AI services and consulting
AIPHINX helps organisations pick the right problems, build working systems on their own data, and run those systems safely once they are live. We work inside your cloud, we document what we do, and we hand it over.
phase 1 frame
+ map the process with the people who run it
+ audit the data that actually exists
+ score the backlog on value, effort and risk
phase 2 prove
+ prototype on your real data
+ agree the evaluation suite first
> go or no-go, on the numbers
phase 3 industrialise
+ guardrails, monitoring, rollback
+ deployed inside your boundary
phase 4 transfer
+ your team owns the core systemDeployed inside your cloud boundary. We do not train on your data.
An evaluation suite is defined before we build, and it decides whether we proceed.
Documentation, pairing and a written handover are in scope from the first week.
Services
Each can stand alone. Together they cover everything between an idea and a system your team runs without us.
We review your data, processes and constraints, then score candidate use cases on value, feasibility and risk. You get a sequenced roadmap with a business case for each initiative, and a clear view of what is not worth doing.
Assistants, retrieval over your own knowledge base, document understanding and agentic workflows. Built with retrieval you can audit, evaluations that gate every release, and guardrails your risk team can review.
The layer that decides whether anything else works. Pipelines, feature stores, vector infrastructure and warehouse modelling, built for lineage, cost control and reproducibility.
Quality inspection, safety monitoring, document capture at scale and geospatial analysis. Trained on your imagery, deployed to the edge or your private cloud, and measured against the process it replaces.
Model inventories, risk classification, documentation and human oversight, mapped to the EU AI Act, ISO/IEC 42001 and the NIST AI Risk Management Framework. Compliance becomes a byproduct of how you build rather than a project afterwards.
Continuous delivery for models and prompts, observability, drift and cost monitoring, and incident response. Run it with us under an agreed service level, or have us build it and train your team to take it over.
Approach
Every phase is fixed in scope and ends with a go or no-go. You can stop after any one of them and still own something useful.
Workshops with the people who do the work. We map the process, the data that exists rather than the data you assume exists, and the constraints that genuinely bind. The output is a scored backlog and a decision on what to build first.
A working prototype on your real data, with the evaluation suite agreed before we start building. We measure against your current baseline and report the numbers, including when they say the project should not continue.
Hardening for production: security review, guardrails, latency and cost targets, monitoring, rollback and documentation. Deployed inside your cloud boundary and integrated with the systems your users already work in.
Further use cases on the platform you now have, your engineers pairing with ours, and a documented handover. The measure of success is the point at which you no longer need us for the core system.
What we build
Described by the problem rather than by the technology underneath it.
Extracting structured fields from contracts, forms, invoices and correspondence, with a citation back to the source page and a review step where accuracy matters.
Question answering over internal policies, procedures and technical documentation, with every answer cited and out of scope questions declined rather than guessed.
Demand, capacity and maintenance models that feed an existing planning process, with the uncertainty made visible instead of hidden behind a single number.
Detecting defects, wear or safety conditions from camera feeds, deployed at the edge so it keeps working when the network does not.
Classifying incoming cases, tickets or claims so that people spend their time on the ones that need judgement.
The shared foundation underneath the above: pipelines, evaluation tooling, monitoring and access control, so the second use case costs a fraction of the first.
Industries
Sectors where the data is messy, the decisions matter and the results have to stand up to review.
Underwriting, client onboarding, reporting and model risk management.
Clinical documentation, trial feasibility, safety monitoring and evidence review.
Visual inspection, predictive maintenance, yield optimisation and forecasting.
Demand forecasting, assortment planning, service automation and content operations.
Routing, arrival prediction, document processing and fleet analytics.
Case triage, records digitisation and citizen services, with transparency requirements built in.
Engagements
Scope and duration are agreed up front. You work with the people you met, not a rotating bench.
A fixed scope diagnostic for teams deciding where to start.
A small cross functional team that takes one system into production and leaves the platform behind it.
We run what is already live so your team can build what comes next.
Every engagement is quoted after a scoping call, once the work is understood. Ask us for a proposal and you will get a written scope, a timeline and a fixed price for the first phase.
About AIPHINX
AIPHINX exists because most enterprise AI stalls in the gap between a convincing demonstration and a system that survives an ordinary working day. We staff for that gap: machine learning engineers, data platform specialists and risk practitioners who have been responsible for the things they built.
We work inside your cloud boundary, we do not train on your data, and we write down what we do so your team can take it over. If the honest answer is that AI is not the right tool for a problem, that is the answer you will get, before the invoice rather than after it.
Claims get an evaluation. We report the numbers even when they are unflattering.
Deployed in your tenancy, with no lock in and no data leaving your boundary.
Documentation, pairing and handover are part of the work, not an afterthought.
Risk, audit and human oversight are designed in rather than added before launch.
Contact
A short call with an engineer rather than a salesperson. If we are not the right fit we will say so, and point you somewhere more useful.
An assessment usually starts within two weeks. Build engagements depend on team availability, normally three to five weeks from signature.
Yes. We are model and cloud agnostic and often work with incumbent integrators and platform vendors. Where a tool you already own is sufficient, we will tell you.
It stays in your tenancy. We do not train on client data, and every engagement begins with a data handling agreement and a documented processing boundary.
Often. We start with a short technical review of what exists and give you a candid recovery plan, including the option to stop.
That is exactly what an assessment answers, in a few weeks, with numbers.