AI Automation
AI Business OS
A turnkey 8-week implementation: connected sales, operations, knowledge and automation systems running inside your company.
AI Business OS is an eight-week implementation for connected workflows that cannot be solved as one isolated automation. It coordinates sales, operations, knowledge and approval rules as one operating model.
What the engagement covers
- You say what has to work — we design it and implement it inside your company
- Written instructions for every role: what to do and how, in plain language
- Team training — you are not left alone with documentation
- Human approval boundaries and a full handover: the system runs, the rules are written down
Final scope, timeline and implementation boundaries are confirmed in conversation. AI Automation is custom work, not an AI Visibility subscription.
When this scope fits
- Several teams or workflows depend on the same customer, process or knowledge data
- Point automations already exist but ownership and handoffs remain fragmented
- Leadership can assign process owners and approve an eight-week implementation scope
How the engagement works
- 01
Operating model
Map shared sources, responsibilities, approvals and the order in which systems must change.
- 02
Connected implementation
Build and test the agreed sales, operations, knowledge and automation layers.
- 03
Adoption
Train each role, publish operating instructions and hand over ownership with known limits.
What exists at the end of each stage
Eight weeks is the published length of this engagement. Stages overlap where the work allows it, but no stage is declared finished before the one it depends on.
Weeks 1–2
The operating model, agreed
Shared sources, responsibilities, approval rules and the order in which systems must change — mapped and signed off before anything is built.
Weeks 3–6
Connected layers, built and tested
The agreed sales, operations, knowledge and automation layers are implemented against the model and tested together, not one at a time in isolation.
Weeks 7–8
Adoption by role
Each role is trained on what it actually does, operating instructions are published where the team already looks, and ownership is handed over with the known limits written down.
After
One operating model, owned by you
Handoffs have owners, approvals have rules, and the parts that were deliberately left manual are documented as decisions rather than gaps.
Operating range
Where these systems already run.
Founded and led by Selena Nigmatullaeva — Founder & AI Systems Architect. Every system here is designed and built hands-on, not outsourced.
- KORA Food Hall
Hospitality operations
Menu, vendor, customer communication and local operations workflows shaped into a clearer operating model.
AI Sales · AI Operations · AI Content
3 AI flows in productiontenant outreach · content · daily ops digest — own venue, built hands-on
- PetID.care
Care and service infrastructure
Customer, pet profile and support workflows organized around trust, data and repeatable assistance.
AI Knowledge Base · AI Operations
- Doki.help
Documents and support
Document-heavy processes translated into clearer guidance, intake and customer-facing support paths.
AI Knowledge Base · AI Automation
- remhaos.com
Real estate and interiors
An AI-assisted presales flow for interior designers: project intake, risk review, pricing context and proposal preparation.
AI Sales · AI Automation
- otherbali.com
Travel media and guides
A Bali guide and media platform: places, guides and recommendations organized so visitors and residents find the right answer quickly.
AI Content · AI Knowledge Base
- VillaOps
Villa & hospitality operations
An operating system for villa management and guest services — day-to-day operations organized into one clear workflow.
AI Operations · AI Automation
Outcome figures for AI Automation engagements are not published: they have not been measured on terms that would survive being printed next to a price. Where results are published on this site, they carry dates and keep their zeros — see the measurement journal, which covers AI Visibility rather than this work.
FAQ
Before you commit
How is this different from four separate sprints?
Sprints solve one workflow each and are correct when workflows are independent. This engagement exists for the case where they are not: several teams depend on the same customer, process or knowledge data, and fixing them separately leaves the handoffs between them broken. The operating model is the deliverable that separate sprints cannot produce.
What does leadership have to commit to?
Two things: a named process owner for each area in scope, and approval of the eight-week scope before the build starts. Without the first, the system has nobody to belong to after handover. Without the second, the stages cannot be sequenced.
What access do you need to our systems?
Only what the agreed workflow touches, and only for the accounts it runs on. Access is requested per tool, in writing, and can be revoked at handover. We do not ask for admin access to systems outside the scope, and we do not move customer data anywhere it was not already stored.
What stays with a human?
Every decision where a mistake carries real cost: money leaving the company, a promise to a customer, anything legal, and anything sent under your name to someone who matters. The system prepares and proposes; a named person approves. Which steps those are is written down before the build starts and again in the handover.
Who owns the system after you leave?
You do. Everything runs inside your own accounts and tools, the rules are written in plain language, and the handover names the person on your side responsible for daily operation. There is no dependency on us to keep it running, and no licence that expires.
What if it does not work?
Where something is built, normal cases and edge cases are both tested before handover, and anything still unresolved is written into the handover rather than left silent. Testing narrows what reaches production; it does not promise nothing will fail there, which is exactly why the approval boundary and the written limits exist. And if a workflow turns out to be a bad fit for automation during the engagement, we say so and stop building it — that answer is part of the work, not a failure of it.