# Invenire — full site content

> Founder-led AI engineering studio. We help teams turn AI prototypes
> into production systems — agents, retrieval, vision, evals, telemetry,
> and the engineering glue. Founded and run by Arve Sølland.

Site: https://invenire.com.au/
Email: hello@invenire.com.au
Based: Gold Coast, Australia (AU · GMT+10)

This file concatenates every page on the site in markdown form so LLMs
can ingest the whole site in a single fetch. The short curated index is
at https://invenire.com.au/llms.txt.

---

## Home

URL: https://invenire.com.au/

Pill: frontier · production · founder-led

### Hero

**Frontier AI for the part after the demo.**

I help founders, CTOs, and engineering teams turn promising AI prototypes
into reliable production systems: agents, retrieval, vision, evals,
telemetry, and the engineering glue required to make them work outside a
controlled demo.

CTAs: Discuss an engagement (https://invenire.com.au/contact/) · See
selected work (https://invenire.com.au/work/)
Proof: Live now — Analytics Agent in the Shopify App Store.

### Credibility strip

National-scale research infrastructure. Live AI products. Confidential
agentic systems. One or two clients at a time.

### Stat strip

- shipping_since: 2005
- production_ai_systems: 6+
- engagements_at_once: one or two
- based_in: AU · GMT+10

### What Invenire is

Invenire is a founder-led AI engineering studio based on the Gold Coast,
Australia. I design and ship agentic systems, retrieval and vision
pipelines, evaluation infrastructure, analytics products, and AI-native
engineering workflows for teams that need AI to work outside the demo.

The models move quickly. I stay close to what is changing, test what
matters, and bring clients the parts that hold up under production
constraints.

### Flagship product: Analytics Agent

**Eyebrow:** flagship · live · production
**Link:** https://analytics-agent.app

Six specialist agents read a Shopify merchant's GA4, Search Console and
store data every day and write three to five plain-English findings. No
dashboard. It is the live proof point — every architecture pattern, every
eval technique, every retrieval choice I recommend on a call has already
been pressure-tested here.

- **mission_briefs** — Six agents, one router, daily brief. Rewritten three times before the briefs stopped reading like LLM slop.
- **anomaly_detection** — Fifteen-minute checks against a rolling baseline. Catches the 2am spike before anyone logs in.
- **ga4_audit** — Eight checks, a 0–100 score, one-click fixes. The fixes are the hard half — and the reason this exists.

### Services (home summary)

**Four ways to bring in a senior AI engineer for the work that has to hold up.**

- **01 / AI strategy and architecture.** Decide what to build, what to buy, and what to ignore. I help teams find the AI opportunities that are technically possible, commercially useful, and worth the operational risk.
- **02 / Production AI systems.** Agents, retrieval, vision, and multi-modal workflows designed to run beyond the demo. Built with evals, telemetry, failure modes, and human review where they belong.
- **03 / Evals and observability.** Golden datasets, regression tests, traces, cost visibility, latency tracking, and quality gates. Trust becomes something you can measure.
- **04 / AI-native engineering.** Help your team adopt Cursor, Claude Code, agent workflows, eval-first development, and AI-assisted delivery without turning the codebase into an unreviewable mess.

(Detailed write-ups: https://invenire.com.au/services/)

### Operating principles (compact strip)

currency · evidence · reliability · focus — full text on
https://invenire.com.au/about/

