AI and automation for Australian businesses, aimed at repetitive work.
The useful AI work is usually the plain kind: reading documents, drafting a first version, answering the question your team is asked every day. Devarko builds these features into software your people already use, from our head office in Hobart and our engineering team in Lahore. Then we check whether they actually save time.
- VetPet Central: AI photo analysis and a vet-help assistant, live for our US client
- You approve: anything that saves, sends or pays waits for a person or a set rule
- Your account: model usage billed to you directly, capped and never marked up
- Training off: on every AI provider account we set up for you
§ 01Overview
AI with a clear job to do
The value sits in your data, your permissions and your workflows, not in the model. So we begin with one narrow task, tested on your own records against a handful of cases with known answers, and we say so when a plain rule or a better form would do the job for less.
Most of our AI work lands inside software that is already live with real users, whether that is a Laravel backend, a Next.js dashboard or a React Native app. That is deliberate. A feature on a screen people open every morning gets used, while a separate AI tool with its own login tends to be forgotten. For our US client, VetPet Central includes AI-assisted insights, with photo analysis and a vet-help assistant, inside the same web and mobile platform pet owners use to book vets.
Sometimes the honest answer is that you do not need a model. A query, a set of business rules or a scheduled job costs less to run, is simpler to justify to an auditor, and behaves the same after a provider ships a new version. On many projects the biggest saving comes from plain automation that moves work between systems, with no model involved at all.
For Australian businesses, you scope the work with an Australian point of contact at our Hobart head office, and our senior engineers in Lahore build it. Lahore mornings line up with Australian afternoons, so reviews and fixes happen inside your business hours. Prompts, settings and code live in your own repository, and model usage runs on a provider account in your name.
Where it fits
- Pulling data from invoices, purchase orders and scanned forms
- Support and onboarding assistants that answer from your own content
- Plain-English search across policies, contracts and manuals
- Routing and approvals that end manual rekeying
- Built into your software. Features appear on screens your team already uses.
- Answers with sources. Each answer links back to the document it came from.
- A person stays in charge. Anything that saves, sends or pays needs approval.
- Costs you can see. Usage is metered, capped and reported.
§ 02What you get
Six deliverables.
AI features in your product
Summaries, first drafts, tagging, classification and plain-English search, added to screens your users already know. Each feature sits behind a switch, so it can be turned off for one customer or for everyone without waiting for a release.
Assistants on your own content
Assistants for customer support, staff onboarding or internal questions that answer from your documents rather than the open internet. Each one has a clear hand-off to a person and keeps a full transcript you can review later.
Search across your documents
Your PDFs, policies, manuals and contracts are indexed with embeddings and retrieval. Staff type a question in everyday language and get an answer that points to the page it came from.
Document extraction
Invoices, purchase orders, bills of lading and scanned forms read into structured data, and checked by a person before it is saved. Our freight work is where this helps most: fewer keystrokes on every shipment file.
Workflow automation
Scheduled jobs, queues, approval steps and connections between your systems, so information is entered once and flows on from there. We use tools such as n8n, Zapier, Make or Power Automate where they fit, and custom code where they do not.
Testing, limits and logging
Each feature gets a test set drawn from your real cases, prompts under version control, rate limits, spending caps and a fallback for when a provider is slow or down. Every call is logged, so you can see what each feature costs and whether its quality has moved.
§ 03How it runs
Four steps, in the open.
- Step 01
Find the costly step
We watch the task being done and talk to the people who do it, so we map the real process rather than the one in the manual. A written scope then names the one or two steps that are worth automating and gives a rough monthly running cost. No code is written until you have read it.
- Step 02
Trial it on your records
A quick trial against your real records, scored on examples you would mark as right or wrong. It settles one question, whether the output is good enough for a user to see, and it gives you a natural point to stop before the build is committed.
- Step 03
Build it into the product
The feature is built into your existing code and goes through code review, testing by our QA engineers and a normal release, like any other change. This is where we handle permissions, edge cases, retries and what happens when the model is unavailable.
- Step 04
Measure and hand over
Reports on usage and cost, logs you can read, and prompts stored in your repository where your team can edit them. We finish with a walkthrough for the person taking ownership, plus written notes on what to keep an eye on.
Stack we use for this
- OpenAI
- Claude
- Gemini
- Llama
- Mistral
- LangChain
- LlamaIndex
- Hugging Face
- PyTorch
- TensorFlow
- Python
- FastAPI
- Node.js
- TypeScript
- Laravel
- Next.js
- pgvector
- Pinecone
- Qdrant
- OCR & document AI
- Speech to text
- n8n
- Zapier
- Make
- Power Automate
- AWS Bedrock
- Azure OpenAI
- Vertex AI
§ 04FAQ