Our approach
Adoption first. Sovereignty throughout.
tabbr gets every department doing real work with AI, with ready apps instead of a blank prompt. From day one, three things stay under your control: the models you use, the record of your AI work, and what your AI learns.
Three kinds of sovereignty
Sovereignty is not only where servers sit. It is who decides which models run, who owns the work they produce, and who benefits when the AI improves.
Model sovereignty
Choose and switch models per task, under one policy
Threads work on every frontier model, and people can switch models mid-conversation without re-explaining. IT routes each department or app to the models it approves, including open models of your choice. Cost is shown before and after a run.
tabbr server runs the apps and enforces your rules. Use it as a service, or deploy it in your own cloud for regulated work.
Record sovereignty
Your organization owns a durable, searchable record of its AI work
tabbr keeps every turn, version and decision. What a thread produces becomes a page with a permanent address, full version history and recorded origin, so anyone with access can pick up the work.
Every run leaves a record your company keeps and can export. The audit trail covers every publish, policy change, approval and run. Enterprise adds retention and legal hold.
Learning sovereignty
Your own feedback improves your AI, privately
tabbr improves from the work your teams accepted: the drafts people approve, correct or reject. That learning stays inside your company only. People data stays out.
Your data trains no outside model, on any tier, by contract. Leader reports cover work done and outcomes kept. They never track keystrokes or screen time.
Every run, the same way
Safe by default. On the record by design.
Every app, ours or yours, follows the same five steps. Nothing reaches a customer, candidate or system of record without a named person saying yes.
Ask
Someone runs an app from a thread and fills in a few inputs.
Gather
The app reads only the systems it was approved to use.
Draft
It prepares the email, brief or update for review.
Approve
External actions wait for a named approver. Nobody approves their own work.
Act and record
It acts, then saves a record of every step to the thread.
If a system isn’t connected, the run stops and tells you. It never pretends.
Rollout
From first login to measured adoption in four steps
Connect identity
Sign in with Google or Microsoft, or SAML SSO with SCIM on Enterprise, so access follows your directory. Connect CRM, help desk, email, calendar, HR and code tools once, for the whole company.
Set policy
Choose which systems and models each department may use, with limits like “triage up to 50 tickets a run.” Name the approvers for each kind of action. Tighten a policy and apps that no longer comply pause on their own.
Roll out to teams
Start with one department and its App Library in a 30-day pilot. Most companies start with sales or support. Add departments when the first one is working.
Measure adoption
Each library ends with a report for that department’s leader: work done and outcomes kept. You see what AI actually changed, not how many people logged in.
AI apps inside tabbr
Ready apps for the work teams already do
The App Library has a library for each department. These are a few of the starter apps. Your own apps join the same library under the same rules.
SalesFrom research to follow-up
Account research brief: one page on an account before you reach out. Call prep: agenda, talk track and likely objections. Meeting follow-up email: a follow-up for every attendee, sent after approval. Proposal first draft from the opportunity and your templates.
MarketingFrom conversations to campaigns
Blog post from a call: a draft post with quotes cleared for review. Campaign report: results across channels, with what to change next. Event follow-up email grouped by interest. SEO content brief with intent, outline and sources.
HiringFrom job post to shortlist
Job post draft from the role, level and team. Screening summary: a structured summary of a candidate against the role, for the recruiter to review. People data never trains models.
Training and onboardingFrom first day to first month
New hire onboarding: a first-30-days checklist for the new hire and their manager. Policy question: answers from your handbook, with the section they came from.
SupportFrom ticket to resolution
Ticket triage: tags, prioritizes and routes new tickets, up to 50 a run under policy. Ticket reply draft grounded in ticket history and your help center. Help article from a ticket and escalation summaries.
ITFrom requests to governance
Access request triage against policy. License usage report: seats paid for versus seats used. Vendor review: security and contract summary. Stale agent check: finds apps nobody has run or owned lately.
Start with one department
Run a 30-day pilot with one team and its App Library.
We connect your systems, turn on that department’s library and set up its leader report. See plans on the pricing page.