Industries
Six sectors. One engineering standard.
Select a sector to see what shapes the work, what we build and what the engineering has to get right.
Healthcare
Clinical-grade software, built around the patient record.
Healthcare systems fail at the hand-offs: between clinic and lab, record and device, clinician and patient. We build the platforms that close them, with privacy handled as a design constraint from the first sprint.
Discuss a healthcare project →Related workSmart ClinicTelemedicine AppAll HealthApplied AI in Healthcare
What shapes the work
- Patient data is protected health information, with access that has to be justified and logged
- Clinical tools must fit existing workflows or they are not used
- Records, labs, imaging and devices rarely share one data model
What we build
- Diagnostic imaging AI for radiology, pathology and ophthalmology
- Patient portals, clinical copilots and care chatbots
- Population health analytics and risk forecasting
- PHI de-identification and HIPAA-aligned data pipelines
What the engineering has to get right
- Role-based access, audit trails and de-identification designed in from the start
- Integration with clinical records and lab systems through standards such as HL7 and FHIR
- Model validation and human review for anything that informs a clinical decision
Finance
Systems that decide in milliseconds and explain themselves afterwards.
In financial services a model is only useful if it is fast, accurate and defensible. We build transaction, risk and knowledge systems with the controls a regulated institution needs to stand behind them.
Discuss a finance project →What shapes the work
- Decisions on credit, fraud and risk must be explainable to customers and regulators
- Fraud patterns shift faster than batch models can be retrained
- Sensitive data sits across core systems that were not built to share it
What we build
- Real-time fraud detection and transaction monitoring
- Credit and market risk models using alternative data
- Personalized financial products and advice
- Secure document Q&A and internal knowledge agents
What the engineering has to get right
- Low-latency scoring pipelines with monitoring for drift and false positives
- Documented model lineage, versioning and reason codes for every automated decision
- Encryption, access segregation and card-data scoping aligned with your security policies
Retail & E-commerce
Personalization and supply chain, run from the same data.
Retail margin is won in two places: the relevance of what a customer sees and the accuracy of what is on the shelf. We connect commerce, inventory and customer data so both improve together.
Discuss a retail project →Related workPartner Pharmacy PlatformAgentic Commerce for Distribution
What shapes the work
- Demand is volatile and promotions distort every forecast
- Catalog, inventory and customer data live in separate systems
- Peak periods multiply load on every service at once
What we build
- Demand forecasting to reduce stockouts and overstock
- Visual search and recommendation engines
- Customer segmentation, churn and sentiment analysis
- Automated replenishment and warehouse management
What the engineering has to get right
- Forecasting and recommendation models trained on clean, joined commerce data
- Search, catalog and checkout services built to hold up at peak
- Experimentation built in, so every change is measured against revenue
Logistics & Manufacturing
From the factory floor to the last mile, instrumented.
Operations improve when the physical flow of goods is visible in data and decisions follow from it. We build the forecasting, routing and inspection systems that turn telemetry into throughput.
Discuss a logistics project →Related workAgentic Commerce for Distribution
What shapes the work
- Downtime and late deliveries compound across the whole chain
- Sensor, fleet and ERP data arrive in different formats and at different speeds
- Decisions often have to be made at the edge, without a reliable connection
What we build
- Route optimization with real-time traffic data
- Predictive maintenance to minimize downtime
- Computer vision defect detection on the line
- Supply chain visibility and digital twins
What the engineering has to get right
- Streaming pipelines for telemetry, with integration into ERP and warehouse systems
- Vision and predictive models deployed at the edge as well as in the cloud
- Routing and planning services that re-optimize as conditions change
Real Estate
Valuation, demand and operations, read from the data.
Property decisions rest on fragmented records, local knowledge and slow paperwork. We build the data platforms and models that make valuation, forecasting and building operations measurable.
Discuss a real estate project →What shapes the work
- Listings, transactions and public records are fragmented and inconsistent
- Valuations have to be transparent enough to defend
- Leases and contracts hold critical terms in unstructured documents
What we build
- Automated valuation from location, comps and market trends
- Investment and demand forecasting from demographic data
- Smart building energy and maintenance optimization
- Tenant and property management platforms
What the engineering has to get right
- Geospatial and market data pipelines that reconcile many sources
- Valuation and forecasting models with clear drivers behind each estimate
- Document extraction and tenant platforms integrated with property management systems
Education & HR
Learning and talent systems that adapt to the individual.
Education and HR share one problem: matching people to what they need next, fairly and at scale. We build adaptive learning, matching and retention systems with privacy and fairness tested, not assumed.
