We’re putting $5.5M behind research and development of smaller, specialized AI models for real-world deployment — Read our manifesto →
    Conscious Engines

    Industries

    Specialized AI shaped around the constraints, data, and operating environments of each industry.

    Healthcare

    Manufacturing and Industrial

    Government and Public Sector

    Legal and Compliance

    Banking and Financial Services

    Energy and Utilities

    Logistics and Supply Chain

    Telecommunications

    Pharma and Life Sciences

    Insurance

    Mining and Resources

    Construction and Infrastructure

    Real Estate and Facilities

    Retail, Hospitality, and Travel

    And beyond

    Wherever specialized AI can outperform a general-purpose model.

    Talk to our team →

    Every industry changes the shape of the problem.

    Data, risk, latency, infrastructure, and success criteria vary from one operating environment to the next. We start with those realities before choosing the model, training strategy, or deployment architecture.

    How we work with industries

    From the first production baseline to deployment and continuous optimization.

    Define the workload and the production target.

    We start by understanding the task, current architecture, available data, and the constraints the system must meet in production.

    • Map users, inputs, outputs, and failure modes
    • Review the current model, data, and infrastructure
    • Define quality, latency, privacy, and cost targets
    Evaluate workflow illustration

    Step output

    A clear baseline and an evaluation plan grounded in your production requirements.

    Case studies

    Case study

    The Million-Dollar Transcription Bill: StudyFetch's 10x AI Cost Reduction

    StudyFetch AI cost reduction reached roughly 10x on its largest workload. See the architecture, verified metrics and enterprise replication plan.

    Sep 7, 2026

    Case study

    [Case Study] SGD 1 Billion in AI Value: How DBS Industrialized 2,000 Models

    How DBS scaled more than 2,000 AI models across over 430 use cases and reported approximately SGD 1 billion in economic value in 2025.

    Sep 7, 2026

    Case study

    [Case Study] When AI Agents Can Move Money: How Conscious Engines Helped Polygon Build an Agentic Wallet

    How Conscious Engines helped Polygon build Kinetic-4B for agentic payments and position it within Polygon's wallet and x402 ecosystem.

    Sep 7, 2026

    Case study

    [Case Study] From 7,000 Questions to 100,000 Documents: Morgan Stanley's Eval-Driven RAG

    How Morgan Stanley built an eval-driven enterprise RAG assistant over 100,000 documents and reached more than 98% advisor-team adoption.

    Sep 7, 2026

    Case study

    [Case Study] Construction Progress Without Guesswork: NCC's AI Progress-Tracking Deployment

    How NCC used Buildots across 68,500 square meters to reduce reporting work, improve on-time construction tasks, and reduce subcontractor disputes.

    Sep 7, 2026

    Case study

    [Case Study] From Weekly Photos to Daily Site Truth: NOVO's 95% Documentation-Time Reduction

    How NOVO Construction used OpenSpace to reduce photo documentation time by 95%, avoid five travel hours weekly, and map 100 times more images.

    Sep 7, 2026