### Selected work (home summary)

**Production systems. Not slides.**

- **Analytics Agent** (SHOPIFY · LIVE · 2025–) — Six-agent system delivering daily Mission Briefs, fifteen-minute anomaly detection and a GA4 audit with one-click fixes to Shopify merchants. Router over specialists, LLM-as-judge evals wired into CI, full cost and latency telemetry per tool call. Live in the App Store. (https://analytics-agent.app)
- **Plant** (SOLO-BUILT · SELF-HOSTING · 2025–) — Seven services, five languages, one operator. An autonomous engineering platform exploring how one senior operator can coordinate AI agents, externalised state, objective verification, and multi-service software delivery. The R&D layer behind the AI-native engineering practice. (https://invenire.com.au/work/plant/)
- **EcoCommons** (NATIONAL · GRIFFITH · 2020–2023) — Australia's national platform for ecological and environmental modelling. Nine partner institutions, a decade of legacy systems re-engineered into one reproducible-by-design modelling platform. (https://invenire.com.au/work/ecocommons/)

(All five case studies: https://invenire.com.au/work/)

### Founder (home summary)

**Arve Sølland.** PRINCIPAL_AI_ENGINEER · FOUNDER

Arve Sølland is a Principal AI Engineer and founder of Invenire. He has
spent 20 years shipping production software, including national-scale
research infrastructure, climate and geospatial platforms, Shopify AI
products, and confidential agentic systems. His work sits where
fast-moving AI capability meets the slower, harder world of production
engineering.

Invenire takes on a small number of engagements at a time, usually where
architecture, implementation, and judgement all matter.

Contact: hello@invenire.com.au · linkedin.com/in/arvesolland

### Closing CTA

**Have a system that has to actually work?**

Thirty minutes. Bring the architecture diagram or the screenshot of the
bug — whichever is closer to where you are stuck. I will tell you what I
would build, what I would not, and whether I am the right person for it.
If I am not, I will say so.

→ Discuss an engagement: https://invenire.com.au/contact/

---

## Services

URL: https://invenire.com.au/services/

### Hero

**AI engineering for serious production work.**

Invenire works with founders, CTOs, operators, and engineering teams who
have moved past AI curiosity and need systems that can be evaluated,
deployed, trusted, and improved.

### How engagements work

Most teams do not need another AI brainstorm. They need a clear decision,
a working system, or a way to tell whether the thing they built is
actually improving.

Engagements usually start with a focused diagnostic, architecture review,
or build sprint. If the work has a clear path to value, it can continue
into implementation, evals, or team enablement.

- **Diagnose** — Clarify the opportunity, constraints, architecture, risks, and evaluation strategy.
- **Build** — Implement the smallest useful system that proves the capability safely.
- **Transfer** — Leave the client with working software, decision records, evals, telemetry, and a team that understands the system.

### 01 / AI strategy and architecture

Decide what to build, what to buy, and what to ignore. I work with
founders, CTOs, and senior teams to identify where AI can create real
operational leverage, then design the architecture, evaluation approach,
and delivery path to get there.

This is for teams that need technical clarity before committing budget,
hiring a team, choosing a vendor, or betting a product roadmap on AI.

**Good fit when** there is pressure to "do something with AI", but the
team needs a clear technical path before spending serious money.

**Deliverables:** Architecture review, opportunity map, model/tool
recommendations, risk register, build/buy/partner recommendation, and
phased implementation plan.

### 02 / Production AI systems

I design and build agentic systems, retrieval pipelines, vision
workflows, and multi-modal applications that are expected to run in the
real world — with observability, fallback paths, permissions, review
loops, and evaluation built in from the start.

The work is not "make a chatbot". It is system design around unreliable
intelligence: what the model should decide, what software should decide,
what a human must review, and how quality is measured over time.

**Good fit when** a prototype already exists, the opportunity is obvious,
but the current system is too fragile, manual, slow, or unmeasured to
trust in production.

**Deliverables:** Working production system, architecture, integrations,
eval harnesses, telemetry, deployment path, handover notes, and
improvement backlog.

### 03 / Evals and instrumentation

AI quality cannot be managed by vibes. I build evaluation harnesses,
golden datasets, regression gates, traces, cost and latency dashboards,
and review workflows so teams can see when a system improves, degrades,
drifts, or fails.

Quality as a number you can fail CI on.

