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
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.
- deliverable
- Architecture review — current systems, proposed shape, integration boundaries.
- deliverable
- Opportunity map — where AI is a real lever for the business, where it is not.
- deliverable
- Model and tool recommendations — which providers, frameworks, and patterns to bet on.
- deliverable
- Risk register — technical, operational, and commercial risks worth tracking.
- deliverable
- Build, buy, or partner recommendation — with the reasoning written down.
- deliverable
- Phased implementation plan — sequencing, milestones, and decision gates.
good fit when
Good fit when there is pressure to "do something with AI", but the team needs a clear technical path before spending serious money.
02 / production-ai-systems
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.
- deliverable
- Working production system — agents, retrieval, vision, or multi-modal as required.
- deliverable
- Architecture and integrations wired to your data, services, and identity model.
- deliverable
- Eval harnesses and telemetry — quality, cost, latency, and failure signals.
- deliverable
- Deployment path, handover notes, and an improvement backlog the team can run.
good fit when
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.
03 / evals-and-instrumentation
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.
- deliverable
- Golden dataset curated against real inputs, ranked by failure cost.
- deliverable
- Scoring rubric and LLM-as-judge setup where appropriate, calibrated to human review.
- deliverable
- Regression tests and release gates — PRs that regress quality fail CI.
- deliverable
- Trace review, model comparison, and a failure taxonomy you can act on.
- deliverable
- Cost and latency tracking — per call, per tool, per release.
good fit when
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?"
04 / ai-native-engineering
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.
- deliverable
- Workflow design and tool selection — which agents, editors, and orchestrators, with the rationale written down.
- deliverable
- Team training on coding-agent patterns, context discipline, and review standards.
- deliverable
- Eval-first development practices and repo-specific playbooks.
- deliverable
- Pilot implementation on a real workstream, with pairing on real PRs.
good fit when
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.
why invenire
Five things the field cannot fake.
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20+ years
shipping_production
- Two decades putting software in front of real users — enterprise, government, and research environments. The reflexes that matter at 3am exist because they were earned.
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Multiple AI systems live
in_production_now
- Analytics Agent, national-scale ecological modelling infrastructure, confidential agentic systems for ML experimentation and audit-grade data work.
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Founder-led
what_i_do
- One operator on the work end-to-end. No account-management layer between the conversation and the code.
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Production-first
engagement_posture
- I work in your codebase, with your data, against your constraints. The system goes live before I leave — not after a handover deck.
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AU · GMT+10
based_in
- Gold Coast based, remote default, comfortable across timezones. Onsite trips on the bigger engagements.
engagements
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 and 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.
who I work with
Four shapes of team.
commerce
E-commerce and ops teams
Shopify merchants, brands, and agencies that want analytics, content, or store-ops automation that actually closes the loop. I know the Admin API and what it takes to ship through review.
climate
Climate, geospatial and regulated
Multi-LLM and vision pipelines over spatial data, MRV verification, document intelligence — domains where audit-grade output and decision provenance are part of the contract, not an afterthought.
founders
Founders and CTOs
Short, sharp engagements: architecture review, model selection, getting an in-house prototype across the line into production. The room where the bets get placed.
engineering
Engineering teams going AI-native
Teams that want Cursor, Claude Code, and agentic workflows to be a practice with a floor — not a per-engineer lottery. Tooling, evals, internal patterns, pairing.
selected work
Production systems. Not slides.
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SHOPIFY · LIVE · 2025–
Production Shopify analytics product. Six core analysis agents coordinated by a router to detect anomalies, surface opportunities, audit GA4 configuration, and produce a plain-English Mission Brief.
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RESEARCH INFRA · MULTI-YEAR
National-scale ecological and environmental modelling infrastructure. Reproducible modelling across nine partner institutions and a multi-year funding environment.
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AUTONOMOUS ENG · 2024–
Autonomous engineering work exploring how one senior operator can coordinate AI agents, externalised state, objective verification, and multi-service software delivery.
founder
Arve Sølland.
PRINCIPAL_AI_ENGINEER · FOUNDER_LED
Twenty years shipping production software. The last several entirely on AI — orchestration, evaluation, retrieval, vision, and the engineering practices that decide whether a system is something the business can rely on. I stay close to frontier models and tools. I do not ship everything I find there.
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.