Private AI · On-Prem or Cloud
Your data never leaves your control.
Neither does the AI.
Rakshan AI Solutions builds private LLM deployments for small and mid-sized businesses — on your own servers or an isolated private cloud, whichever fits your workload and budget — so your team gets AI for email, spreadsheets, and internal work without sending confidential data to a third-party API.
YOUR INFRASTRUCTURE
YOUR TEAM
RAKSHAN VAULT
YOUR DATA
PUBLIC AI
In plain terms: your team's everyday AI use — email, spreadsheets, internal docs — is handled by Rakshan Vault, running on infrastructure you control — on-prem or in an isolated private cloud. There's no public AI tool in the loop, so there's nothing to send elsewhere.Note: Vault doesn't block staff from opening a public AI tool directly in another browser tab — that's a separate kind of control (a network/DLP layer), not what a private LLM deployment does on its own.
RISK
Employees paste client emails, contracts, and spreadsheets into public LLMs to save time — with no visibility into where that data goes.
SOLUTION
An LLM deployed entirely within your own infrastructure. No API calls out, no third-party training on your data, no exceptions.
OUTCOME
Your team keeps the productivity gains of AI. Your data keeps the same access controls it already has today.
Solutions
One problem live. More on the way.
Rakshan starts with SMBs — the problem they feel first is data sovereignty. From there, Rakshan expands upmarket into the SIEM and AI agent security layers larger enterprises already run. See what's next on the Products page.
LIVE — ACCEPTING POCs
Private LLM Solutions
Self-hosted LLM deployment for internal use — email drafting, document Q&A, spreadsheet analysis — running entirely inside infrastructure you control, on-prem or in an isolated private cloud. No data egress, no public AI API, no vendor retaining your prompts.
View the product →Product
Rakshan Vault
The first product out of Rakshan — a private LLM your team can actually use day to day.
RAKSHAN VAULT — PRIVATE LLM
AI for your daily work, kept inside your own environment.
Vault installs a capable LLM on your own servers or a private cloud instance — whichever makes sense for your workload and budget — no data ever transits to an external API. It's set up for the work SMB teams actually do: drafting and replying to email, reading and summarizing spreadsheets, answering questions against internal documents.
Email drafting
Spreadsheet analysis
Internal Q&A
Document summarization
- On-prem server — installed on hardware you already run
- Air-gapped — for teams with no external network path at all
- Private cloud (VPC) — your own cloud account, still no third-party model access
Not sure which fits? It usually comes down to workload: heavy, GPU-hungry usage often costs less in a private cloud than buying that hardware outright, while steady, moderate usage is often cheaper long-term on your own servers. We size this with you during the POC.
Use Cases
What it looks like in practice
Early illustrative scenarios — Rakshan is taking on its first POC customers now.
Law firm drafts client email
PROBLEMAssociates pasted case details into a public chatbot to speed up drafting
USEDRakshan Vault, on-prem, connected to internal case files only
OUTCOMESame drafting speed, zero case data leaving the firm's network
ILLUSTRATIVE SCENARIO
Finance team reads a spreadsheet
PROBLEMAnalysts needed AI-assisted summaries of financial data too sensitive for cloud tools
USEDVault's local spreadsheet analysis, no API call ever made
OUTCOMEFaster reporting cycles, data stayed within existing access controls
ILLUSTRATIVE SCENARIO
Manufacturing SMB summarizes reports
PROBLEMNo IT budget for enterprise-scale AI governance, but still wanted AI for internal reports
USEDVault deployed on existing on-site server, no new cloud spend
OUTCOMEAI adoption without adding a new vendor data-sharing risk
ILLUSTRATIVE SCENARIO
Clinic drafts patient visit summaries
PROBLEMStaff pasted patient notes into a public chatbot to speed up after-visit summaries
USEDRakshan Vault, on-prem, connected to the practice's own records only
OUTCOMESame turnaround time, zero PHI leaving the practice's network
ILLUSTRATIVE SCENARIO
Have a workflow that shouldn't leave your control?
Tell us what you're working with — we'll tell you honestly whether Rakshan can help.
Blog
Notes on building AI that stays put
Field notes, guides, and research from building private AI for small and mid-size businesses.
About
AI that stays inside your business
Rakshan AI Solutions builds AI systems for small and mid-size businesses that need the benefits of AI without sending their data somewhere else to get it.
Why I started this
I've spent my career working with the kind of data companies can't afford to lose track of — enterprise logs, the quiet record of everything a system does. That work taught me how much of "AI adoption" right now really means: copy your data, send it to someone else's servers, hope their policies hold up.
This problem doesn't stop at SMBs — large enterprises face the same data-sovereignty question, often with more at stake regulatorily. Rakshan starts with SMBs, where the need is most immediate and least served today, and builds from there as the same problem shows up further upmarket.
For a hospital, a law firm, or an accounting practice, that's not a small ask. Rakshan exists to build the alternative — AI that runs where your data already lives, sized for a business that isn't running its own data center.
I started Rakshan in September 2026, and I'm building it the way I'd want a vendor to build it for me: small working tools first, real conversations with the businesses using them, nothing oversold.
MC
Mahesh Cheruku
Founder · CTO · Engineer · LinkedIn ↗
Who's behind this
A small, hands-on team
No layers between you and the people building this.
SC
Sandhya Cheruku
Director of HR, Marketing & Sales
What we stand for
Secure, Intelligent, Trusted
Three words are easy to put on a logo. Here's what each one actually commits us to.
Secure
Your data stays inside infrastructure you control by default. If something we build can't make that true, we say so up front instead of after the contract.
Intelligent
The AI has to save real time or catch something a person would've missed — not just add a chat window to a process that worked fine without one.
Trusted
You should be able to see what a system did and why, after the fact, without taking our word for it. That's what the logging is for.
How we work
Small tools first, built with real businesses
Rakshan is early and built deliberately slowly. This is the actual sequence, not a sales pitch.
01
Build a small, working tool
Not a slide deck — a narrow, functioning piece of software aimed at one real problem, sized to be finished in weeks, not quarters.
02
Put it in front of a real business
Direct conversations with the SMBs it's built for, before it's polished, to find out what's actually useful and what isn't.
03
Refine it based on what they say
What survives contact with a real workflow shapes the product. What doesn't, gets cut.
04
Grow from what works
Rakshan grows one proven tool at a time rather than launching everything at once.
Have a workflow that shouldn't leave your control?
Tell us what you're working with — we'll tell you honestly whether Rakshan can help.
Contact
Tell us what you're working with
Whether it's a workflow that shouldn't leave your control or just a question about how a private deployment would fit your business — reach out directly.