Enterprise AI Consulting that Ships to Production
Enterprise AI consulting only pays for itself when something reaches production and stays there. We take organisations from a stalled proof of concept to a live system with owners, monitoring, and an ROI model a CFO can read — without ripping out the stack you already run.
Stuck between PoC and production
The pattern is familiar. A promising demo lands executive attention. A pilot runs on a curated dataset with a friendly vendor on hand. Then the real work starts: messy data, unclear ownership, a security review nobody scoped, and a model that looked strong offline and does nothing for the P&L once users arrive. Months later the programme is still "in evaluation," the budget is questioned, and the next AI initiative inherits the distrust. This page is for enterprises that already know AI matters and are done collecting slides. You need a partner who will say which use case is buildable this quarter, who owns the outcome after go-live, and what "done" means in production terms — latency, cost per request, override rates, drift alerts — not in demo terms.
The three calls that decide the outcome.
Made explicitly, with the trade-off written down, before anything gets built.
Which use case earns the right to go first
Ranking ideas by board excitement produces roadmaps that stall at the first data gap. Ranking by data readiness alone produces work nobody in the business cares about. We score value and readiness on separate axes and take the highest-value item that is actually buildable this quarter. That is rarely the idea at the top of either list on its own, and it is the only sequencing that survives contact with procurement and a live calendar.
Build depth versus buy-and-adapt
Most enterprise AI work is now buy-then-adapt: a capable model behind a gateway, grounded with retrieval and prompting, with fine-tuning reserved for stable tasks where you hold proprietary labelled data. Building from scratch is justified far less often than vendors suggest. The honest test is whether the capability is a durable differentiator or a commodity you are paying to re-implement — and whether your data is even allowed to leave your boundary while you decide.
What "production" means before anyone codes
Production is not a checkbox after the model works in a notebook. It is an agreed definition of success: the decision path the system will sit in, the latency and cost envelope, the human oversight model, the rollback plan, and the metric a business owner will read on Monday morning. We write that definition into the engagement before build starts. Without it you get another PoC that cannot explain why it should keep running.
The capabilities we bring on day one.
Each engagement assembles from this menu, sized to your scope, paced to your calendar.
Strategy that survives procurement
Readiness, use-case scoring, and ROI tied to named P&L lines — not a deck that dies in the budget cycle.
Build that reaches users
RAG, LLM apps, classical ML, and vision delivered into the systems your teams already open.
MLOps from day one
Drift detection, reproducible artefacts, safe rollout, and a retraining gate so last quarter's model stays honest.
Outcomes you can defend
Baselines before launch, online measurement after, and unit economics that hold at volume — not a one-time slide.
Governance that arrives before build, not after
Classification under the EU AI Act follows the decision the system makes, not how sophisticated the model looks. A modest classifier used in hiring, credit, or access to essential services carries obligations a larger drafting model used internally does not. We classify candidate use cases at scoping time, flag where a data protection impact assessment or processor agreement must exist before data moves, and design audit trails as a property of the platform rather than a retrospective document. That keeps legal and security in the room early — the cheapest place to find a blocker.
From kickoff to live in four phases.
Each phase has named deliverables, named owners and a named gate, and every one respects the systems you already have in place.
Discover
Stakeholder interviews across business, data, security, and legal. An honest inventory of what data is usable today, which decisions AI would actually change, and where previous pilots stalled.
Decide
Use cases scored on value and readiness. ROI modelled against named cost or revenue lines. Production definition, governance classification, and the first sprint scoped in enough detail to start.
Build
A pod embeds against your stack: retrieval and models where they earn their place, evaluation harnesses before scale, integration with identity and systems of record, and documentation produced during the build.
Run & hand off
Monitoring on quality, cost, and drift; runbooks your team can operate; and a clear path to own the system without us — or to keep an embedded pod if that is the better operating model.
What a good fit looks like.
Stated up front, so neither of us spends a call finding out this was the wrong conversation.
Engagement shape
A phased engagement: a fixed-fee readiness and sequencing sprint (around 60 days), then a scoped production sprint of roughly eight weeks against one use case, with an optional embedded pod for MLOps and the next wave.
From your side
A named business owner for the outcome, access to the data and systems the use case depends on, and whoever will speak for security and data protection. Without those three, consulting becomes theatre.
When not to hire us
If you want a generic chatbot on public web content with no proprietary knowledge and no production constraints, an off-the-shelf product will be cheaper. If you need a slide deck to justify a budget you have not decided to spend, we are the wrong partner.
Where this engagement draws from.
Enterprise AI consulting pulls the practice areas the use case needs — you do not buy every layer, you buy the path to production.
Enterprise AI Consulting questions, answered.
Strategy-only work ends at a roadmap. This engagement carries the same people through build and into production operations. The roadmap includes a first sprint scoped in enough detail to start, and the practitioners who write it are the ones who will build against it — which tends to produce more conservative and more achievable sequencing.
No. Rowth is deliberately additive. We build an intelligence layer on the data, applications, and cloud you already run. No rip-and-replace, no forced re-platforming. Platform work is introduced only when duplication across use cases makes shared substrate cheaper than another one-off.
Whatever the use case needs to reach a defined production bar: retrieval and LLM application work, classical machine learning, computer vision, automation with confidence routing, API packaging, and the MLOps practices that keep the system honest after launch. You do not buy every practice area — you buy the path from the decision you chose to a live system with owners.
With a baseline captured before launch, an online measurement design agreed with the business owner, and total cost of ownership that includes inference, monitoring, and human review — not just model fees. Offline metrics select candidates; only the online design establishes value. We treat that discipline as part of delivery, not a follow-up project.
Yes, and that constraint often decides architecture rather than preference. Self-hosted open-weight models, region-pinned processing, and PII redaction before inference are all in scope. We settle the data boundary in discovery so the build does not invent a compliance problem at go-live.