Opportunities › AI agents for regulated, offline-first teams › Proposed app
Stillroom
Self-hosted AI agents with approvals and audit trails, no internet needed
- Score
- 36
- confidence 57
- Evidence
- 6
- items on the page
- Rivals
- 4
- named in sources
- First version
- 60 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2022
- Emerging market · AI estimate
ScreensConcept
Desktop app: Teams monitor agent workflows, approve steps, and audit logs during sustained monitoring and governance sessions at their desks. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Runs today
Air-gapped42 runs
About this appAI proposal
Stillroom is a self-hosted workspace for running teams of AI agents on your own servers. Models run locally, nothing leaves your network, and the app works in an air-gapped room. Staff open it as an installable web app from your internal address.
Built for finance, healthcare and government teams that were told no to cloud AI. Every agent has a written scope, every tool call is logged, and risky actions wait for a named person to approve them before they run.
Unlike general agent frameworks, governance is the starting point, not an add-on. You get role-based access, tamper-evident logs and exportable reports for auditors, without writing the control layer yourself.
- Tagline
- AI agents that stay in the room, and show their work.
- Voice
- calmprecisesober
- Why this name
- A still room is quiet and closed off: nothing comes in or goes out, and you can see everything inside. That matches offline, auditable AI.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Runs fully offline
Works with local open models on your hardware. No outside calls, no telemetry, and updates arrive as signed packages you load by hand.
No member yet
Multi-agent workflows
Chain agents such as intake, reviewer and summarizer into one flow. Set what each may read, write or call.
No member yet
Approval gates
Mark any step as needing sign-off. The run pauses until an authorized person approves or rejects it from their phone or desk.
No member yet
Tamper-evident audit log
Prompts, outputs, tool calls and approvals are recorded in a hash-chained log you can verify and export.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F4B43Pine · primary#F4F1EAPaper · surface#1B2623Ink · text#D9822BAmber · accent#3E7CB1Slate blue · data
Typography
Aa
Stillroom
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
AI agents that stay in the room, and show their work.
PricingAI proposal
LangChain's pricing page (verified) lists one paid plan at $39 a month, a free plan, and prices on request for larger customers. Team follows that; Business is a starting point to test.
Community
Free
- 1 user
- 2 agents
- Local models only
- Basic run log
Team
$39 a month
- Up to 10 users
- Unlimited agents
- Approval gates
- Hash-chained audit log
- Auditor report export
Business
$149 a month
- Up to 50 users
- Directory sign-in
- Data boundary rules
- Offline license key
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- No loop in the product. Stillroom runs inside customer networks, so use does not reach outsiders. Reach comes from sales, open documentation, sample compliance templates and word of mouth among compliance and IT staff.
- Do they become customers?
- Auditors who read exported reports may ask about the tool, but that is not proven. Test whether auditor and compliance readers of reports ask where it came from and whether their own teams then enquire.
- First customers
- Compliance-led IT teams of 20 to 200 people at community banks and credit unions that have barred cloud AI but want document summarizing and review.
- Where to reach them
- Direct outreach: Contact heads of compliance and IT risk at small banks and credit unions with a short demo of the approval flow and audit log.
- GitHub and docs: Publish a free Community build and an install guide for air-gapped setups to draw self-hosting teams.
- Industry forums: Share sample governance policies in regulated-IT communities and ask for pilot teams.
- First milestone
- Five paid pilots among small financial institutions, each running at least one agent with approval gates in production for 30 days.
Why people already pay for this
- VerifiedGitHub repository gained 9322 stars in 960 days
- Public claimThere is demand for AI agents that work fully offline without external dependencies
- Public claimRegulated teams need AI capabilities that comply with their constraints
- Public claimPrivacy-conscious organizations need AI they can host on their own infrastructure
- AI inference
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Local model runtimeOpen-source local LLM server with an OpenAI-style APIAgents must call models on the customer's own hardware with no outside network.
- This app · Self-hosted packagingContainer bundle with its own Postgres and authCustomers cannot use Vercel or Supabase cloud; the app must run inside their network.
RisksAI inference
- TechnicalSmall local open models may be too weak for reliable multi-step agent work, and customers may lack GPUs to run better ones.
- CommercialRegulated buyers have long procurement cycles and may expect certifications a small team does not yet hold.
- RegulatoryClaims about compliance could be read as guarantees. Rules differ by sector and country, so reports must support audits, not replace them.
- CommercialFree open-source frameworks have drawn many GitHub stars; teams may build the governance layer themselves instead of paying.
- TechnicalSupporting installs in many customer environments, including air-gapped ones, can cost more support time than the price covers.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.