How we approach real-world AI projects
Detailed industry solutions and project narratives: the challenge, why it is hard, the solution architecture, how we validate it, and what we measure.
All case studies
Predictive Shipment Risk and Intelligent Dispatch
A regional logistics operator coordinates shipments across hubs, carriers, and customer locations. Dispatchers rely on spreadsheets, transport-management records, GPS feeds, and phone calls. Delays are often recognized only after a delivery misses its promised window, while re-planning consumes staff time.
Read case study FinTechIntelligent Document Processing and Reconciliation
A financial services team manually extracts information from onboarding documents, statements, invoices, and transaction records. Staff compare values across systems and investigate mismatches. Poor scans and varied layouts increase workload, while errors can affect customers or compliance.
Read case study PropTechAI-Assisted Property Operations and Maintenance Triage
A property manager receives maintenance requests through email, messaging, calls, and a service portal. Categories are inconsistent, duplicate tickets are common, urgent issues compete with routine requests, and staff spend time searching leases and building manuals.
Read case study EdTechCurriculum-Grounded Learning Assistant
An education provider wants to support learners with different needs while educators handle repetitive questions, lesson preparation, assessments, and progress reporting. Generic chatbots can give unsupported answers and uniform learning paths may not reflect learner progress.
Read case study ClimateTech & Clean EnergyRenewable Forecasting and Predictive Maintenance
An energy operator manages renewable assets whose output varies with weather and equipment condition. Forecasts differ across sites, while maintenance teams rely on periodic inspections and alarm thresholds. Faults may go unnoticed until generation falls.
Read case study MediaTech & AdTechSearchable Media and Campaign Intelligence
A publisher or marketing team manages videos, audio, campaign assets, and performance reports. Staff spend time creating transcripts, tagging content, finding reusable assets, and preparing campaign summaries. Metadata is inconsistent and performance data is spread across platforms.
Read case study HealthcareTech & Life SciencesClinical Documentation and Knowledge Assistant
A clinical team faces documentation burden and fragmented information across notes, forms, guidelines, and protocols. Clinicians want faster structured note drafting and easier access to approved information, but medical content must remain accurate, attributable, privacy-conscious, and reviewed.
Read case study Manufacturing & IndustrialTechVisual Quality Inspection
A manufacturer inspects products at production stages. Manual inspection can vary across shifts, defects may be rare, and images change with lighting, camera position, material, and production conditions. Missed defects and false rejects both carry costs.
Read case study Retail & E-commerceCustomer Support and Inventory Intelligence
An online retailer receives repetitive questions about order status, returns, delivery, product details, and stock. Support agents move between order systems and policies, while forecasting errors create stockouts and excess inventory.
Read case studyThe same disciplined process in every project
Discovery and baseline
Map workflows, users, systems, failure points, costs, and success criteria before development.
Data and risk assessment
Review data quality, permissions, privacy, retention, representativeness, and likely failure modes.
Prototype
Build a narrow proof of concept against representative examples and clear acceptance tests.
Evaluation
Use held-out data, operational baselines, error analysis, and segment checks where appropriate.
Integration and human oversight
Use role-based permissions, approval gates, audit logs, and exception handling.
Pilot and rollout
Start in shadow or limited mode, gather feedback, define rollback, and expand only after acceptance criteria are met.
Monitoring and improvement
Track quality, cost, latency, drift, adoption, exceptions, and outcomes; version meaningful changes.
Have a problem worth testing?
Tell us about the workflow, decision or research question. We'll tell you honestly whether AI can help and how we'd find out.
Research-led AI, from clinic to company.