Vertical playbooks, applied.
Each sector has its own physics. Our engagement model adapts — the engineering rigour does not.
Six verticals, one practice.
Manufacturing
Edge inference on the line. Sub-second defect classification, anomaly scoring on PLC streams, and digital-twin maintenance models.
- Visual defect detection (>99% recall)
- Acoustic anomaly detection
- OEE uplift modelling
Retail & FMCG
Hierarchical demand models reconciled across SKU × store × week, integrated with promo calendars and weather features.
- SKU-level forecasting
- Markdown optimisation
- Voice-of-customer NLP
Healthcare & Life Sciences
Explainable, audit-trailed AI with clinical ontologies, regulated imaging pipelines, and HL7/FHIR integrations.
- Medical imaging triage
- Clinical knowledge graphs
- Documentation copilots
Financial Services
Streaming fraud detection, KYC document understanding, and explainable credit models that pass model-risk review.
- Real-time fraud scoring
- KYC / OCR pipelines
- Model risk documentation
Logistics
Probabilistic ETAs, dynamic routing, and inventory positioning models that survive contact with reality.
- Probabilistic ETAs
- Last-mile routing
- Network optimisation
Public Sector
AI Act-ready systems with transparent decisioning, full audit trails, and procurement-friendly delivery.
- Document understanding
- Decision support
- Open data pipelines
Different domains.
Same discipline.
We pick the model and stack that fits the problem, not the partner that pays.
We test on real data, not curated demos. The boring numbers matter.
Lineage, evaluation, monitoring — built in from day one, not bolted on.
Every system ships with the runbook your team needs to own it.
Why sector shape decides the model.
Sectors differ in three ways the model cannot afford to ignore. The shape of the data is the first — manufacturing yields high-frequency sensor streams under tight latency, retail produces sparse panel data under heavy seasonality, healthcare produces longitudinal records under strict consent. The cost of an error is the second — a misclassification in vision QA scraps a part, the same misclassification in clinical triage erodes trust. The decision cycle is the third — a fraud model has milliseconds, a planning model has a week. A practice that ignores these does not deliver the same engagement six times; it delivers six unsuitable engagements.
The work that actually adapts is not the work the press release shows. Model architecture, labelling protocol, evaluation harness, deployment topology, the human-in-the-loop contract, retraining cadence, the documentation that ships with the system — every one of these is sector-specific. Choosing them well is the difference between a deployment that survives the first regulatory inspection or the first stock-out and one that does not. What stays the same across sectors is the engineering discipline: honest data, honest baselines, honest measurement of what changed when the model was switched on.
Sector questions, answered.
Which industries does IT Researches specialise in?+
Manufacturing (vision QA, predictive maintenance), retail and FMCG (demand forecasting, sentiment), healthcare and life sciences (clinical knowledge graphs, imaging), financial services (fraud, risk, document AI), logistics (ETA, routing, inventory) and the public sector (citizen services, compliance, research).
Do you have experience with regulated industries?+
Yes — healthcare under HL7/FHIR with clinical ontologies, financial services under model-risk review, and public-sector deployments that are AI Act-ready with transparent decisioning and full audit trails.
What AI use cases are most common in manufacturing?+
Sub-second visual defect detection on the line (typically >99% recall), acoustic anomaly detection on PLC streams, predictive maintenance, OEE uplift modelling and digital-twin yield optimisation.
How do you approach retail and FMCG forecasting?+
Hierarchical demand models reconciled across SKU × store × week, fused with promo calendars, weather and event features, then connected into S&OP.
Can you work with healthcare data?+
Yes. We build explainable, audit-trailed AI on top of clinical ontologies, regulated imaging pipelines and HL7/FHIR integrations; every model ships with documentation a clinical safety officer will sign off on.
What's different about your financial-services work?+
Streaming fraud detection, KYC document understanding and explainable credit models that pass internal model-risk review and external regulator scrutiny.
Which industry is yours?
Tell us the sector, the data and the decision. We'll tell you whether AI is the right answer.