01
Memory on your metal
Emails, contracts, SOPs, design archives, and meeting notes stay indexed inside your perimeter. Retrieval is instant — without a round trip to a vendor cloud.
Intelligence · Private AI
Your second brain — local, private, compounding.
Build a sovereign AI layer on hardware you control: models, memory, and agents that run inside your perimeter. No tokens shipped to a vendor cloud, no training on your confidential corpus — just a second brain that gets sharper as your team works.
Local AI
Your second brain — local, private, and compounding.
Public APIs rent you intelligence by the token. Private AI gives your organization a second brain that lives on hardware you control: it remembers your corpus, learns your workflows, and never ships your thinking to someone else's training pipeline.
02
Compounds over time
Every document you approve, every agent run you log, and every correction your team makes enriches the same private index. The system gets sharper; you do not pay again per query for that depth.
03
Works when the internet doesn't
Local inference on your LAN or air-gapped rack. Principals, counsel, and operators keep access during travel, outages, or policy events that block external APIs.
04
You own the model and the data
Open-weight models, your embeddings, your audit trail. No surprise policy change, no training opt-out checkbox — custody is architectural, not contractual.
Rent vs own — at a glance
Rent via API
Pay per seat + per token forever
Own locally
CapEx once, predictable OpEx
Rent via API
Vendor sees your prompts
Own locally
Prompts never leave your VLAN
Rent via API
Knowledge scattered across SaaS tabs
Own locally
One private index for the org
Want the numbers for your team size? Jump to the cost comparison ↓
01
Second brain on your metal
Index contracts, SOPs, matters, and institutional memory once — then query, draft, and reason against it locally. The corpus compounds; you are not metered per retrieval.
02
Air-gapped & on-prem
Models, embeddings, and RAG indexes run inside your environment — your VPC, your data center, or fully air-gapped. Nothing leaves your perimeter, by design, not by checkbox.
03
Tuned on your knowledge
Contracts, SOPs, client files, design archives, regulatory libraries — fine-tuned or retrieval-grounded on the corpus your team actually depends on every day.
04
Hardware to humans
We size and procure the GPUs, deploy the stack, write the integrations, train your operators, and stay on for managed operations if you want us to.
05
Evaluated and governed
Eval suites, guardrails, audit logs, role-based access, and a kill switch. The same controls a regulated industry would demand of any other production system.
Proof
Matter search and drafting prep without sending text to a public API.
Mid-size law firm (anonymized)
Challenge
Associates searched four separate repositories for precedents and client history. Piloting ChatGPT was blocked by ethics and client confidentiality requirements.
Result
Private AI deployed on-prem with RAG over matter files and firm templates. Internal copilot for research prep; all inference stays inside the firm network.
“Partners wanted AI speed with firm-grade custody. We got both — and a kill switch we control.”
— Operator, Mid-size law firm (anonymized)
Research prep time
↓ 41%
External model calls
0
Time to production
11 weeks
Architecture overview
Air-gapped optional — models, indexes, and apps never leave your VLAN.
Interfaces
- Internal chat
- Search
- Workflow hooks
Agent & RAG
- Tool policies
- Embeddings
- Eval harness
Model runtime
- Open-weight LLM
- GPU pool
- Guardrails
Your corpus
- Matters
- Templates
- Regulatory libs
IngestIndexRetrieveGenerateReviewAudit
Economics
Own your AI & data vs rent forever
API bills scale with every enthusiastic user. Owning private infrastructure front-loads cost, then flattens — especially when usage compounds across the firm.
- Starting API spend
- $15,600/mo
- Ownership upfront
- $173,500
- Ownership OpEx
- $3,840/mo
- Crossover
- Owning typically pulls ahead around year 2 at this profile (12% annual API growth assumed).
5-year cumulative spend
$785k saved vs API rental
Year 1Rent $187k · Own $220k
Year 2Rent $397k · Own $266k
Year 3Rent $632k · Own $312k
Year 4Rent $895k · Own $358k
Year 5Rent $1.2M · Own $404k
Rent APIs (grows ~12%/yr)Own stack (upfront + flat OpEx)
| Year | Rental cumulative | Ownership cumulative |
|---|---|---|
| 1 | $187,200 | $219,580 |
| 2 | $396,864 | $265,660 |
| 3 | $631,688 | $311,740 |
| 4 | $894,690 | $357,820 |
| 5 | $1,189,253 | $403,900 |
Illustrative model for planning conversations — not a quote. We size hardware and run your workload profile before any number goes on paper.
Capabilities
What's included.
- Private “second brain” index over your institutional knowledge
- Self-hosted open-weight models, sized to your workload
- Retrieval-augmented generation grounded on your corpus
- Optional fine-tuning and continued pre-training
- Agent frameworks with tools, memory, and approvals
- Internal chat, search, and copilots for your team
- Eval harness, observability, and red-team testing
- GPU procurement and capacity planning
- Managed operations and on-call
Typical engagement
Discovery → use-case shortlist → reference architecture → hardware procurement → install, fine-tune, and integrate → operator training → managed run.
Deployment model
Private infrastructure, matched to your risk profile.
Every Private AI rollout can run on private infrastructure. Choose managed private hosting when you want us to operate it, or an on-site encrypted private server when maximum physical control matters.
Full deployment comparison →Managed private hosting
We deploy and host for you
We run Private AI on private servers, manage access and operations, and keep the platform isolated from shared public infrastructure.
On-site encrypted server
We build it at your location
For maximum security, an engineer builds your encrypted private server on-site, documents the handoff, and leaves your team in physical control.
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