Strategy is the easy part.
Deployment is where AI fails.
Four out of five enterprise AI projects never deliver their business case. Aphelion pairs three decades of corporate systems delivery with a private AI platform we built ourselves for your business, so our AI reaches full potential, inside infrastructure you own.
What we do
Getting AI into production is a delivery problem, not a model problem. These are the disciplines that decide whether a pilot becomes a system your business actually runs on.
AI Strategy & Value Mapping
Finding the handful of use cases where AI changes your economics, sequencing them by value and feasibility, and being honest about the ones that will never repay the effort.
Private Deployment & Infrastructure
Sizing, specifying and standing up AI on hardware you control — on-premise, private cloud or fully air-gapped — including model selection, tuning and cost modelling.
Data & Retrieval Engineering
The most underestimated work in any AI programme. Cleaning, structuring and indexing your knowledge, with permission-aware retrieval and lineage from source system to answer.
Systems Integration & Automation
Connecting AI to the ERP, MIS, WMS and production systems your business actually runs on, and automating the workflows around them so the value compounds.
Bespoke Build & Extension
Custom agents, interfaces and applications built around your processes — for the work that no off-the-shelf tool covers, extending the platform rather than working around it.
Evaluation, Governance & Adoption
Evaluation baselines, output monitoring, audit trails and access policy — plus the training and workflow redesign that decides whether anyone uses what you have built.
Ten percent model. Ninety percent everything else.
BCG's widely cited split puts roughly a tenth of the effort in any successful AI programme on the algorithm itself, a fifth on data and technology, and the remaining seventy percent on people, process and workflow redesign. Almost every stalled AI project we see got the first ten percent right and stopped there.
We start where the value is, not where the technology is. That means mapping the handful of processes where AI genuinely changes your cost or capacity, sizing the prize honestly, and telling you which use cases are not worth building.
- Opportunity mapping and use-case prioritisation
- AI and data readiness assessment
- Business case, KPIs and success criteria
- Operating model and workflow redesign
- Build, buy or extend decisions
It is almost never a model problem
When an AI system gives a wrong answer, the cause is usually upstream: a superseded policy document still sitting in the index, two contradictory versions of the same procedure, a permission boundary that was never enforced. Gartner attributes the majority of failed AI projects to data that was never ready rather than models that were not capable.
This is the least glamorous and most decisive part of the work. We treat your knowledge base as a system to be engineered, versioned and maintained, not a folder to be pointed at.
- Source audit, data lineage and ownership
- Document cleaning, structuring and chunking
- Hybrid retrieval, reranking and citation grounding
- Permissions inherited from your source systems
- Versioning, refresh and decommissioning pipelines
Your models, your infrastructure, your data
Open-weight models have closed much of the gap with the large hosted services for the work most businesses actually need — summarising, drafting, extracting, answering over internal documents. That makes private deployment a practical choice rather than a compromise, and it removes the two objections that stall most enterprise AI programmes: where the data goes, and what happens to the bill at scale.
We deploy the Aphelion platform inside your environment, sized to your real workload. Nothing is metered per token and nothing leaves your control.
- Infrastructure sizing and hardware specification
- On-premise, private cloud or air-gapped deployment
- Model selection, quantisation and fine-tuning
- Agent design and prompt architecture
- Total cost modelling against per-token alternatives
Thirty years of the systems AI has to plug into
An AI agent is only as useful as the systems it can reach. That is where most AI specialists run out of road: they understand models but have never migrated an ERP, reconciled a stock ledger or cut over a production system without stopping the line. We have spent three decades doing exactly that.
It is the combination that matters. Deep familiarity with MIS, ERP, WMS and production environments, paired with a private AI platform we built ourselves, means the integration work is core capability rather than a subcontracted risk.
- ERP, MIS, WMS and production system integration
- REST, GraphQL and legacy API bridging
- Event-driven pipelines and real-time synchronisation
- Data migration, reconciliation and validation
- Phased cutover without operational downtime
Corporate systems delivery
Private — your data stays yours
Strategy, build, integrate, support
Why most AI programmes never
reach production
The evidence on this is unusually consistent. MIT's Project NANDA found that the overwhelming majority of enterprise generative AI pilots produced no measurable impact on profit and loss. RAND has reported that around four in five enterprise AI projects fail to deliver their intended business value, with roughly a third abandoned before they ever reach production. Gartner's own forecasting points the same way, and identifies data that was never ready as the single most common root cause.
