Facts you can trust.
Decisions you control.
Your systems, connected and checked. The routine work, automated. Money involved? Human in the loop! 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 logging repairs and maintenance on one system.
unaudited writes. Nothing leaves without a name on it.
every morning, a brief with the replies already drafted.
One policy,
endless systems.
The non-deterministic model proposes. Your fixed policy layer validates and authorises. Verified, isolated tools execute. This is the only way to achieve end-to-end reliability for complex agents across your diverse stack. Start the action sequence now.
- 01
Read
Search, summarise, classify. No approval, because nothing changes.
- 02
Propose
Draft the email, the ticket, the order. Show exactly what would happen.
- 03
Approve
A person confirms the exact action. Sends, deletions and payments never skip this.
- 04
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
Hi Nadia, confirming payment goes out Friday. Could you send the remittance address for the new entity? Thanks, Alex
Vernier
Our operations intelligence engine. It connects the data your organisation already has, checks each source against the others, and turns what it finds into actions a person approves. Any industry, any source with an export or an API.
- 01
Connect
Read from the systems you already run, through their exports or APIs. Nothing is replaced.
- 02
Check
Match the same customer, site, product or asset across systems, then compare. Where sources disagree, the gap is recorded, not averaged away.
- 03
Surface
Each morning, every role sees its part: what changed, what is out of line, and what it is costing.
- 04
Act
The follow-up is drafted. A person approves anything that moves money or changes a record.
Where
to start.
Most start with a plan that takes weeks to discover what is worth building. Knowing what you need to solve will lead into the build. The plan is written by the people who deliver, so it never promises what cannot ship.
Strategy
Find the work worth automating
Time spent with leadership and the people actually doing the work. The outcome is a prioritised list, the first pilot scoped and priced, and a clear view of what should be left alone.
Governance and compliance
A risk assessment, a deliverable framework people will follow, and the POPIA, GDPR and ISO attestations you need. Policies people read, not soulless files.
Training for your teams
Practical sessions built around each team's focus. What the tools can help with in finance, operations or support, what information should stay out of them, and how to validate output before relying on it.
Build
Operations Intelligence
Vernier connects the systems you already run, from sales and finance to CRM and IoT, checks them against each other, and answers questions about any of it.
Operations automation
Reporting, reconciliation, stock counts, ticketing and document handling. Proper software with logins and audit trails, built to keep working as the business grows.
Assistants that draft, and wait
Connected to your mail, files and tasks. They prepare the work and propose the next action, but a named person approves it. And because the system can work across different AI providers, you aren't tied to one.
Recent work
Work we're proud of. The store and user counts are real; the volumes and values are illustrative.
The accounting system, rebuilt and checked
Six years of the group's ledger rebuilt in a system we built, balanced month by month. Journals for sales, rent, delivery, payroll and royalties are built straight from the source systems and checked line by line against the books. Supplier bills wait for a second person.
The store app
Counts, deliveries, waste and the daily checklist on the store's tablet, with notices from head office. A count that looks wrong asks for a recount before it can be submitted, every entry reaches the stock engine the same day, and the app keeps working when the connection drops.
A stock engine of our own
Built to replace a vendor's stock system. It reads the same counts and recipes, and every night its figures are checked against the vendor's, store by store and item by item. Items, recipes and reasons are edited in one place, so the switch happens on evidence.
Audits, visits and corrective actions
Food safety, brand standards and health and safety audits are read from the auditors' reports every morning, beside district manager and quality visits. Every finding opens a corrective action: the store explains what it did, the district manager verifies, quality assurance closes.
Azani.
An everyday assistant we built and use.
07:00. It reads.
One consolidated mailbox, sorted into what needs a reply, what can wait, and noise.
It drafts.
Each reply written in the owner's voice, with the thread it answers underneath.
You tap.
Nothing sends without approval. The tap is the approval.
It is logged.
Who approved what, and when. Kept for 90 days, then purged.
Less like a bot,
more a dependable work companion.
Six things that are true of it, and of everything we build.
Nothing sends without a tap.
Send, save, delete: every write waits for a named person. Reads and drafts happen on their own.
Everything is logged.
Every step, with who approved it and when, so you can answer for it later.
Passkeys, no passwords.
Nothing to phish, nothing to reuse.
Model swappable.
Anthropic and OpenAI pass the same tests. No rebuild to change.
Your own cloud.
Google Cloud, Johannesburg, in a project you own. No stored model key; identity federation instead.
Kept 90 days, then purged.
Voice is discarded the moment it is transcribed.
Some of the other
products we've built.
Built for ourselves or for a partner, and run by us.
In their words.
Attributed by role and sector.
Running more than 40 locations, our problem was never whether the technology could do it. It was operational creep and data that didn't join up. We didn't get a demo or a deck. We got automated reconciliations that work with the systems we already had, and an audit trail we can follow. Branch managers get a useful summary every morning. Anything with money attached still waits for a person to approve it. It took hundreds of hours of dull work off our people without adding risk we couldn't see.
Chief Operating OfficerRegional quick-service restaurant and retail group
In lending and wealth management, an AI that makes something up and sends it to a client is not a mistake we can absorb. What won our audit committee over was the boundary control. Their line is that consequences need a person, and the build follows it. The system does the slow work of reading portfolio data and drafting client briefs. An advisor still decides whether anything goes out. It's POPIA-aligned, it runs in our own cloud, and the board stopped asking me about it, which was the outcome I wanted.
Chief Risk and Compliance OfficerFinancial services firm
Most of the agencies we spoke to wanted to sell us a platform and keep us on it. This was the opposite. The architecture is vendor-neutral and it runs on our own infrastructure. They went through our fulfilment handoffs, took out the duplicate manual entries between dispatch hubs, and left us with an audit trail from one end to the other. If a better model comes out next month, we can swap it in without rewriting anything. Not many people in this market build that way.
Chief Technology OfficerRegional logistics and distribution network
Running clinics across several disciplines means a constant drag of administration: intake triage, referral paperwork, balancing schedules. We couldn't have a system inventing patient notes, and we couldn't have questions about where the data sits. They worked out which of those bottlenecks were actually worth automating and left anything clinical alone. Our admin staff get back more than 15 hours a week, and it's documented well enough that my own team runs it.
Managing DirectorIntegrated healthcare network
We'd had a run of AI pilots that went nowhere and cost real money. The difference here was that the roadmap was tied to numbers I recognise, unit economics rather than whatever the industry was excited about that quarter. Research and drafting were split into defined roles, with a check before anything gets relied on. We went from experimenting with prompts to running something we can depend on.
Managing PartnerCommercial law and corporate advisory practice