Auto Alpha AdvisoryThe second reader inside your business.
If someone on your team re-types what’s in one system into another, or reads the same kind of document every morning to decide where it goes — that’s the work this page is about.
Not AI as a concept. Not tools you evaluate and subscribe to. The specific, bounded work that a machine can read, classify, route, and act on — reliably, on your real data, with an audit trail — while your senior people do what they’re actually for.
We run AAA on it. We’ll say so plainly until we’ve delivered it for you.
inward read · own audit 88/100 · 2026-07-07
the same gates your pilot gets — nothing ships unproven
Why the stage gates exist
Around 30% of generative AI projects are abandoned after the proof-of-concept phase, according to Gartner’s July 2024 analysis.1 The failure pattern is consistent: a prototype works on clean demo data, the client commits to production, and the system meets the actual data — the edge cases, the format inconsistencies, the documents that don’t match the schema — and the output becomes unreliable before anyone knows how to fix it.
MIT’s NANDA initiative put a harder number on the same pattern in 2025: roughly 95% of enterprise generative-AI pilots deliver no measurable P&L impact.2 “Generic tools like ChatGPT excel for individuals because of their flexibility, but they stall in enterprise use since they don’t learn from or adapt to workflows,” wrote the study’s lead author, Aditya Challapally. That is the gap this practice exists to close — bounded scope, your real data, and a system that holds up after the demo.
The stage gates are our answer to that. You don’t pay for a production system until the Pilot proves itself on your data. You don’t commit to monitoring and tuning until Ship has delivered something worth running.
The staged path
Every engagement starts with the Map — a free 90-minute session where we tell you what shape your problem is. If the honest answer is that automation isn’t the right investment right now, that’s what you’ll hear. The Map is the free door; it’s also the honest one.
- stage 01
Map
A free 90-minute session: we map your workflows and find the high-leverage automations — or tell you it isn't worth it yet.
gatehonest go / no-go - stage 02
Pilot
Built and proven on your own data, in your own accounts, behind a human gate — never on clean demo data.
gate75%+ accuracy to advance - stage 03
Ship
Handed over and run in production in your stack. Monitored and tuned only once it has earned it.
ships at85%+ to ship · 90%+ before it runs unsupervised
| Tier | What it covers | Price |
|---|---|---|
| Map | 90 minutes — we tell you what shape your problem is: a workflow, an agent, or something Zapier already solves | Free |
| Pilot | A working system on your real data, in staging, fixed price. You don't commit further until it proves itself. | From R30 000 |
| Ship | Production: monitoring, error handling, POPIA review, documented handover — the repo and credentials are yours | From R75 000 |
| Run | Optional monitoring and tuning after Ship | From R3 500/mo |
| AI Implementation Sprint | Fixed scope, price and deadline — the fast path to production, 4–8 weeks | R50 000–R120 000 |
The Sprint is the fast path for clients who already know exactly what “done” looks like — fixed scope, fixed price, fixed deadline, 4–8 weeks to production. It assumes a defined problem, real data access from day one, and a client-side owner. If you’re not sure what the problem is yet, start with the Map.
Named capabilities
Two named capability families live inside this practice. Both route through the same free Map and the same staged path.
Knowledge Systems — search over your own documents, every answer cited back to the source. The pattern is retrieval-augmented generation: the model’s answers are grounded in your retrieved documents, not its training memory (Lewis et al., NeurIPS 2020).3 For businesses where the answer to a customer or staff question lives somewhere in a folder, a shared drive, or a set of PDFs — and the work is finding it reliably.
Operator Tools — the screens your team reviews, approves, and acts from. For businesses where the bottleneck is a person reading the same inputs every day and making a structured decision that a well-designed system could surface, queue, and route.
Full capability details are established at proposal stage. The Map is where scope is agreed.
What we run on ourselves
Three tools built inside AAA in approximately 80 days: the audit engine (9-domain, live and self-serve), the AI Visibility Tracker (running on our own tenant, Phase 1 launched 2026-05-29), and Komply — a compliance OS, in private build. The same discipline that produces the retainer reports, the build CI gates, and the monthly Layer 1 sweeps is the practice we’re describing here.
One of those tools reads AAA’s own work records and writes the time log. That is the inward read — the machine doing the bookkeeping so the practice runs leaner.
Our own audit scored 88/100 (AAA, 2026-07-07, audit engine v3). We don’t sell a standard we don’t hold ourselves to.
Not sure where you are? Start here.
Before the Map, there is a two-minute self-serve assessment that shows you which AI features you’re already paying for in the tools your business uses: See my unused AI features
This is not a lead form. It is a diagnostic. The output tells you whether there is a Pilot worth scoping — before you book anything.
Sources · 3
- Gartner press release, 29 July 2024. At least 30% of generative-AI projects projected to be abandoned after proof-of-concept by end-2025, citing production-data integration failures as a primary cause.
- MIT NANDA, “The GenAI Divide: State of AI in Business 2025,” reported by Fortune, 18 August 2025 — about 95% of enterprise GenAI pilots show no measurable P&L impact; the Challapally quote as published.
- Patrick Lewis et al., “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks,” NeurIPS 2020.