None of this is a verdict on the technology. It is a verdict on delivery. The failures cluster in the same places every time: use cases chosen for novelty rather than value, knowledge bases that were never engineered, integrations treated as an afterthought, and teams who were handed a tool without anyone redesigning the work around it.
"Ask any AI partner to show you a system running in production, in a real business, today. Slide decks are easy to produce. Working deployments are not."
The large consultancies understand this well, and several have published the research that proves it. What they are less well suited to is the delivery itself at anything below enterprise scale — engagements are measured in quarters and hundreds of thousands, senior people rotate off accounts, and the platform being deployed usually belongs to somebody else.
Aphelion is built the other way round. The team that maps the opportunity is the team that engineers the data, deploys the platform and integrates it with your ERP — and the platform is ours, so there is no third party between the specification and the system.
Our approach
Four stages, each with a decision point you control. If the value is not there at the end of stage one, we will tell you — and you have lost weeks rather than quarters.
Discovery & Readiness
We map where value is actually leaking, audit the systems and data an agent would need to reach, and rank candidate use cases by value against feasibility. You get an honest readiness picture, including the use cases we would not build.
StrategyDesign & Proof
A costed proposal with defined deliverables, acceptance criteria and an evaluation baseline agreed up front — so “is it working?” has a measurable answer. Where the risk warrants it, we prove the hardest part first.
PlanningDeploy & Integrate
The platform goes into your environment, the data pipelines are engineered, and the integrations into your ERP, MIS or WMS are built and tested. Iterative, with working software in front of you throughout.
DeliveryAdopt & Improve
Managed go-live, training and documentation — then the part most programmes skip. We monitor output quality, retrain where accuracy drifts, and keep redesigning the workflow around the tool until people actually use it.
SupportThe difference in working with a team that builds what it sells
We are not a reseller and we are not an integrator for somebody else's platform. Aphelion was built by the same people who will scope your programme, engineer your data and deploy it into your environment.
That removes the handoff gaps where AI projects usually die — between the strategy deck and the build team, between the platform vendor and the integrator, between the specification and what actually shipped.
Private by design
Every solution we build runs on infrastructure you control. We have no incentive to route your data through third-party cloud services — our architecture reflects that.
One team, full stack
Consultants, AI engineers, integration specialists and software developers sit in the same team. No gaps, no delays between disciplines.
Evidence over slides
We would rather show you a working system than a case study. Every capability on this page is one we run ourselves, in our own platform, before we deploy it for anyone else.
Common questions
The questions we are asked most often by organisations weighing up an AI programme.
Discovery and scoping are fixed-price, so you can test whether the value is real before committing to a build. Delivery runs as either a fixed-price project or a retainer, depending on how well defined the scope is. We are deliberately structured for organisations that cannot justify a six-figure strategy engagement before anything gets built.
No. The consulting, data engineering and integration work stands on its own, and we will happily assess or improve a deployment built on something else. We are open about the fact that we built our own platform and think it is the right answer for most private deployments — but that is a recommendation, not a condition of working together.
No, and waiting until it is tidy is how AI programmes stall indefinitely. Data readiness is scoped per use case rather than across the whole business: we engineer the specific sources one agent needs, prove the value, then widen. The readiness assessment in discovery tells you exactly how much remediation each use case requires before you commit to it.
We work alongside internal IT rather than around them. Your team keeps oversight and ownership of everything we build, and we document and hand over so you are never dependent on us to operate it. Most of our engagements exist because a capable IT team needed specialist AI, retrieval or integration capability outside their current remit.
Every engagement includes handover, documentation and a defined support period. AI systems in particular need continued attention: source documents go stale, accuracy drifts, and workflows change around the tool. Most clients move to a retainer covering monitoring, evaluation and continued development for exactly that reason.
A focused deployment against a well-understood data source is typically six to twelve weeks from discovery to live. Programmes involving heavy data remediation or several system integrations run longer. The large firms typically quote nine to twenty-four months to production; being smaller and owning the platform is precisely why we do not have to.
Ready to get AI past the pilot stage?
Tell us where the pain is. We will tell you what is possible and what it will take to get there.