Custom ML & predictive analytics

Custom ML models that score, classify, forecast and rank your own data, built to run in production. We build the product around the model, so your team ends up with a working tool: recommendations to act on, forecasts to plan against, alerts that fire early enough to matter.

what it is

what is predictive AI?

Predictive AI reads the data your business already generates and makes a call about it: the forecasting, scoring, ranking and detecting behind everyday operational software. It's one part of our wider AI development services, and a different job from generative AI. Here's the line between them.

Predictive AI

Reads the data you already have and hands back a number, a label or a ranking. It scores a record, classifies it, forecasts a value, ranks a list or flags the outliers. It measures and decides, and it's the side this page is about.

Generative AI

Creates new content instead: a paragraph, an image, a reply, a summary. A different kind of model and a different kind of product. It writes and draws, which is why it's a separate service.

Forecasting on your operational data

Models that turn a live data feed into a number your team can plan against. For LVLogics we built a web platform that ingests silo sensor telemetry and forecasts runout in real time, so operators know when a silo will empty and can schedule refills instead of guessing. Pulling reliable forecasts out of messy real-world sensor data took iterative processing and steady refinement of the calculation models, and that work is what makes them dependable enough to schedule against.

Optimisation and recommendation from live telemetry

Products that take a model's output and put it in front of the person who acts on it. The Roam AI ESP Platform turns an Electric Submersible Pump optimisation engine into a tool for oil and gas operations, and our part was the interface. We redesigned the platform and built its new front-end, turning complex operational data into workflows centred on a recommendations inbox and real-time well monitoring, with alerts, comments and historical setpoint data alongside. The optimisation model is Roam AI's. Our work was the front-end that makes its recommendations something an operator can read, weigh and act on.

Anomaly and threshold detection on time-series signals

Alerting that watches a stream of readings and flags what falls outside safe bounds while there's still time to respond. We built this into the LVLogics monitoring layer, and on the Roam ESP platform we built the front-end where operators manage alerts and monitor wells in real time.

Data-analytics and decision-support platforms

When the goal is to help a domain expert see and rank hundreds of metrics rather than automate the decision, we build the platform for it. We built a sports-analytics platform that maps hundreds of performance statistics onto 40-plus visualisation types (scatter, heatmaps, radar, strike-zone breakdowns, 3D pitch paths), so analysts can read the numbers at a glance and a decision-maker can act on them.

Fairness and bias evaluation of ML models

Stress-testing an existing model to check it behaves fairly across the groups it affects. We built Equality AI, a Webby-winning (2024) responsible-AI platform that helps data scientists evaluate and reduce bias in the clinical decision-making algorithms healthcare relies on.

services

what we build

wolf
the bigger picture

more than predictive analytics

Need a model trained from scratch on your data, full ML model development and an MLOps pipeline, or computer vision on images and video? Those are part of our broader custom machine learning development offering. Start there and we'll scope the right approach with you.

products

what you can build

Forecasting and demand planning

Demand forecasting software that estimates demand, load, risk or runout so teams can plan ahead instead of reacting.

Scoring and prioritisation

Rank leads, tickets, accounts or records, or build churn prediction models that flag which customers are about to leave, so people spend time on what matters most.

Anomaly and fraud detection

Flag the transactions, readings or behaviour that don't look right, early enough to act.

Recommendation and personalisation

Recommendation engine development that suggests the next product, article or action based on what a user has done before.

Classification

Sort incoming records (documents, requests, events) into the right category automatically.

Operational optimisation

Turn live sensor or telemetry data into a recommended setting or next best action.

our process

how we approach predictive analytics development

01

Discovery

We run workshops to pin down what you're trying to predict and what a good answer looks like, before a line of model code is written. Then we map and model the data the model will depend on, because AI quality starts with data quality. This is also where we agree the accuracy target that makes the model useful for your decision.

02

Scope and estimate

We prioritise with a must / should / could breakdown, and simulate the ongoing hosting and processing costs so scale holds no surprises later. You get a clear estimate and a billing model that fits (time-and-materials or fixed), and we build only once the scope and the data are confirmed.

03

Build

We shape your raw inputs (telemetry, records, time-series) into a form the model can use, benchmark the candidate approaches and pick the best performer while keeping it swappable. Around that we build what makes the output usable: the dashboards, the alerts, the recommendation flows and the human-review controls that turn a prediction into a decision your team can make.

04

Evaluation and launch

We measure the model against real benchmarks, tune it, harden the system, run final testing and ship to production, then support the go-live. For data that keeps changing, this is where we make sure the model holds up on the messy real-world edge cases, not only the clean ones.
wolf
our process

how we approach predictive analytics development

01

Discovery

We run workshops to pin down what you're trying to predict and what a good answer looks like, before a line of model code is written. Then we map and model the data the model will depend on, because AI quality starts with data quality. This is also where we agree the accuracy target that makes the model useful for your decision.

02

Scope and estimate

We prioritise with a must / should / could breakdown, and simulate the ongoing hosting and processing costs so scale holds no surprises later. You get a clear estimate and a billing model that fits (time-and-materials or fixed), and we build only once the scope and the data are confirmed.

03

Build

We shape your raw inputs (telemetry, records, time-series) into a form the model can use, benchmark the candidate approaches and pick the best performer while keeping it swappable. Around that we build what makes the output usable: the dashboards, the alerts, the recommendation flows and the human-review controls that turn a prediction into a decision your team can make.

