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AI and data consultancy

We build AI systems that work in the real world.

DigitalPath AI designs and ships AI agents, LLM-powered pipelines and data automation for Australian businesses.

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Plenty of AI projects stall at the prototype. We build the production system — and keep it running.

What we do

Five kinds of work. Each is a fixed-scope package with its price stated up front, so you know what you're buying before we start.

  • Platform Delivery

    A platform built around your workflows — users, integrations and admin — deployed to production.

    From $7,500

    Packages Request a quote

  • Standalone Instance

    Your own isolated instance of a platform, seeded with your data and ready for testing.

    From $18,000

    Packages Request a quote

  • Integration Modules

    Sign-in, billing, organisations, permissions or notifications added to a product you already have.

    From $4,000

    Packages Request a quote

  • Workflow Automation

    A manual process mapped, automated, run in parallel until it's trusted, then handed over.

    From $4,000

    Packages Request a quote

  • Data & Modelling

    Data audits, models, ETL pipelines and migrations validated at both ends.

    From $5,000

    Packages Request a quote

Prices in AUD, excluding GST, for a standard scope.

What we've built

Platforms we run in production today, built by the same team that will work on yours. See all projects

Client work

Results from finance, health insurance and supply chain. Client names stay confidential — we're happy to walk through the details on a call.

Financial services

An LLM document pipeline that pulls structured data out of PDF statements and prospectuses, plus an ETL feed from Morningstar straight into PostgreSQL. Reporting became jobs analysts run on demand.

~70%
less manual data entry
Days → hours
data refresh cycles
~40%
faster analytics queries
The problem we were solving

Analysts at an Australian financial advisory firm spent hours copying data from PDFs and Morningstar feeds into spreadsheets, which delayed every research cycle.

Built with Python, LLMs, PostgreSQL, ETL, REST APIs.

Health insurance

Churn and lifetime-value models in Python, with self-service Power BI and Qlik Sense dashboards on top, so retention, marketing and executives work from the same numbers.

~40%
less manual reporting time
In production
churn and LTV models
Self-service
dashboards for every team
The problem we were solving

A private health insurer wanted to see which members were likely to leave early enough to act. Reporting was manual and inconsistent across teams.

Built with Python, scikit-learn, Power BI, Qlik Sense, SQL Server.

Supply chain

Consolidated operational data from systems that didn't talk to each other, machine learning to surface patterns, and automated daily reporting in Python.

Daily
automated operations reporting
Trend signals
from large operational datasets
No spreadsheets
in the reporting loop
The problem we were solving

A food supply chain operator had orders, inventory and deliveries spread across disconnected systems and no early view of bottlenecks.

Built with Python, SQL, machine learning, BI tools.

Already built, ready to reuse

These modules run in our own products today. Using them in your build means you don't pay for them to be written again.

  • Subscription billing

    Stripe checkout, customer portal and the webhooks that matter — created, changed, cancelled, failed.

    Running in AusCovers

  • Sign-in and OAuth

    Email and password, Google and GitHub, verification and reset, rotating refresh tokens.

    Running in AusCovers, Datly, Dialoga, DigitalPath AI

  • One account across apps

    A central hub so the same user signs in to every product, with per-app approval and revocation.

    Running in DigitalPath AI, Dialoga

  • Contact forms that route

    Spam protection, server-side validation and each message delivered to the right inbox.

    Running in DigitalPath AI, Datly

  • Chat and AI assistants

    Real-time multi-tenant chat with assistants grounded in your own documents.

    Running in Dialoga

  • AI data tools

    Schema visualisation, SQL dialect conversion and document-to-data extraction with LLMs.

    Running in Datly

Who you'll work with

A small team based in Sydney.

  • Alex Martinez

    Alex Martinez

    Co-founder, AI and data lead

    10+ years shipping data and AI systems across finance, health and supply chain.

    Full profile
  • Claire Reinthal

    Claire Reinthal

    Co-founder, strategy

    Drives strategy, positioning and growth — turning AI products into scalable businesses.

    Full profile

Questions

What does a project cost?

Every engagement is a fixed-scope package with its price listed on the Services page — from a short code review or process mapping to a full platform build. Your quote confirms the scope and the final price before anything starts.

How long does it take?

Smaller packages take from a few days to five weeks. Platform builds run from four weeks for a pilot to twelve weeks for a full platform. Each package lists its duration.

How do we start?

Book a discovery call or request a quote. We'll talk through what you need, then send a proposal with scope, timeline and price.

What happens after launch?

Support retainers cover monitoring, fixes and small changes, from $1,200 a month. Automation retainers start at $1,500 a month.

Can you work with sensitive data?

Yes. We build in role-based access and audit trails, and where your data lives is agreed per project — including a standalone instance on infrastructure you control.

Something else? Ask us directly.

Ready to transform your data?

Book a complimentary 30-minute discovery call to discuss your data challenges and goals. We'll deliver a detailed proposal within 48 hours.

Book a discovery call