DesMind
Services

AI and software, engineered end to end.

We design, build and run intelligent systems: the model, the product around it and the cloud it runs on. One team stays accountable for the whole result.

01 / 06

AI and LLM applications

We build generative AI products that answer from your knowledge, act inside your systems and stay within the rules you set.

The problemGeneric chatbots invent answers, cannot see internal knowledge and are hard to trust with real work.

What we build

  • Knowledge assistants over documents, policies and tickets
  • Customer-facing and staff copilots
  • Agents that call your APIs and workflows
  • Document extraction and summarisation pipelines

How we do it

  • Retrieval-augmented generation with hybrid search and reranking
  • Prompt design and fine-tuning for consistent behaviour
  • Evaluation sets for accuracy, groundedness and safety
  • Access control, citations and audit trails

Technologies

  • OpenAI
  • Anthropic
  • Open-weight models
  • LangChain
  • Vector databases
  • FastAPI

An assistant your team can rely on, with every answer traceable to a source.

02 / 06

Machine learning and data science

We turn historical data into models that support real decisions, and we keep them accurate after launch.

The problemModels built in notebooks rarely reach production, and the ones that do often drift without anyone noticing.

What we build

  • Forecasting and demand prediction
  • Risk scoring and classification
  • Computer vision and document recognition
  • Recommendation and matching systems

How we do it

  • Feature engineering with leakage control
  • Calibration, fairness and stability testing
  • Model versioning and automated retraining
  • Monitoring for drift and performance

Technologies

  • Python
  • scikit-learn
  • PyTorch
  • TensorFlow
  • Keras
  • Pandas

Models that improve measurable outcomes and stay reliable once they are live.

03 / 06

Privacy-preserving and responsible AI

Grounded in our doctoral research, we design AI that learns from sensitive data without exposing it.

The problemHealth, finance and public-sector data often cannot be centralised, which stops AI projects before they start.

What we build

  • Federated training across hospitals, sites or devices
  • Privacy-first health and wellbeing applications
  • Governance-ready AI for regulated environments
  • Bias and fairness assessments

How we do it

  • Secure aggregation and differential privacy
  • Data minimisation and separation by design
  • Privacy risk analysis and documentation
  • Human review for sensitive outputs

Technologies

  • Federated learning
  • Differential privacy
  • Secure aggregation
  • FastAPI
  • PostgreSQL

AI that meets your privacy obligations instead of working around them.

04 / 06

Software and product engineering

We design and build complete digital products: interface, front end, APIs, databases and payments.

The problemOff-the-shelf tools rarely fit how an organisation works, and custom builds often stall between design and delivery.

What we build

  • Customer, tenant and partner portals
  • SaaS platforms and admin dashboards
  • Payment, booking and billing systems
  • Cross-platform mobile applications

How we do it

  • UI/UX design and accessible front ends
  • Typed APIs and well-modelled databases
  • Payments, email and background jobs
  • Automated and end-to-end testing

Technologies

  • React
  • Next.js
  • TypeScript
  • Node.js
  • Flutter
  • PostgreSQL
  • MongoDB

A product your users enjoy and your team can extend with confidence.

05 / 06

Cloud, DevOps and MLOps

We build the foundations that let you release often without surprises: automated delivery, observability and sensible costs.

The problemManual deployments, missing monitoring and unmanaged cloud spend turn every release into a risk.

What we build

  • Cloud architecture on AWS and Azure
  • CI/CD pipelines with test and evaluation gates
  • Containerised and serverless deployments
  • Model serving and MLOps pipelines

How we do it

  • Infrastructure as code
  • Logging, metrics and alerting
  • Security hardening and access control
  • Cost reviews and optimisation

Technologies

  • AWS
  • Azure
  • Docker
  • Kubernetes
  • Terraform
  • GitHub Actions

Faster, safer releases on infrastructure your team understands.

06 / 06

Data, automation and Microsoft 365

We connect your data and tools so information reaches the right people and routine work runs on its own.

The problemStaff lose hours copying data between systems, chasing approvals and maintaining spreadsheets.

What we build

  • Operational dashboards and reports
  • Approval and onboarding workflows
  • Microsoft 365, SharePoint and Teams solutions
  • Data pipelines between business systems

How we do it

  • Process mapping before automation
  • Power Automate and PowerShell scripting
  • Identity, access and licence governance
  • Documentation and staff training

Technologies

  • Power BI
  • Power Automate
  • SharePoint
  • Entra ID
  • Python
  • SQL

Less manual effort, clearer data and lower licence costs.

What we can build

Solutions we deliver

Most engagements combine several capabilities. These are the systems clients most often ask us for.

Knowledge assistants

Answers from your documents, with sources.

AI copilots and agents

Multi-step tasks completed across your systems.

Decision-support tools

Forecasts and risk scores inside everyday workflows.

Portals and SaaS platforms

Self-service for customers, tenants and partners.

Payment and billing systems

Online payments, receipts and reconciliation.

Mobile applications

Cross-platform apps with secure back ends.

Dashboards and analytics

Live reporting from data you can trust.

Workflow automation

Approvals, onboarding and notifications that run themselves.

Ways to engage

Start small, then scale with evidence

Every engagement is scoped after discovery, so you only commit to what the evidence supports.

2 to 3 weeks

Discovery sprint

Test feasibility before you invest in a full build.

  • Use-case and data assessment
  • Architecture and risk review
  • Working prototype or proof of concept
  • Costed roadmap with success metrics
Book a discovery sprint
Most common 6 to 16 weeks

Build and launch

A production system delivered in clear milestones.

  • Fixed milestones with live demos
  • Evaluation suite and acceptance tests
  • Deployment to your cloud
  • Runbooks, training and a support window
Plan a build
Monthly

Ongoing partnership

Engineering and advisory capacity when you need it.

  • Monitoring and model improvement
  • New features and integrations
  • Architecture and AI strategy advice
  • Priority response
Discuss a partnership
How we work

A delivery process you can see into

  1. Discover

    Goals, constraints, data access, compliance needs and success metrics, agreed in writing.

  2. Design

    Architecture, evaluation plan and milestones, with effort and risk called out early.

  3. Build

    Iterative delivery with working demos, tests on critical paths and staging environments.

  4. Launch and support

    Deployment, monitoring, runbooks and a defined support window after go-live.

Also available

Supporting services

Technical and AI strategy

Architecture reviews, AI readiness assessments and roadmaps.

Blockchain and smart contracts

Contract design, testing and on-chain integration.

Research and technical writing

Journal-ready papers, reports and documentation.

Workshops and training

Hands-on sessions in Python, React, cloud and applied AI.

FAQ

Common questions

Do you work with clients outside Australia?

Yes. We work remotely with teams in different time zones, with regular written updates and scheduled demos at every milestone.

Can our data stay inside our own environment?

Yes. We can deploy into your cloud account or on-premises infrastructure, use self-hosted models, and apply federated approaches so raw data never leaves its source.

How do you know the AI is accurate?

During discovery we build an evaluation set with you: real questions and expected answers. We track accuracy, groundedness and safety against it throughout the build, and releases are gated on those results.

Who owns the code and models?

You do. Code, prompts, evaluation sets and infrastructure definitions are handed over with documentation and training.

What does an engagement cost?

It depends on scope, data readiness and compliance needs. Most clients start with a fixed-price discovery sprint, which produces a costed plan for the full build.

Not sure which service fits?

A 30-minute call is usually enough to tell whether AI is the right tool, and what a sensible first step looks like.

Book a call