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Interactive demo · Build & Foundations

In-House AI LLMs

Private LLMs on your data, your perimeter. Watch how we take your private data and perimeter from where it is today to live in production — analyze, assess, build, deploy.

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01
AnalyzeInteractive

We map your private data and perimeter.

Before we build anything, we scan your current surface and surface exactly what is working, what is broken, and where the upside is hiding.

vpc · your documents
Analyzing

Inventorying data sources, access roles, PII…

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  • 1Document storesOpen
  • 2Access rolesper-user / per-role neededGap
  • 3PII handlingmust stay in VPCIssue
  • 4Current LLM usethird-party APIIssue
  • 5Audit / lineagenoneGap
What we found — Valuable internal data — but answering it today means sending it to a third party.

Illustrative scan of a representative current-state surface — your live engagement maps your real data.

02
AssessInteractive

We score the opportunity.

Every move is plotted by impact against effort on your own data, so the first build is the obvious, defensible one — not a guess.

opportunity.dnai
What delivers capability inside your boundary — impact vs. effort.scored on your data
Impact
Low effortHigh effort
Private VPC LLM + RAGimpact 90effort 55

Capability without data ever leaving your perimeter.

The plan — Stand up the VPC model + RAG with per-role access; governance and tuning layer on.100%Data stays inside your perimeter
03
BuildInteractive

We build it on your stack.

Private LLMs on your data, your perimeter — assembled stage by stage over your real tools, tested and shipped to production with a clear owner at every step.

build.dnai
Building on your stack
integration.log
→Ask in your app…
04
DeployInteractive

It runs live.

Here is what your team sees once it is in production — the dashboard, the numbers, and the work moving on its own.

0%
Data that leaves perimeter
0%
Retrieval access controlled
0%
Queries audit-logged
private-rag.dnai
Inside your perimeterAsk
  1. 01Ask in your appQ: What is our refund policy for enterprise?
    Source
    Asked inside your internal chat / your own API
    Channel
    First-party endpoint — no third-party assistant
    Payload
    Question text only; no customer record attached
Step 1 / 5

Illustrative demo data — your build runs a private model in your own VPC, retrieves only from your documents, and logs every query against your roles and audit rules.

Live · What your team sees
›Q: What's our refund policy for enterprise?
›Answer drawn from Policy-v4.pdf, Sec 3
›Sources: 2 docs - your VPC - 0 left perimeter
›Logged: user dana.k - role: support - 14:02

Want In-House AI LLMs running on your network?

Book a 30-minute call — we will analyze your business, scope the build, and come back with a fixed plan and a numbers-anchored target.