1
Weeks 1–2
Value Map
Decompose the cost and revenue base and the business processes. Identify the value creation opportunities, then size and rank them.
You getA sized shortlist and a baseline
Data & AI value partner for PE-backed / owner-led business and enterprise
We map where data and AI actually create value in your business, then put engineers on the floor to build it. Two to three use cases live in production in twelve weeks — not pilots, not slideware.
Our Approach
Weeks 1–2
Decompose the cost and revenue base and the business processes. Identify the value creation opportunities, then size and rank them.
You getA sized shortlist and a baseline
Weeks 3–8
Data foundation, integration, evaluation. Two to three use cases into your systems, inside your own data boundary.
You getWorking software in your own stack
Weeks 9–12
In production with real users. In-language training on the floor — an unused system books nothing.
You getUsers on it weekly, not licences
Beyond
Managed service and cost governance — or a clean handover to your team. Then roll the proven pattern to the next function, site or business.
You getThe second deployment at a fraction of the first
AI Deployment
Deliberately senior at the point of contact.
Linking pin to your management and business
Owns the client relationship and the judgement calls, carries the management conversation, and — fully understanding how your business and processes run — architects the data and system flow.
In-region, in-language engineers
Full-stack build — data plumbing, integration, process rework, evaluation, deployment and run. Pods of 3–5 per engagement, staffed against your language and systems.
Digital, data & AI leaders across industries
Fluent in what digital, data and AI can do now, with hands-on experience landing it. Brought in per engagement and embedded in each pod for industry depth.
Explicitly absent — an analyst pyramid · a research lab · a sales organisation · a bench you fund between engagements
The delivery muscle — a standing partnership with a large digital and AI engineering organisation gives us production capacity on demand: hundreds of AI-native engineers reachable per build. Pods run in parallel across several engagements.
Why us
Each one is structural. None of them is a discount.
We start with how the business actually runs — the process, the data, the decisions — and rework it before a single model is trained. Consultants redesign the process and walk away; dev shops build software onto a broken one. We carry both ends.
Re-engineer and build.
Stop at any gate and keep everything produced up to that point. We engineer the cost of the intelligence itself — the right model at the right price on every use case — so the return in the business case is not eaten by the run rate behind it.
ROI and right model.
Operating partners embedded in your process and senior domain advisors set the direction, while a partnered engineering organisation supplies production capacity on demand — without an analyst pyramid or a bench you fund between engagements.
Right people, right cost.
Squeezed between the global firms on brand and the dev shops on price? No — the combination is the position: re-engineer and build, at dev-shop economics.
Team
Senior at the point of contact on every engagement — one accountable owner per engagement, with domain advisors embedded in the pod.
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Advisors are brought in per engagement and are not employees of the firm. Roles shown are current positions held independently of VectorOp; no advisor is deployed onto an engagement that conflicts with their employment. Advisor biographies are available in full under NDA.
Get in touch
A value map on the business you run, or a second opinion on an idea you are weighing. Both start with a conversation.