04

Evaluation and launch

We measure the model against real benchmarks, tune it, harden the system, run final testing and ship to production, then support the go-live. For data that keeps changing, this is where we make sure the model holds up on the messy real-world edge cases, not only the clean ones.

responsible AI and security

Predictive models drive real decisions: who gets flagged, what gets scheduled, which setting a machine runs at. The guardrails matter as much as the accuracy.

Fairness you can check. A prediction that affects people should be measurable, so its impact can be tested rather than assumed. Equality AI is a whole platform dedicated to measuring and reducing bias in healthcare ML models, and that discipline informs how we approach any high-stakes prediction.

Human-in-the-loop where the stakes are high. Where a prediction carries weight, our default is that the model proposes and a person decides. On the Roam ESP platform we designed and built the front-end where that happens: a recommendations inbox where operators review what the client's model proposes and act on it.

Honest about uncertainty. We agree upfront that a model can't be guaranteed 100% right, set the accuracy bar that makes it useful, and design the product to handle the cases where it's unsure.

core tech

tech stack

Python

AWS

Azure

Docker

Kubernetes

projects

our work

We specialise in premium mobile app development services, web development services, and everything that revolves around them: Product Design, Product Strategy, AI integration, QA and maintenance. Over the years, we've not only built powerful digital products but also played a key role in boosting conversion rates, optimizing performance, adapting to growing user bases, and improving app store rankings and reviews. These efforts have driven greater user engagement and significantly increased revenue.

Curious to see more?

See Our work

partners whotrustus

From startups to scale-ups and industry giants—brands across various industries choose us as their trusted partners. They rely on us to transform their ideas into stunning products, deliver innovative solutions, and enhance existing projects to help them stand out in the market.

insights

pack knowledge

Choosing an app development partner in Ireland

How to Choose an App Development Partner in Ireland

blog post publisher

Adrian Florian

co-CEO

Reading time: 10 min

Sep 8, 2026

A practical guide to choosing an app development partner in Ireland, covering costs, grants, nearshore trade-offs, and taking an AI prototype to production.

An isometric 3D illustration clustered in the center of a wide magenta-pink banner with subtle geometric patterns and the Wolfpack Digital logo at the top center. The illustration depicts a modern digital and physical payment system, featuring a central smartphone with a yellow screen displaying a digital wallet interface with a balance of "$215.8." Below the phone, a stack of gold coins, a payment card terminal with a large yellow 'PAY' button, and an inserted yellow credit card labeled "BANK" are arranged. To the right, a dark wallet sits behind the setup, and an unrolling physical paper receipt with transaction details is visible. Floating above are two security icons: a yellow speech bubble with a shield and checkmark, and a green speech bubble with a checkmark. The color palette is composed of pink, yellow, gold, and dark grey, with clean lines and a minimalist aesthetic.

How we audit fintech codebases in the AI era (and what still needs a human)

blog post publisher

Victor Motogna

Head of Engineering

Reading time: 11 min

Aug 31, 2026

Victor Motogna, Head of Engineering at Wolfpack Digital, breaks down how to decompose a fintech code audit into small deterministic and LLM-driven steps, and why every model flag has to be reproduced before it counts as a finding.

A Wolfpack Digital team member wearing a pink #webyte t-shirt, with colleagues in the background

Wolfpack Digital on Company Culture

blog post publisher

Corina

Marketing Specialist

Reading time: 2 min

Feb 9, 2021

What is company culture and why does it matter? Our take on building a company we love, where every team member enjoys the journey, and the seven values that guide us.

FAQ

frequently asked questions

wolf
Predictive AI scores, classifies, forecasts or ranks data you already have: it reads existing information and returns a number, a label or a ranking. Generative AI creates new content, like text or images. Predictive measures and decides; generative writes and draws. This page is about the predictive side; for generative work see our AI development services.
Both are possible, and we scope it in discovery. Sometimes the model already exists. On the Roam ESP platform the optimisation engine was the client's, and we redesigned the platform and built the front-end operators use to review and act on its recommendations. Other times we build the predictive layer ourselves, as we did for LVLogics' runout forecasting. If you need from-scratch ML model development or a full MLOps pipeline, we'll scope that under our AI development services.
It depends on what you're predicting, and it's the first thing we work out together, because AI quality starts with data quality. In discovery we map and model the data the model will depend on before committing to an approach, so you know early whether your data can support the prediction you want.
No one honestly can, and we say so upfront. What we do is agree the accuracy bar that makes the model useful for your decision, measure against real benchmarks, and design the product to handle the cases where the model is unsure, including human review where the stakes are high.
A few examples: LVLogics, a platform that forecasts silo runout from live IoT sensor data; the Roam AI ESP Platform, where we redesigned the platform and built the new front-end that puts an ESP optimisation model's recommendations in front of the operators who act on them; and Equality AI (Webby winner, 2024), which evaluates and reduces bias in healthcare ML models.
Python and Pandas for shaping data, Plotly for charting, and Sigfox for the LVLogics sensor telemetry, running on AWS or Azure with Docker and Kubernetes. We benchmark and pick the right approach per project rather than forcing one library, and we keep the model swappable so you're never locked to a single choice.
AI-native development is about how we build: the engineering method and how AI fits into your existing product and workflows. This page is about what we build on the predictive side: the custom ML models and the products around them. If you're not sure which you need, start with the AI development services overview.
We're a full-service software and app studio that builds predictive AI as one of our capabilities, and the work above is shipped rather than promised. Our team is senior, we're ISO 9001 and ISO 27001 certified, and we've been building and shipping production software since 2015. Because a prediction only earns its keep once it's inside a product people actually use, we can also fit a model into your existing app and workflows — that's our AI-native development work.