AI consulting studio

Put AI to work
where it actually pays.

We design AI workflows, ship custom software with AI-assisted engineering, and cut the token bills that come with both. Measured in hours saved and dollars kept, not demos.

  • n8n
  • Claude
  • OpenAI
  • DeepSeek
  • Gmail & Outlook
  • Slack
  • AWS
  • Azure
  • GCP
  • Supabase
  • Vercel
10–20 hrssaved per person per week on inbox and admin
40–70%lower LLM spend after prompt and routing optimisation
2–6 wksfrom first call to a working workflow in production

Services

Four ways we make AI useful

Every engagement starts with the work you already do and ends with something running in your stack.

AI workflow automation

We map the repetitive flows in your business and rebuild them in n8n, Make or code, with an LLM doing the judgement calls. Inbox triage, drafting replies, CRM updates, document intake, reporting.

  • Mailbox automation for Gmail and Outlook
  • n8n and Make pipelines with human approval steps
  • Monitoring, retries and audit logs built in

AI-assisted coding

We set up your team to ship faster with agentic tools like Claude Code, without losing review discipline. Repo conventions, guardrails, test loops and CI that keep quality up while velocity climbs.

  • Agent-ready repos and CLAUDE.md conventions
  • Automated PR review and security checks
  • Hands-on training for your engineers

Custom software

Internal tools, customer-facing products and integrations built from scratch, with AI features where they earn their place. Secure by default, deployed with proper CI/CD and observability.

  • Web apps, APIs and integrations
  • RAG, search and assistant features
  • DevSecOps pipelines on AWS, Azure or GCP

Token & cost optimisation

If you already run LLMs in production, we find where the money goes. Prompt trimming, caching, model routing, batching and evals that prove the cheaper path gives the same answers.

  • Spend audit and per-feature cost breakdown
  • Prompt caching and routing to smaller or cheaper models such as DeepSeek
  • Eval suites so quality is measured, not guessed

Outcomes

What an engagement looks like

Shared inbox to triaged queue

A services firm handling hundreds of client emails a day. We built an n8n flow that classifies, drafts and routes every message, with a one-click approve step in Slack. The team reviews instead of types.

Result: replies out in minutes, one full-time role redeployed.

Cutting an LLM bill in half

A product team spending heavily on a single frontier model for every request. We added prompt caching, routed simple calls to a smaller model and built an eval set to prove parity.

Result: spend down 58% with no measurable quality change.

Agent-ready engineering team

A startup wanting to adopt AI coding tools safely. We restructured the repo, wrote conventions, added automated review and ran two weeks of pairing with the engineers.

Result: PR throughput up, review comments per PR down.

Process

Small steps, fast proof

  1. 01

    Discovery call

    Forty-five minutes on where time and money leak today. You leave with a shortlist of candidates, ranked by payoff.

  2. 02

    Scoped pilot

    One workflow, one feature or one cost audit, delivered in two to six weeks with a clear success metric.

  3. 03

    Ship & measure

    Running in your stack with monitoring, docs and a handover. We report the numbers, good or bad.

  4. 04

    Expand or stop

    If the pilot pays, we scale it. If it does not, you have learned that cheaply. No retainers you cannot justify.

About

Engineers first, consultants second

Mallpup is a small Brisbane-based studio of engineers, working remotely with clients worldwide, who have run production systems, security programmes and design systems before AI became the headline. We bring that discipline to AI work: version control, tests, monitoring and honest measurement.

We are tool-agnostic. n8n or code; Claude, OpenAI or DeepSeek; AWS, Azure or GCP. The right answer is the one that is cheapest to run and easiest for your team to own after we leave.

FAQ

Questions we get on the first call

What does an AI consulting engagement with Mallpup look like?

It starts with a 45-minute discovery call to find where time and money leak today. We then scope a single pilot, delivered in two to six weeks with a clear success metric, and expand only if the numbers justify it.

Which tools do you use for AI workflow automation?

Mostly n8n, plus Make or plain code when it fits better. We connect Gmail, Outlook, Slack, CRMs and databases, and use Claude, OpenAI or DeepSeek models for classification, drafting and extraction steps, always with human approval where it matters. We deploy on AWS, Azure or GCP, whichever you already run.

Can you automate my inbox or shared mailbox?

Yes. Mailbox automation is one of our most common projects: triage, labelling, drafting replies, routing to the right person and updating your CRM, with a one-click approve step so nothing goes out unseen.

How much can token optimisation save?

Teams typically cut LLM spend by 40–70% through prompt trimming, prompt caching, routing simple requests to smaller models and batching. We build an evaluation set first so the savings are proven not to hurt quality.

Do you build custom software as well as automations?

Yes. We build web apps, APIs, integrations and internal tools, with AI features where they earn their place, deployed with CI/CD, security checks and monitoring.

How do we get started?

Use the contact form below or email hello@mallpup.com with a couple of lines about the work you want to hand off. We reply within one business day.

Contact

Tell us what is eating your week

A couple of lines about the work you would like to hand off is enough. We reply within one business day with a straight answer on whether we can help.

Prefer email? hello@mallpup.com

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