This is often the missing layer between a promising AI prototype and a
system the business can actually rely on.

**Good fit when** the team has shipped something AI-powered, but nobody
can confidently answer: "Is it getting better, worse, more expensive, or
more risky?"

**Deliverables:** Golden dataset, scoring rubric, LLM-as-judge setup
where appropriate, regression tests, trace review, model comparison,
failure taxonomy, cost/latency tracking, and release gates.

### 04 / AI-native engineering practices

I help engineering teams adopt modern AI development workflows — Cursor,
Claude Code, agentic coding, eval-first development, review loops, and
automation — without losing control of architecture, quality, or
security.

The goal is not to make developers type less. The goal is to increase the
amount of correct, reviewed, production-ready work a small team can
safely ship.

**Good fit when** your engineers are already using AI tools informally,
but the organisation has not yet developed shared practices for review,
testing, security, architecture, and measurement.

**Deliverables:** Workflow design, tool selection, team training,
coding-agent patterns, review standards, eval-first development
practices, repo-specific playbooks, and pilot implementation.

### Engagements

Invenire works with a small number of clients at a time. Engagements are
scoped around outcomes, not open-ended hours.

- **Diagnostic and architecture sprint** — For teams that need technical clarity before committing to a build, vendor, hire, or roadmap.
- **Production AI build** — For teams turning a prototype, workflow, or opportunity into a production system.
- **Evals and observability review** — For teams that have shipped something AI-powered but cannot yet measure quality, cost, drift, or risk with confidence.
- **AI-native engineering enablement** — For engineering teams adopting AI development workflows with discipline.
- **Principal advisory** — For focused senior input: architecture review, technical due diligence, AI roadmap review, vendor assessment, model/tool selection, or executive guidance.

Scope and investment are discussed after there is enough context to be
useful. Invenire is not a fit for low-cost implementation capacity or
generic AI experiments without a clear owner.

### CTA

**Have a system that has to actually work?** Thirty minutes. → Discuss an
engagement: https://invenire.com.au/contact/

---

## Selected work

URL: https://invenire.com.au/work/

### Hero

**Systems that made it past the demo.**

Selected work across AI agents, analytics, autonomous engineering,
national research infrastructure, climate platforms, model-training
loops, and audit-grade data workflows.

The common thread is not the domain. It is the level of responsibility:
systems that need to produce useful outputs repeatedly, under
constraints, with enough structure to be trusted by real users.

### Case study — Analytics Agent

**Tag:** SHOPIFY · LIVE · 2025– · https://analytics-agent.app

A production Shopify analytics product that turns GA4, Search Console,
and store data into daily merchant decisions. Six core analysis agents
are coordinated by a router to detect anomalies, surface opportunities,
audit GA4 configuration, and produce a plain-English Mission Brief.

The product demonstrates Invenire's core operating pattern: bounded
agents, structured outputs, recurring evaluation, operational telemetry,
and commercial UX wrapped around the model layer.

### Case study — Plant — autonomous engineering platform

**Tag:** SOLO-BUILT · SELF-HOSTING · 2025–
**Link:** https://plnt.dev
**URL:** https://invenire.com.au/work/plant/

An autonomous engineering platform exploring how one senior operator can
coordinate AI agents, externalised state, objective verification, and
multi-service software delivery. Seven services across five languages,
designed around agent-first workflows and completion checks rather than
ad hoc prompting.

Plant is the R&D layer behind Invenire's AI-native engineering practice:
how to use agents aggressively without losing architecture, reviewability,
or control.

The decisions that make it work: externalised state beats context
accumulation (conversation is deleted between iterations); objective
completion verification, not the model's word; tooling built for agents,
not adapted from human tools; Code Mode (a typed TypeScript API generated
from MCP schemas); a system that watches itself; and a system that builds
itself, every repo developed using the loop.

What it is: a real, self-hosting system doing real work. What it isn't: a
commercial product — test coverage is deliberately uneven and several
conveniences suit a solo operator.

