AI software engineered to move the needle — not just the dashboard
We design, build, and deploy intelligent systems that integrate directly into your operations. No vapourware demos. No generic chatbots. Just measurable shifts in how your business performs, decided by data and shaped by domain expertise from our base in Cork, Ireland.
Every engagement follows a structured arc designed to reduce risk and accelerate value. We move from discovery to deployment in iterative sprints, validating assumptions against real data at every gate.
01
Signal mapping
We audit your existing data estate, identify high-value signals, and document the gaps. This phase produces a prioritised opportunity map — a living document that anchors every subsequent decision. We interview stakeholders across departments to capture tribal knowledge that rarely appears in spreadsheets.
02
Architecture and prototyping
Our engineers design the model architecture, select appropriate frameworks, and build a functional prototype within weeks. We favour modular designs that can evolve alongside your business rather than monolithic systems that resist change. Early user testing happens here, not after launch.
03
Training and validation
Models are trained on your proprietary data, stress-tested against edge cases, and validated by domain experts on your team. We publish transparent performance reports — precision, recall, fairness metrics — so you understand exactly what the system can and cannot do before it touches production.
04
Deployment and evolution
We handle infrastructure provisioning, monitoring dashboards, and automated retraining pipelines. Post-launch, our team remains embedded for a transition period to ensure the system performs under real-world load. Quarterly reviews keep the model aligned with shifting business conditions.
Capabilities
What we build
Each capability area draws on deep research partnerships and production-grade engineering. We do not resell third-party APIs — every solution is purpose-built for your context.
Predictive analytics platforms
Forecast demand, churn, equipment failure, or financial risk with models calibrated to your historical patterns. Our platforms integrate with existing BI tools and surface actionable alerts rather than passive reports, enabling front-line teams to act before problems materialise.
Document intelligence
Extract structured data from invoices, contracts, medical records, and regulatory filings using custom-trained vision and language models. Our pipelines handle multi-language documents, handwritten annotations, and degraded scans with consistently high extraction accuracy.
Intelligent automation
Combine robotic process automation with adaptive AI to handle workflows that require judgement — approvals, triage, routing, quality inspection. Unlike rigid RPA scripts, our systems learn from exceptions and improve over time without manual rule updates.
Conversational AI
Build domain-specific assistants that understand your terminology, comply with your policies, and escalate gracefully. We train retrieval-augmented generation systems grounded in your knowledge base, reducing hallucination rates to levels acceptable for regulated industries.
Data engineering and MLOps
We design the pipelines, feature stores, and monitoring infrastructure that keep models healthy in production. From lakehouse architecture on cloud-native platforms to on-premise deployments behind air-gapped networks, we handle the full spectrum of operational complexity.
Computer vision systems
Detect defects on production lines, monitor safety compliance on construction sites, or classify satellite imagery at scale. Our vision models are optimised for edge deployment, running inference on compact hardware with latency measured in milliseconds rather than seconds.
Proof points
Measured, not promised
We track every engagement against baseline metrics agreed before work begins. Here are anonymised results from recent projects across logistics, healthcare, and financial services.
Supply chain optimisation — logistics firm
Replaced a manual demand forecasting process with a gradient-boosted ensemble model trained on three years of shipment data, weather patterns, and promotional calendars.
37% reduction in overstock
Claims triage — insurance provider
Deployed a natural language classification pipeline that routes incoming claims to the correct adjuster team within seconds, eliminating a two-day manual sorting backlog.
4.2 hours saved per adjuster daily
Quality inspection — food manufacturer
Installed edge-deployed vision models on packaging lines to detect label misalignment, seal defects, and foreign objects at 120 units per minute.
98.6% defect catch rate
Patient intake — regional health network
Built a document intelligence system that extracts structured fields from referral letters, reducing manual data entry by clinical staff and freeing time for patient care.
12 minutes saved per referral
Common questions
Before you reach out
How long does a typical engagement last?
Most projects span eight to sixteen weeks from signal mapping through initial deployment. Complex enterprise integrations may extend to six months. We scope every project with clear milestones and deliver working software at the end of each sprint, so you see progress continuously rather than waiting for a big reveal.
Do we need a large dataset to get started?
Not necessarily. During the signal mapping phase we evaluate data volume, quality, and relevance. In many cases, transfer learning and synthetic data augmentation techniques allow us to build effective models with surprisingly modest datasets. We will be transparent if the data is insufficient and recommend collection strategies before committing to model development.
Can your AI software integrate with our existing systems?
Yes. We design every solution with integration as a first-class concern. Whether you run SAP, Salesforce, legacy on-premise databases, or a custom microservices stack, we build API layers and event-driven connectors that slot into your architecture without requiring a platform migration.
How do you handle data privacy and compliance?
All data processing follows GDPR requirements by default, given our Irish base. We implement data minimisation, pseudonymisation, and access controls from day one. For clients in healthcare or finance, we layer additional safeguards aligned with HIPAA, PCI-DSS, or sector-specific regulations as needed.
What happens after deployment?
We offer ongoing support tiers that include model monitoring, automated retraining, performance reporting, and priority engineering hours for feature enhancements. Many clients transition to a quarterly review cadence after the first year, keeping models aligned with evolving business conditions without a large standing retainer.
After you submit the form, a senior engineer — not a sales representative — will respond within one business day. We will ask clarifying questions about your data landscape, current pain points, and success criteria before proposing any engagement structure.
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Disclaimer
Published 15/03/2026.
The content on this website is intended as general information about AI software development services. It does not constitute professional advice in any regulated domain including but not limited to legal, medical, or financial fields.
Performance metrics cited in case studies reflect specific client engagements and are not guarantees of future results. Outcomes depend on data quality, organisational readiness, and other factors unique to each project.
Catalyst Stream accepts no liability for decisions made based on information presented on this site. For project-specific guidance, please contact us directly at [email protected].