loan file extraction, KYC review, research on internal data.
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Government & Defense
Air-gapped assistants for controlled environments.
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Manufacturing
Maintenance and quality assistants trained on your manuals.
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Insurance
Claims intake, triage and underwriting memos from the full file.
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Energy & Utilities
Field procedure search and outage reporting at the edge.
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Professional Services
Firm knowledge bases that respect client walls.
Read more →Why teams choose private AI for regulated industries
Regulated teams already know what AI could do for them. The blocker is where the data has to go. A public AI service means client files, patient records, customer accounts or controlled technical data leave your environment and land on a vendor's servers, under the vendor's retention terms. For most firms that answer to a regulator, an auditor or a client contract, that is a long review with an uncertain outcome.
Private AI for regulated industries changes the starting point. The model runs on infrastructure you control: your own servers, your own cloud account, or a network with no route to the internet. Prompts, documents and outputs stay inside the boundary you already secure and audit. Your existing controls for access, logging, retention and change management apply to the AI system the same way they apply to any other internal application.
LLM.co builds these systems on open-weight models such as Llama, Qwen, Mistral and DeepSeek. You own the code, the prompts, the evaluation sets and any fine-tuned weights. Nothing calls a third-party model API unless you decide it may.
What regulated sectors have in common
Law firms, health systems, banks and defense contractors work under different rules, but their AI requirements look similar once you write them down. Each one needs to know who asked what, which records the answer drew on, and whether that person was allowed to see those records in the first place.
- Data stays in place. Models are deployed next to the data, so sensitive records never travel to a model vendor.
- Permissions are inherited. The AI reads access rights from the source system, such as a DMS, an EHR or a file share, and answers only from records the user can already open.
- Every interaction is logged. User, prompt, retrieved passages, model version and answer are written to your SIEM under your retention rules.
- People approve consequential outputs. Drafts, extractions and recommendations go to a reviewer before anything is filed, sent or posted.
- The system is documented. Purpose, data flows, evaluation results and known limits are written up for your compliance, risk or security team.
How to pick a first use case
The best first project is narrow, frequent and easy to check. Good candidates involve reading a large volume of documents that already exist and producing something a person reviews anyway: an issue list, a summary, a set of extracted fields or a search result with citations. Accuracy is measurable against past work, and a reviewer catches errors before they matter.
Avoid starting with anything that makes a final decision about a person, such as a credit approval, a clinical judgment or a legal conclusion. Those uses carry the most regulatory weight and need the most evidence. Build trust on drafting and retrieval first, with an evaluation set drawn from closed matters, historical charts or completed files.
Our custom AI development work starts with a fixed-fee, two-week discovery sprint. We rank candidate use cases, audit the data, model the cost of running on-premises AI or in your cloud account, and write the architecture. You leave with a plan whether or not you build with us.
Private AI by sector
Legal. Law firms and legal departments use private AI for clause search, contract review against the firm's playbook, drafting from precedent and discovery triage. The system connects to iManage or NetDocuments and mirrors matter security and ethical walls, which supports the duty of confidentiality and the duty to understand the technology a lawyer uses.
Healthcare. Clinical and revenue-cycle teams use it for chart summaries, prior-authorization packets, coding support and drafted patient correspondence. Models run inside the network and connect through FHIR and vendor-supported EHR interfaces. The design follows the minimum necessary standard, and a business associate agreement is available.
Financial services. Banks, lenders and asset managers use it for loan file extraction, KYC and AML review, internal research and reporting drafts. Customer data stays where your GLBA safeguards already apply, and each system ships with documentation your model risk management team can review.
Government and defense. Agencies and contractors use it for document search, proposal support, maintenance knowledge and analyst tools inside CUI enclaves and disconnected networks. Deliveries arrive as signed bundles with an SBOM, built to support CMMC, NIST SP 800-171, ITAR and agency authorization programs.
Manufacturing, insurance, energy and professional services teams use the same patterns for maintenance assistants, claims intake, field procedure search and firm knowledge bases.
What private AI does and does not do for compliance
Running AI on your own infrastructure removes one major risk, which is disclosure to an outside model provider. It does not settle every question. You still need policies on who may use the system and for what, review steps for outputs, and records that show the controls work. We build the technical controls and the documentation your program asks for. Certification, attestation and legal judgments remain with your auditors, assessors and counsel.
Asked on every scoping call.
What is private AI?
Private AI means the models, your prompts and your documents all stay on infrastructure you control. That can be servers you own, a dedicated account in your own cloud, or an air-gapped network. Nothing is sent to a third-party model provider unless you decide a specific, non-sensitive request may go there.
Which industries benefit most from private AI?
Teams that handle data they cannot share with a vendor. That includes law firms, health systems, banks and lenders, insurers, government agencies and defense contractors. Manufacturers with valuable designs and utilities with isolated operational networks use it for the same reason. Their data has to stay inside a boundary they control.
Does private AI make us compliant with HIPAA, GLBA or CMMC?
No system makes an organization compliant on its own. We build private AI to support your existing program, with access controls, audit logs, data residency and written documentation of how the system works. Certification, attestation and legal conclusions remain with your auditors, assessors and counsel.
Are open-weight models accurate enough for regulated work?
For extraction, search, summarization and drafting on your own documents, they usually are. We run a bake-off between candidate models against an evaluation set built from your past work, so you see measured accuracy before you commit to hardware or rely on the output.
What is a good first AI project for a regulated team?
Pick a task that is frequent, document-heavy and already reviewed by a person. Contract issue lists, chart summaries, loan file extraction and policy search are common starting points. Avoid first projects that make final decisions about people, such as credit approvals or clinical judgments.
Where does the AI system run?
On-premises in your data center, in a dedicated account in your own cloud, or on an air-gapped network with updates delivered as signed bundles. Many clients start in their own cloud account and move on-premises once usage is steady. We size both options during discovery.
Who owns the system once it is built?
You do. Source code, prompts, evaluation sets, fine-tuned weights and infrastructure code are delivered to your repositories. LLM.co does not charge a platform license, and you can run, change or retire the system without us.
How long does a first project take?
Custom AI development with us starts with a two-week, fixed-fee discovery sprint. A focused first system usually reaches production in eight to twelve weeks after that, depending on integrations, security review and the number of review cycles your team needs.
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.