**Signal:** services=7 · languages=5 (PHP, Go, Rust, Swift, TypeScript) · operator=1 · self-hosting=every repo built by the loop it runs
**Scale:** Spark ~7,000+ test cases · Ralph ~24k LOC, 470+ tests · Foreman ~58k LOC, 1,000+ test functions · Chief ~24k LOC · Telemetry 5-crate Rust core
**Tech:** Laravel, React, Go, Rust, Swift, Docker, AWS spot, Pulumi, MCP, Claude Code, PostgreSQL/pgvector, SQLite/Tantivy

### Case study — EcoCommons — national ecological modelling

**Tag:** TECHNICAL LEAD & SOLUTIONS ARCHITECT · GRIFFITH UNIVERSITY · 2020–2023
**Link:** https://ardc.edu.au/project/ecocommons
**URL:** https://invenire.com.au/work/ecocommons/

National-scale ecological and environmental modelling infrastructure. As
Technical Lead and Solutions Architect at Griffith University, Arve
helped deliver a reproducible modelling platform across nine partner
institutions and a multi-year funding environment.

This work matters because production AI has the same hard requirements
as serious research infrastructure: provenance, reproducibility,
permissions, reliability, and users who need to trust the output.

The hard part was making national-scale, reproducible science work across
nine partner organisations, four scientific disciplines, and a decade of
incompatible legacy systems (BCCVL, ecocloud, CSDM re-engineered into one
platform), wiring in national and global data providers (ALA, TERN, GBIF,
OBIS, IMOS) and giving researchers a reproducible-by-design cloud
sandbox.

**Signal:** role=Technical Lead & Solutions Architect · scope=national, 9 partner institutions · program=3-year, 2020–2023 · domains=biodiversity, biosecurity, hydrology, agriculture
**Tech:** distributed cloud + HPC, microservice architecture, species-distribution & climate models, FAIR data pipelines, JupyterLab / virtual-desktop sandboxes, reproducible-by-design environments

### Case study — Autonomous model-training agent

**Tag:** CONFIDENTIAL CLIENT · APPLIED AI · 2025–2026 · Reference available under NDA
**URL:** https://invenire.com.au/work/autonomous-training/

A confidential agentic system that runs repeated ML experimentation
loops: hypothesis, code, training run, scoring, analysis, and next
experiment. The hard work is not making the agent act. It is preventing
reward hacking, false progress, wasted compute, and unreviewable changes.

The engineering challenge was designing the loop so progress could be
trusted.

The engineering is all in the guardrails: an evaluation harness the agent
cannot edit; git as the experiment ledger; a deployability rule rather
than a bare accuracy score; and a loop built to survive its own length by
keeping working memory in files.

**Signal:** human ML engineers in loop=none · compute=commodity spot GPUs · reproducibility=git-ledgered · search=architecture chosen per problem
**Tech:** Python, PyTorch, timm, agentic coding agent (Claude Code), spot-GPU infrastructure, object storage, git-as-ledger

### Case study — Image annotation and QA platform

**Tag:** CONFIDENTIAL CLIENT · PRODUCTION · 2025–2026 · Reference available under NDA
**URL:** https://invenire.com.au/work/annotation-platform/

A confidential platform for turning inconsistent human review into
audit-grade, model-ready datasets. The system combines weighted
consensus, disagreement surfacing, provenance, and QA workflows so the
resulting data can be trusted downstream.

The value is not automation for its own sake. It is higher-confidence
data production under real operational constraints.

**Signal:** build=solo, end to end · role=design, architecture, implementation · trust model=weighted consensus, disagreement surfaced, full provenance · output=human-verified, model-ready datasets
**Tech:** Next.js, React, TypeScript, PostgreSQL, cloud data pipeline, SSO, object storage

### Earlier shipped work

Before the frontier work: document-intelligence and retrieval systems for
clients with strict compliance and audit requirements, and twenty years
of commerce platforms, payment integrations and web infrastructure.

