Field-deployed intelligence, not outsourced AI delivery. Getting an AI to demo is easy; making it real — true in production, trustworthy as it changes, safe to build a business on — is the hard part, and the whole of forward-deployed engineering. Arc makes it real and stands behind it: research and engineering deployed into your reality, not systems integration.
Forward-deployed engineering is not Arc's main line — it is the house's own research-to-production engine, turned outward. Arc builds its own frontier; it lends that engineering because good work should reach production, not stall at the demo.
WHAT ARC DOES
Making it real is the hard part.
Anyone can get an AI to demo. The hard part — and the whole of forward-deployed engineering — is making it real: a system that holds true in production, stays trustworthy as it changes, and is safe to build a business on. The distance between a thing that demos and a thing you can trust is where Arc works.
Often what you have is not a product yet but the makings of one — a research judgement, a prototype, a model capability, or simply your own domain knowledge, research, and data. You bring the domain; Arc makes it a real intelligence system — one you can use, validate, govern, deploy, and iterate.
Arc does not hand you a demo and walk away. It engineers the boundary between the deterministic and the non-deterministic until the system can be trusted — and then it stays behind it, so the system stays real as it changes: as the model is swapped, the data drifts, the workflow grows.
Arc makes AI real — and stands behind it.
IS THIS YOU?
Four ways a problem reaches Arc.
A shaky system, a demo, a gap you can't fill, or a frontier no one has crossed — four starting points, one destination: real.
You have it — just not real yet
Harden. It demos, but wobbles in production — confident wrong answers, an agent that drifts. Arc makes it stable: trustworthy under real load, not just the happy path.
IN a system that wobbles in production→OUT a stable system, trusted under load
Productise.It's a prototype, not a product. On Arc's software-engineering foundation it crosses the demo→production gap — measured, maintainable, safe to run and to serve.
IN a working demo / prototype→OUT a maintainable, measured production system
You don't have it — and need it real
Enable.A capability you can't build alone — pose-and- rehabilitation assessment, affect-aware interaction, traceable document intelligence. Arc deploys what it already holds, into your system.
IN a capability gap (Arc holds the tech)→OUT the capability, deployed into your system
Strategic R&D.A capability that doesn't exist yet. Arc invents it — research carried all the way to a production system, the method kept.
IN an unsolved research problem→OUT the invented capability + the method, built in
Not Arc's work: a plain chatbot, a dashboard, a database or API migration, automation with no AI crux. Where the path is clear, an integrator will do — Arc takes what must be made real.
HOW AN ENGAGEMENT WORKS
The build-with-you centre.
Forward-deployed engineering is the build-with-you centre of Arc's engagement spectrum. The four faces above run as one capability — research-to-production, compressing whatever you bring into a system you can use, validate, govern, deploy, and iterate. All four are the same work underneath: engineering the boundary between the deterministic and the non-deterministic.
The ladder
Bounded and gated — each step earns the next, and you don’t spend ahead of proof.
AI System Diagnostic
2–4 weeks · the entryFor an existing system you cannot yet trust. Low-risk; it filters and shows depth before any build.
You keep: architecture diagnosis · failure-mode map · recommended architecture · evaluation plan · a go / no-go.
Deployment Sprint
6–10 weeks · one buildFor a validated direction ready to build, carried across the demo → production crossing.
One bounded build — a slice carried to running, not a whole product shipped in ten weeks. A real productisation runs as a sequence of these, each gated on the last.
You keep: a running prototype or internal system, with the evaluation harness, inside your environment.
Governance Retainer
ongoing · holdFor a deployed system that must stay trustworthy as it changes.
You keep: evaluation regression · agent monitoring · failure review · model migration · workflow hardening.
Engagements are scoped to value, not sold by the hour. Terms are set per engagement.
WHAT YOU KEEP
A system, and the instruments to trust it.
Four capabilities a generic shop cannot bring — and the artifacts they leave behind.
- Problem definition
- System architecture
- Evaluation & failure diagnosis
- Governance & trust
What you keep
- A failure map and risk register.
- A recommended architecture.
- An evaluation harness and regression loop.
- A governance gate.
- A running system in your environment.
- A field report.
THE METHOD
From your start to trusted, step by step.
- 01
Establish the ground
What is real, and what must be made real — for a system in hand, what the Diagnostic found; for a capability you don't yet have, the research or held technology that supplies it.
- 02
Boundary mapping
What must be deterministic, what may stay probabilistic, what needs evaluation, what needs governance.
- 03
Substrate intervention
Supply the missing knowledge substrate, evidence layer, and document structure the answers must stand on.
- 04
Evaluation harness
Build the eval set, the failure taxonomy, and the regression loop — measurement brought in at the start, not bolted on.
- 05
Governance gate
Build the supervision, attestation, and permission boundary — trust by construction.
- 06
Deployment artifact
Deliver a running system inside your environment — not a slide.
DATA · IP · ENVIRONMENT
Arc goes to the data.
By default the work is client-side: Arc brings the method and tooling into your environment; your data, infrastructure, and operational control stay yours. From lightest to deepest — bring the method; bring the toolkit into your environment; an Arc control-plane over your data-plane; or fully managed, only where the data is low-sensitivity. Decisive for law, finance, health, and government.
The split is fixed before work begins: you own your data and your deliverables; Arc retains its pre-existing technology, methods, and general, non-client-specific learnings. ArcSoft Pty Ltd signs and holds the commercial terms; Arc Intelligence brings the people and the judgement.
WHY ARC
Why Arc, not an integrator.
Arc's edge is engineering the line between the deterministic and the non-deterministic — making deterministic what can be, and measuring, calibrating, and governing what must stay probabilistic. An integrator connects services; a model vendor ships a model; neither holds the substrate, the evaluation, and the governance that make a non-deterministic system trustworthy. Arc does.
Models make AI possible. Arc makes AI real.
A stronger model does not erase these problems — it sharpens them: more capability, more permission, more hidden risk. Arc builds the layer serious deployment needs, and over time defines what a grounded answer, a deployable system, and an evidence-bearing result actually are.
WORK THAT BACKS ARC
What stands behind the work.
Facts tell more than word-of-mouth. Arc is early by design; what stands behind the work is the record — the patents it has filed, the research it has published, and the flagship systems it brings to every engagement.
Patents
Provisional patents on the retrieval, evaluation, and governance methods.
The IP→Research
Notes on the failures FDE is built to catch — found-but-unusable chunks, evidence grounding, evaluation-first measurement.
The archive→Systems
The flagship systems the engagement carries in — Hunter-RAG, Lancet, Cawsal.
The portfolio→APPLY
Most engagements begin with a Diagnostic.
Engagements are selective and problem-led — they begin where a genuinely hard AI problem meets a domain in which being wrong is costly: law, finance, health, research, regulated software. They come through referral, research alignment, or real technical need. Write to us with a concise account of the problem.