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. For the Roam AI ESP Platform we built the web and mobile product that turns an Electric Submersible Pump optimisation engine into a tool oilfield engineers use: submit-inference-request flows, setpoint recommendation history, contour optimisation plots showing current versus optimal, and recommendation cards an engineer can accept, reject or override with a comment and an audit trail. The optimisation model is Roam AI's. Our job was the product that makes its recommendations something an engineer can read, weigh and act on safely.

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. This shipped as part of the Roam ESP platform (telemetry-boundary breaches and alerts on downhole amps, motor temperature, pump pressures and frequency) and the LVLogics monitoring layer.

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. On the Roam ESP platform, model recommendations are surfaced for an engineer to accept, reject or override, with comments and a full audit trail. The person stays in control of the call.

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

Wolfpack Digital Q2 2026 wrap-up — the pack goes AI-native

Q2 2026 Wrap-up: The Pack Goes AI-Native

blog post publisher

Gina Lupu Florian

Founder & co-CEO

Reading time: 12 min

Jul 8, 2026

Gold at the Chambers Ireland Awards, a Webby nomination for ROAM-AI, an AI cost estimator on our own site, and an MVP service that goes from idea to live in weeks — here's everything the pack did between April and June 2026.

Wolfpack Digital team accepting the Gold award for Digital & Creative Agency Excellence at the 2026 eir business Chambers Ireland Awards, held at the Dublin Royal Convention Centre.

Inside Ireland's Top Software Development Agency

blog post publisher

Cristina Strîmbu

Marketing Specialist

Reading time: 6 min

Jul 6, 2026

Wolfpack Digital was named Ireland's top digital and creative agency at the 2026 Chambers Ireland Awards. Here's a look at the work behind the win.

AI-native software development concept — a humanoid AI head beside lines of HTML and JavaScript code, with the Wolfpack Digital logo.

What Is AI-Native Software Development? (And How It Differs From AI-Assisted)

blog post publisher

Adrian Florian

co-CEO

Reading time: 6 min

Jul 3, 2026

AI-native software development means AI runs through the whole build while senior engineers direct it. Here's what AI-native means, how it differs from AI-assisted, and how Wolfpack Digital builds this way.

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 built the platform engineers 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, which turns an ESP optimisation model into a tool oilfield engineers use; and Equality AI (Webby winner, 2024), which evaluates and reduces bias in healthcare ML models.
Python and Pandas for shaping data, Plotly for the charts behind the Roam ESP plots, 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.