### CTA

**Want something like this built?** Thirty minutes. → Discuss an
engagement: https://invenire.com.au/contact/

---

## About

URL: https://invenire.com.au/about/

### Hero

**AI engineering for the part after the demo.**

Tag: principal_ai_engineer · founder

I'm Arve Sølland, founder of Invenire. I help teams design, evaluate,
and ship AI systems that need to work in production — agents, retrieval,
vision, analytics, automation, and AI-native engineering workflows.

### Founder bio — twenty years, three eras of stack

I have spent 20 years shipping production software, from national-scale
research infrastructure to modern agentic AI systems. Before founding
Invenire, I was Technical Lead and Solutions Architect on EcoCommons,
Australia's national platform for ecological and environmental
modelling, and Technical Lead on the Pacific iCLIM project with Griffith
University and SPREP.

Today, my work is focused on production AI: agents, retrieval, evals,
telemetry, vision pipelines, analytics systems, coding agents, and the
engineering practices required to make them reliable.

I stay close to frontier models and tools because the ground is moving
quickly. But I am not interested in novelty for its own sake. The test
is whether a system becomes more useful, reliable, faster, cheaper,
safer, or easier to operate.

### Why clients bring me in

Clients usually bring me in when AI has become important enough that
experiments are no longer enough. They need someone who can speak
strategy with leadership, architecture with engineers, and implementation
with the codebase open.

- **The prototype works, but nobody trusts it** — I help teams add evals, telemetry, review loops, and architecture around fragile AI prototypes.
- **The opportunity is clear, but the path is not** — I help teams decide what to build, what to buy, what to ignore, and where AI can create real leverage.
- **The team is moving fast, but without a system** — I help engineering teams adopt AI-native workflows without sacrificing quality, architecture, or review.
- **The work is too important for generic advice** — I work best where the problem needs both senior engineering judgement and current AI capability.

### Career arc — The same bar, three eras of stack.

- **The frontier work (2024–).** Agents, evals, retrieval and vision in production: Analytics Agent for Shopify merchants, the Plant autonomous engineering platform, and two confidential applied-AI client systems — an autonomous model-training agent and an enterprise annotation and QA platform. The systems are on the work page; the judgment behind them is what I sell.
- **National-scale infrastructure (2020–2023).** Technical Lead and Solutions Architect for EcoCommons (https://ardc.edu.au/project/ecocommons), Australia's national platform for ecological and environmental modelling — a three-year program across nine partner institutions at Griffith University. Designed the platform and led the engineering team and partners through to launch.
- **Research infrastructure (before 2020).** Technical Lead on the Pacific iCLIM project — a Griffith University and SPREP (https://www.sprep.org) collaboration funded by Australia's Department of Foreign Affairs and Trade. Led delivery of the climate-change decision-support tools behind SPREP's Pacific Climate Change Portal, including the Adaptation Project Tool and the Climate Finance Navigator, and ran the training that put them in the hands of government officials across the Pacific. The stack changed — the bar didn't.

### Operating principles — Four non-negotiables.

- **currency** — The AI frontier changes quickly. I track the models, tools, patterns, and failure modes closely, then apply the parts that hold up under production constraints.
- **evidence** — AI systems need measurement. I prefer evals, traces, golden datasets, regression gates, cost curves, and failure taxonomies over opinions about whether a model "seems better."
- **reliability** — Non-determinism is not an excuse. Production AI needs bounded responsibilities, structured outputs, observability, fallback paths, human review where appropriate, and tested recovery.
- **focus** — The goal is not to use the most AI. The goal is to ship the smallest system that creates leverage and can be trusted.

