Private LLM systems built around your documents, workflows, and access controls. Your data stays under your control — your documents, prompts, and outputs stay in your environment. Strong encryption in transit and at rest, aligned with SOC 2 protocols. First deployment live in about 30 days, full engagements 8–14 weeks.
A custom LLM system is a private, fine-tuned large-language-model deployment hosted inside your own perimeter — on-premises, private cloud, or in a controlled hosted environment — with your documentation as the training corpus and zero data transfer to public model providers. It is the only architecture compatible with HIPAA, CMMC, FedRAMP, and FISMA workloads that involve sensitive business data.
Public AI creates risk when your team is working with sensitive business data. If employees are feeding contracts, customer records, internal documentation, financial data, support history, or operational knowledge into ChatGPT or Claude, your information is leaving your control boundary. That is a privacy problem, a governance problem, and a security problem. Off-the-shelf AI is not built for enterprise data. You need a custom LLM system that runs inside your environment and is built around your documents, workflows, and access controls from day one.
At Autom8ion Lab, we build custom LLM systems for enterprise business data with one clear rule: your data stays under your control. Your documents stay in your environment. Your prompts stay in your environment. Your outputs stay in your environment. We don't bolt generic AI onto critical workflows. We build private AI systems around your business logic, your internal data, and your operating requirements. Our systems are built for teams that need private LLM deployment, secure retrieval architecture, and full control over model access, storage, logging, and integrations. We design environments with strong encryption in transit and at rest, and we align implementation with SOC 2 protocols.
If employees are feeding contracts, customer records, internal documentation, financial data, support history, or operational knowledge into ChatGPT or Claude, your information is leaving your control boundary. That is a privacy problem, a governance problem, and a security problem.
A business AI system is only useful if it understands your documents, your terminology, and your internal rules. Off-the-shelf AI is not built for enterprise data. You need a custom LLM system that runs inside your environment and is built around your documents, workflows, and access controls from day one.
You need private LLM deployment, secure retrieval architecture, and full control over model access, storage, logging, and integrations. We design environments with strong encryption in transit and at rest, and we align implementation with SOC 2 protocols.
For defense contractors, federal agencies, and regulated teams with government-specific requirements, we also support deployments that account for federal security and procurement realities — FedRAMP-aware deployment paths and NIST-focused control alignment when the environment requires them.
The standard for enterprise AI is simple: keep sensitive business data inside your control boundary. That means the model, retrieval layer, document pipeline, and user interface run inside a private environment, not through a public endpoint. When we deploy a private LLM system or private-cloud architecture, your sensitive data stays inside your perimeter. Documents do not get forwarded to public APIs. Prompts do not get routed through third-party AI vendors. Outputs remain governed by your internal controls. We build private inference environments that prioritize data privacy, secure access, auditability, and SOC 2 protocols. You get AI capability without giving up control.
A business AI system is only useful if it understands your documents, your terminology, and your internal rules. We focus on secure document workflows backed by your operational data and business logic. We use your internal documentation, contracts, knowledge bases, compliance records, SOPs, support history, policies, and structured systems to build an LLM workflow that reflects how your business actually runs. Document extraction with context. Classification and routing. Grounded responses anchored to approved internal sources. Business-specific model behavior tuned to your processes instead of forcing your team into a generic chatbot.
A model alone does not solve the real problem. You need security architecture that protects sensitive data and supports enterprise deployment requirements at every layer. Private deployment boundaries — on-premise or private cloud with strict network controls and no public AI dependency. Private LLM hosting inside your controlled environment. SOC 2 protocol alignment across access control, logging, change management, and secure data handling. Role-based access control. Encryption in transit and at rest. Audit logging and traceability for every interaction. Custom API integrations into your source systems without bypassing your controls.
Three deployment patterns most teams evaluate when AI moves into sensitive workflows. Each is the right answer for a different scope.
| Dimension | Autom8ion Lab (private LLM) | Public LLM API wrapper | Generic AI vendor |
|---|---|---|---|
| Data residency | Your environment, your perimeter | Sent to public provider | Vendor-controlled cloud |
| Training set isolation | Your data never trains public models | May be retained for training | Vendor-defined, opaque |
| Fine-tuning on your corpus | Yes — SOPs, policies, prior outputs | Limited or none | Vendor's marketplace, generic |
| Encryption + access control | SOC 2-aligned, in transit + at rest | Provider-controlled | Vendor-controlled |
| Audit logging | Per-prompt, per-output traceability | Provider logs only | Vendor logs only |
| Deployment options | Private cloud, on-prem, GCC High | Public cloud only | Vendor SaaS only |
Your environment, your perimeter
Sent to public provider
Vendor-controlled cloud
Your data never trains public models
May be retained for training
Vendor-defined, opaque
Yes — SOPs, policies, prior outputs
Limited or none
Vendor's marketplace, generic
SOC 2-aligned, in transit + at rest
Provider-controlled
Vendor-controlled
Per-prompt, per-output traceability
Provider logs only
Vendor logs only
Private cloud, on-prem, GCC High
Public cloud only
Vendor SaaS only
This is not a generic chatbot. It is a custom LLM system built for private enterprise operations, controlled environments, and practical execution.
Pull key fields, clauses, requirements, and exceptions from complex files without relying on manual review. Built around your internal documentation, contracts, and operational records so the system understands your terminology, exceptions, and approved formats — not generic templates.
