AI software services we offer

Five distinct service lines, each scoped around a specific kind of business problem. Pick one, combine several, or ask us to recommend the right starting point.

Service breakdown

Data visualisation representing predictive analytics

Predictive analytics

We train machine-learning models on your historical data to forecast future events: customer churn, product demand, equipment failure windows, or staffing needs. Each model is validated against a held-out test set, and we report precision, recall, and the business metric you care about most (e.g. "percentage of churners caught six weeks early").

Typical timeline: 6 to 10 weeks from data handover to production deployment.

  • Feature engineering from raw transactional or sensor data
  • Model selection (gradient-boosted trees, time-series models, neural nets where justified)
  • REST API endpoint for real-time or batch predictions
  • Automated retraining pipeline triggered by accuracy drift
Pythonscikit-learnXGBoostMLflow
Documents being processed by intelligent scanning

Document intelligence

Paper invoices, scanned contracts, medical intake forms, insurance claims: we build pipelines that read these documents, extract the fields you need, and push structured data into your database or ERP. Our pipelines handle multi-page documents, rotated scans, and mixed languages (English, Irish, French, German tested so far).

Accuracy after a two-week tuning cycle typically exceeds 95% on field-level extraction. For high-stakes fields like monetary amounts, we add a confidence score so your team can review only the uncertain cases.

  • OCR with layout-aware models (LayoutLMv3, Donut)
  • Custom entity extraction for non-standard document types
  • Integration with SAP, Oracle, Xero, or custom databases
  • Human-in-the-loop review interface for low-confidence extractions
TesseractLayoutLMFastAPI
User interacting with a conversational AI assistant on laptop

Conversational AI

We build internal and customer-facing chat assistants grounded in your own data. Unlike generic chatbot platforms, our assistants retrieve answers from your knowledge base, product catalogue, or policy documents, and cite the source for every response. This reduces hallucination risk and makes the output verifiable.

One insurance client deployed an internal assistant that answers policy-coverage questions for their call-centre agents. Average handle time dropped by 35 seconds per call because agents no longer had to search through PDFs manually.

  • Retrieval-augmented generation (RAG) with vector search
  • Fine-tuning on your domain vocabulary when needed
  • Slack, Teams, or web-widget deployment
  • Conversation logging and analytics dashboard
LangChainpgvectorOpenAI API
Server room with data flowing through fibre optic cables

Data pipeline engineering

Before any model can run, the data needs to arrive clean, on time, and in the right format. We design and build ETL/ELT pipelines that pull from your source systems (databases, APIs, flat files, event streams), transform the data, and load it into a warehouse or feature store. If you already have a pipeline that breaks every other Monday, we can audit and fix it.

We have built pipelines processing anywhere from 10,000 rows per day to 50 million events per hour, depending on the client.

  • Batch and streaming ingestion (Airflow, Dagster, Kafka)
  • Data quality checks with Great Expectations or custom validators
  • Schema evolution handling for upstream changes
  • Cost-optimised storage on AWS S3, GCS, or Azure Blob
Apache AirflowdbtKafkaSnowflake
Professionals reviewing an AI strategy document in a meeting room

AI advisory and training

Not every engagement needs custom code. Sometimes you need someone to evaluate whether a vendor's AI claims hold up, to review a model your internal team built, or to train your developers on machine-learning fundamentals so they can maintain systems independently.

We offer half-day and full-day workshops tailored to your team's skill level, covering topics from data preparation best practices to model evaluation pitfalls. We also provide written technical reviews of third-party AI tools you are considering purchasing.

  • Vendor evaluation reports (we test the tool on your data, not just read the brochure)
  • Model audit: bias testing, accuracy benchmarking, code review
  • Hands-on workshops for engineering teams (4 to 12 participants)
  • Quarterly check-ins for companies on a retainer
WorkshopsAuditsStrategy

Indicative pricing

Every project is quoted individually after the data audit, but these ranges give you a starting point for budgeting.

Pilot

€4,500
Fixed price, two weeks
  • Data audit report
  • Minimal viable model or pipeline
  • Accuracy and feasibility assessment
  • Written recommendations
Start a pilot

Full build

€15k–€45k
6 to 12 weeks, fixed price
  • Everything in the pilot
  • Production-grade model or pipeline
  • API and system integration
  • 90 days post-launch support
  • Full code and documentation handover
Request a quote

Advisory

€1,800/day
Minimum one day
  • Vendor evaluation or model audit
  • Workshop for up to 12 participants
  • Written deliverable included
  • Follow-up call within two weeks
Book a session

Service comparison

Use this table to see which service line fits your situation. If you are unsure, the scoping call (free, 45 minutes) is the fastest way to find out.

Feature Predictive analytics Document intelligence Conversational AI Data pipelines
Typical timeline 6–10 weeks 4–8 weeks 5–9 weeks 3–6 weeks
Minimum data needed 6 months of history 200 sample documents Knowledge base or docs Access to source systems
Ongoing retraining Yes, automated Periodic, as formats change When knowledge base updates N/A
On-premise option Yes Yes Yes (self-hosted LLM) Yes
Post-launch support 90 days included 90 days included 90 days included 60 days included

Not sure which service fits?

Tell us about the problem and we will suggest the right approach. The scoping call is free and takes 45 minutes.

Get in touch