AI Solutions by Industry
Perimattic builds production AI for six industries: banking, financial services, healthcare, manufacturing, logistics and travel. Each system is trained on the client’s own data, integrated with the platforms that industry runs on, and designed around its regulations from day one.
Explore AI solutions for your industry
Each industry page covers the use cases that pay back, the data they need, where to start, and the regulations and systems we design around.
Why does AI need to be built for a specific industry?
AI only pays back when it handles the edge cases, systems and rules of one industry. A fraud model needs a bank’s transaction history and an audit trail for model risk review; a clinical documentation model needs the EHR and HIPAA controls; an inspection model needs the lighting and line speed of a specific plant.
That is why we organise our work by industry. The engineering is shared, from data pipelines to monitoring, but every engagement starts with the data, systems and regulations of your sector.
What are the top AI use cases in each industry?
The highest-return use cases differ by industry, but the pattern is the same: high-volume decisions, existing historical data and a cost that is easy to measure.
| Industry | Top AI use cases | Key regulations | Typical first project |
|---|---|---|---|
| Banking | Fraud scoring, AML alert triage, KYC extraction, credit underwriting | SR 11-7, ECOA/FCRA, PCI DSS, EU AI Act | Fraud scoring or KYC extraction |
| Financial Services | Claims automation, underwriting, payments fraud, advisor copilots | DORA, NAIC AI Bulletin, FCA Consumer Duty | Document extraction or claims triage |
| Healthcare | Clinical documentation, revenue cycle, imaging support, triage | HIPAA, FDA SaMD/CDS, ONC HTI-1, DCB0129 | Referral extraction or denial prediction |
| Manufacturing | Visual inspection, predictive maintenance, scheduling, knowledge assistants | IEC 62443, ISO 9001/IATF 16949, 21 CFR Part 11 | Visual inspection or a knowledge assistant |
| Logistics | Route optimisation, demand forecasting, ETA prediction, freight documents | Hours of service, customs data, GDPR | Freight document automation or ETAs |
| Travel | Dynamic pricing, personalised offers, servicing assistants, disruption | EU pricing rules, EU261, PCI DSS, GDPR | Servicing assistant or demand forecasting |
What every industry engagement has in common
The use cases change from sector to sector. These four principles do not.
Your data, your model
Models are trained on your own history, so they learn your customers, assets and edge cases rather than a vendor's averages.
Compliance designed in
The rules each industry runs under, from HIPAA to SR 11-7, are architecture constraints decided before any model code is written.
Integrated where work happens
Outputs land in the core banking system, EHR, MES, TMS or booking engine your teams already use, not in another dashboard.
Measured against a baseline
Today's accuracy, cycle time or loss rate is measured first, so results are proved in your own numbers.
From scoping call to production in five stages
Every engagement follows the same staged process, priced before the work starts and measured against a baseline agreed up front.
- 11–2 weeks
Scoping and data assessment
Use case defined, data readiness scored, compliance constraints listed, and a fixed quote agreed.
- 21 week
Baseline and evaluation design
Today's accuracy, cycle time or loss rate measured, and success thresholds agreed in writing.
- 32–8 weeks
Model development
Data preparation, training and iteration against a held-out test set, with weekly demos on real data.
- 42–4 weeks
Integration
Inference wired into the systems your teams already use, with auth, audit logging and fallback paths.
- 5Ongoing
Production and monitoring
Deployment, drift and accuracy monitoring, alerting, retraining and runbooks for your operations team.
AI by industry: frequently asked questions
Which industries does Perimattic build AI for?
Perimattic builds production AI systems for six industries: banking, financial services (insurance, wealth and payments), healthcare, manufacturing, logistics and travel. We also deliver AI projects in professional services, education and real estate when the use case fits our experience.
Which industries benefit most from AI?
Industries with high volumes of repeatable decisions and rich historical data benefit most. Banking and insurance (fraud, claims, underwriting), healthcare (documentation and revenue cycle), manufacturing (inspection and maintenance), logistics (forecasting and routing) and travel (pricing and service) all fit that pattern.
How is industry-specific AI different from general-purpose AI tools?
General-purpose tools are trained on broad public data and tuned to be acceptable across many tasks. Industry-specific AI is trained on your own transactions, documents or sensor data, integrated with industry systems such as an EHR or TMS, and built around that industry's regulations, which is what makes it accurate and auditable enough for production.
How do you handle compliance in regulated industries?
Compliance requirements are listed during scoping and treated as design constraints: data residency, access control, audit logging, explainability and documentation are built in from the start. We supply the evidence your compliance, risk or clinical safety teams need, while certification and regulatory sign-off stay with your organisation.
Where should a company start with AI in its industry?
Start with a use case where labelled history already exists, the cost of today's process is easy to measure, and someone will act on the output. Document extraction, forecasting and alert triage are common first projects because they pay back quickly with low risk.
How long does an industry AI project take and what does it cost?
A proof of concept for one use case typically takes 2–4 weeks and starts from $3,500. A production system with integration and monitoring takes 6–14 weeks and typically costs $10,000–$30,000. Large integrations or projects needing formal validation are quoted after scoping.