Identify document types, tag sensitive records, and move data into the right downstream workflow automatically. Tied to your access model and approval logic so routing decisions follow your operating rules, with full audit logging on every action.
Anchor outputs to approved internal sources so the system answers based on your environment, not internet guesswork. Critical when handling customer records, contracts, internal documentation, financial data, regulated information, and proprietary workflows.
Tune prompts, retrieval logic, and workflows around your actual processes instead of forcing your team into a generic chatbot. This is the difference between a chatbot and a production system — built for private enterprise operations, controlled environments, and practical execution.
Built for Tech, Construction, Healthcare, and Finance teams that need AI capability without giving up control of their data.
Custom LLM systems for tech companies handling sensitive customer records, internal documentation, contracts, and proprietary operating knowledge. Private deployment inside your perimeter, SOC 2 protocol alignment, RBAC, and full audit logging — so engineering, support, and operations teams get AI capability without leaking data through public endpoints.
Document extraction, classification, and routing for construction operations — contracts, specs, submittals, and operational records. Grounded responses anchored to your project documentation, internal SOPs, and approved sources so the system answers based on your environment, not internet guesswork. Built for real workflows, not demos.
Secure document workflows for healthcare teams handling sensitive records, internal documentation, and compliance artifacts. Private inference environments with encryption in transit and at rest, role-based access, and audit logging for every retrieval event and workflow action — so AI processing stays inside your security boundary.
Custom LLM systems for finance operations handling contracts, financial files, support data, and proprietary operating knowledge. Private LLM hosting inside your controlled environment, SOC 2 protocol alignment, and integrations with your source systems through secure APIs — designed deliberately to keep sensitive business information inside your control boundary.
We build custom API integrations so the LLM connects to your source systems without bypassing your controls. Pair with our AI agent development work when you need agents that execute decisions on top of LLM outputs, our workflow automation when LLM outputs need to drive downstream tasks, our cybersecurity and compliance work for governance support, and our scalable cloud infrastructure when private deployment needs the right foundation underneath it.
Your AI system should not send sensitive business data outside your environment. You need a partner who understands software development, security architecture, private AI deployment, and the operational reality inside growing companies. Instead of forcing public AI into workflows it was never designed to handle, build a custom system that runs securely inside your stack. We deliver custom LLM systems for enterprise business data with private deployment options, encryption in transit and at rest, and practical automation built around your operations.
We do not measure success by demo quality. We measure it by security, speed, and operational impact. Teams that replace public AI with Autom8ion Lab's custom LLM systems gain real control and real results — private data handling for sensitive prompts, documents, and outputs with no external data transfer; 30-day launch timelines for clearly scoped use cases; faster document processing through extraction, classification, retrieval, and summarization workflows; SOC 2 protocol alignment across access control, logging, change management, and secure data handling; and founder-led delivery with senior engineering involvement from scoping through go-live. For defense contractors, federal agencies, and regulated teams with government-specific requirements, we also support deployments that account for federal security and procurement realities. If you need a commercial-first AI system with a path into defense or government requirements, we can build for that from the start.
Yes, and that is the default architecture rather than an upgrade. The model, the retrieval layer, and the document pipeline all sit inside your control boundary, whether that is on-premises, private cloud, or a controlled hosted environment. Prompts, documents, and outputs never reach a public model provider. For CUI specifically, that boundary is what makes NIST 800-171 and CMMC Level 2 achievable. We engineer to those controls and produce the documentation your assessor expects, but we are not an RPO or C3PAO and do not perform the assessment ourselves.
All three can satisfy it. The deciding factors are where your CUI is contractually allowed to live, what cloud footprint you already run, and how much operational burden you want to own. A self-hosted model keeps everything inside your perimeter and gives the simplest data-residency story, at the cost of running the infrastructure. GCC High and GovCloud shift that burden to the provider and inherit their FedRAMP authorizations, but only work if your enclave scoping and identity model are right. We scope this in week one rather than arriving with a preferred answer.
No. Your documents form the retrieval corpus, and where fine-tuning is in scope, a private model artifact that stays in your environment. Nothing is sent to a public provider for training and no third party gains rights to your content. Encryption applies in transit and at rest.
Retrieval is filtered by your existing access model before results reach the model, not after. A query only searches documents that user is already permitted to open, so the model cannot summarise its way around a permission it never had. Access, retrieval, and output events are logged so the path from question to answer is traceable.
It depends on model size, how many people use it at once, and whether you need fine-tuning or inference only. We size it against your real usage during scoping rather than quoting a reference build. Where dedicated hardware is not justified, a private-cloud or controlled hosted deployment gives the same data-boundary guarantees without the capital cost.
If your team is still using public AI to process sensitive business data, you have a security gap. You also have an execution problem. If you need a custom enterprise LLM system, secure private deployment, or a 30-day launch plan for a high-value AI use case, get in touch and let's see if this works for you.
Federal contractor identifiers: UEI YY2DR3KSENH7 · CAGE 9YCS7 · SDVOSB Pending
Stop doing manual work that could be automated. Let's build something custom that actually fits how your business works. AI automation, workflows, LLM systems, whatever you need.
We'll build a system that's secure and scales as you grow. From AI agents to cloud infrastructure, everything adapts as your business expands.
Agents that execute multi-step work inside your environment, under your access model.
Learn moreCMMC 2.0, NIST 800-171, HIPAA and SOC 2 controls implemented with the evidence trail.
Learn moreHow a compliance-first engineering firm differs from a general AI practice on regulated work.
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