AI strategy and governed AI systems, Cape Town
AI that does the work, and asks before it acts.
We map what is worth automating, then build it into the systems you already run. Reads and drafts happen on their own. Anything with consequences waits for a person. Try it below.
I'm a demo of the assistant we build. Ask me about yesterday's sales, the inbox, stock counts, or say clean up. The data is invented; the rules are real.
stores reporting through systems we built, every week.
users on a ticketing app with no per-seat licence.
unaudited writes. Nothing leaves without a name on it.
every morning, a brief with the replies already drafted.
One rule,
every system.
The model may propose. Only a deterministic policy layer may authorise. Only registered tools may execute. Scroll to watch one action go through it.
- 1
Read
Search, summarise, classify. No approval, because nothing changes.
- 2
Propose
Draft the email, the ticket, the order. Show exactly what would happen.
- 3
Approve
A person confirms the exact action. Sends, deletions and payments never skip this.
- 4
Audit
Every step logged with who approved it, so you can answer for it later.
- 31 unread across 4 mailboxes
- 3 need you
- 9 can wait
- 19 noise
0% through
Two ways
in.
A plan before you spend, or a build because you already know. The plan is written by the people who will build it, so it never promises what cannot ship.
Strategy
Use-case discovery and roadmap
Four weeks with leadership and the people doing the work. A ranked roadmap, the first pilot scoped and priced, and a list of things we would leave alone.
Governance and compliance
A risk assessment, a framework people will follow, and the POPIA, GDPR and ISO material you need. Policies people read, not binders.
Team enablement
Role-specific sessions: what the tools do for finance, ops or support, what must never be pasted into one, and how to check the answers.
Build
Ask your data a question
Plain-language questions over point of sale, ERP, reports and spreadsheets, with every answer traced to the source rows.
Operations automation
Reporting, reconciliation, stock counts, ticketing, document handling. Real software with logins, audit trails and no seat fees.
Assistants that act
Connected to mail, files and tasks. Proposes actions, executes only what a named person approves. Provider-neutral, so no vendor lock-in.
Recent work
Anonymised. Numbers are the client's own. All work
Delivery reconciliation for a 78-store QSR group
Weekly exports from two delivery platforms turned into order-level reporting: late, missed or misreported orders, and what the platforms paid versus what they claimed.
Point of sale you can talk to
The group's POS connected to an assistant, so the ops team asks about yesterday's sales at any store in plain English.
Ticketing freed from per-user licences
Repairs and maintenance rebuilt as an installable web app for 250 users across three email domains, with photo capture and offline queueing.
Stock counts on phones
Paper and spreadsheets replaced by mobile forms that know which stores belong to which region, feeding the inventory system directly.
Our own product
Azani.
The assistant we built for ourselves and use every day. Every morning at 07:00 it reads a consolidated mailbox, sorts what needs a reply, drafts each one in the owner's voice, and sends a push. Through the day it researches with sources and captures notes, photos and voice memos into tasks and Drive. It sends nothing without a tap.
- Runs on Google Cloud, Johannesburg, our own project
- Login passkeys, no passwords
- Model swappable; Anthropic and OpenAI pass the same tests
- Model auth identity federation, no stored key
- Writes send, save, delete: all approval-gated, all logged
- Retention 90 days, then purged; voice discarded after transcription
Also ours: MedSync, a multi-tenant clinic treatment platform; PocketAlpha, a governed paper-trading workspace; and Echelon, a team of specialised AI agents with an orchestrator and an audit log.