Discuss a education project →What shapes the work
- Student and employee records carry strict privacy obligations
- Models that rank people must be tested for bias
- Content and data sit across learning and HR systems that integrate poorly
What we build
- Adaptive learning paths that respond to each student
- Candidate-to-role matching on skills and experience
- Retention risk models with suggested interventions
- Immersive VR/AR training and simulation
What the engineering has to get right
- Adaptive models evaluated for fairness across groups before release
- Integration with learning management and HR information systems
- Accessible interfaces and immersive training built for real classrooms and workplaces
How we work
Easy to engage. Easy to scale.
Vetting, contracts and delivery, engineered to raise your team’s velocity and cut your overhead.
Vetted Senior Talent
We accept the top 1% of engineers, so you only work with proven talent.
Every engineer is assessed on technical depth, problem-solving and communication before joining a client team. Our engineers average eight to ten years of hands-on experience, so they contribute from the first sprint instead of learning on your budget.
Timezone-Aligned Management
Product and project managers in your time zone, wherever your engineers are.
Our client engagement team is distributed globally. You get a product or project manager who works your hours, joins your meetings live and keeps delivery moving across time zones, so nothing waits overnight for an answer.
AI-Native Engineering
Engineers who build with AI, and build AI into your product.
Our teams use AI tooling across design, coding, testing and documentation to ship faster, and they have production experience with LLM applications, machine learning and MLOps. You get AI capability inside the team rather than from a separate vendor.
Full-Stack Capabilities
One team, from roadmap to production support.
Strategy, product engineering, cloud, data, AI and QA under one roof, across eight practice areas. The people who plan your architecture are the same people who build, ship and run it.
Scalable Teams
Start with one engineer. Grow to several teams.
Add or reduce capacity as your roadmap changes, and move between staff augmentation, dedicated teams and outsourcing without changing partner. We handle hiring, onboarding and replacements.
Standard MSAs & SOWs
Clear paperwork, so work starts sooner.
We work under a master services agreement with a statement of work for each engagement, so scope, responsibilities and terms are written down before anything starts. Adding a team or a project means a new SOW, not a new negotiation.
NDAs & IP Protection
Everything we build belongs to you.
NDAs are signed before any access is given, and IP assignment is part of every contract in every engagement model. Your code, models and data stay yours.
Enterprise-Grade Security
Security handled. Compliance built in.
Engineers work inside your environment under least-privilege access. Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work.
Managed Delivery
We manage delivery, quality and people.
Team leads run sprints, demos and reporting; we handle retention, performance and handovers. You set direction and see progress in the open, without managing every engineer.
FAQ
Industries: frequently asked questions
Which industries does AIGard work in?
AIGard works across healthcare, finance, retail and e-commerce, logistics and manufacturing, real estate, and education and HR.
The Industries page describes typical work in each sector, and the case studies set out selected experience of the team in detail.
Does AIGard have experience in regulated industries?
Yes. The team’s selected experience includes healthcare platforms covering clinical records, diagnostics, telemedicine and pharmacy operations.
Our practices align with ISO 27001, SOC 2, GDPR and HIPAA, with PII masking, encryption and audit logging built into data work. Specific regulatory requirements are reviewed during scoping.
Do we need a partner with experience in our exact industry?
Domain experience shortens the learning curve, but engineering quality and integration with your team matter more to the outcome.
AIGard matches engineers to your stack and domain at the first vetting stage, and you interview them before they join, so you can judge the fit directly.
How does AIGard handle sensitive data in industry projects?
Engineers work inside your environment under least-privilege access, with NDAs signed before any access is given.
Access is reviewed as roles change and removed when work ends. Data-handling needs and vendor requirements are reviewed as part of scoping the engagement.
What is the difference between staff augmentation, dedicated teams and software outsourcing?
The difference is how much of the delivery you manage. With staff augmentation, you direct individual engineers inside your own team. A dedicated team is a self-managing AIGard team working against priorities you set. With software outsourcing, AIGard owns delivery of the whole project against milestones you approve.
All three draw on the same vetted engineers and the same contracts, security practices and ownership terms.
How quickly can AIGard engineers start?
Engineers typically start within two to four weeks of the scoping call. The exact date depends on the roles, seniority and number of people required.
We confirm a realistic start date in writing before you commit, and you meet the proposed engineers before work begins.
How much does it cost to work with AIGard?
AIGard engineers typically cost 30–50% less than the fully loaded cost of a comparable US-based hire, with no recruiting fees, benefits overhead or insurance costs on your side.
The figure for a specific engagement depends on the model, the roles, the seniority mix and the size of the team. We issue a written proposal after the scoping call, so the commercial terms are agreed before any work starts.
Who owns the code, models and intellectual property?
You do. Intellectual property assignment is written into every contract, in every engagement model.
That covers source code, designs, documentation, data, models and prompts produced for you. Third-party components and their licenses are identified, so ownership is clear at handover.