### Stack (`$ arve --stack --json`)

```
{
  "languages":   ["TypeScript", "Node.js", "Python", "PHP",
                  "SQL", "Rust", "Go", "Bash"],
  "ai":          ["LLM orchestration", "multi-agent systems",
                  "retrieval pipelines", "eval frameworks",
                  "vector indexing", "geospatial", "vision"],
  "infra":       ["AWS", "Azure", "GCP", "Docker",
                  "Kubernetes", "Terraform", "GH Actions"],
  "databases":   ["Postgres", "MySQL", "MongoDB", "DuckDB",
                  "Snowflake", "Vector stores"],
  "specialties": ["Agentic AI", "Enterprise security & compliance",
                  "Client-facing technical delivery"]
}
```

### Formal training

- **2006** — Master of Information & Communication Technology — Australia
- **2005** — Bachelor of Information Technology — Australia
- **2001** — IT Studies — Norway

### References

Available on request. EcoCommons and iCLIM relationships are real and reachable.

### Contact

- **studio_inbox / Email** — hello@invenire.com.au
- **network / LinkedIn** — linkedin.com/in/arvesolland
- Founder-led. You work directly with Arve.

---

## Contact

URL: https://invenire.com.au/contact/

### Hero

**Discuss a production AI engagement.** Pill: currently open · limited capacity

Invenire takes on a small number of clients at a time. The best fit is a
founder, CTO, operator, or engineering team with a serious AI
opportunity, a fragile prototype, or a production system that needs
better architecture, evals, or delivery.

CTAs: Pick a time (Cal.com inline embed on page) · Email instead
(hello@invenire.com.au)

### Discovery call

Pick a time. Thirty minutes, no slides. We'll talk through what you're
building, where it's stuck, and whether Invenire is the right fit. Send
the short version by email first if that's easier — the next step is a
focused architecture call.

The booking calendar is the inline Cal.com embed on the page. Prefer
email? hello@invenire.com.au.

### Channels

- **email** (hello@invenire.com.au) — Primary channel. Best for project enquiries, scoping calls and anything you'd like a written trail of.
- **linkedin** (linkedin.com/in/arvesolland) — Track record, recommendations and the longer history.
- Founder-led. You work directly with Arve.

### What to include in your first message

To make the first conversation useful, include:

- The system, workflow, or opportunity you are working on
- What already exists: idea, prototype, production system, team, data, or codebase
- What is currently blocking progress
- What changes if this works
- Your timeframe
- Your rough budget range or commercial constraints
- Whether you need strategy, implementation, review, or team enablement

### Fit

**Good fit:**
- You have a serious AI opportunity and need senior technical judgement.
- You are moving from prototype to production.
- You need evals, telemetry, architecture, or review before scaling.
- You want a small senior studio, not a large consulting team.
- You value working software over slide decks.

**Not a fit:**
- You want a generic chatbot with no clear business owner.
- You need low-cost development capacity.
- You are looking for AI theatre rather than operational change.
- You are not ready to give access to the people, systems, or context required to do the work properly.

### Scope and investment

Most work starts with a focused diagnostic, architecture review,
advisory engagement, or build sprint. Scope and investment are discussed
once there is enough context to understand the problem properly.

Invenire is a small, founder-led studio and only takes on a limited
number of engagements at a time. The best fit is work where senior
engineering judgement matters.

### Quick answers (FAQ)

- **q / capacity — Are you taking new engagements?** Yes — limited capacity. Typically 1–2 clients at a time.
- **q / location — Do you work remote or onsite?** Both. Remote default; onsite trips possible globally.
- **q / minimum — What's the minimum engagement?** Usually a diagnostic sprint or focused build sprint. Smaller scopes by exception.
- **q / nda — Do you sign NDAs?** Yes, for serious conversations. Send yours or use ours.

### Response time

**Replies within 48 hours.** Australian business days. If it's urgent,
say so in the subject line and we'll prioritise.

→ hello@invenire.com.au

---

© 2026 Invenire · Gold Coast, AU