AI SaaS & custom software

Real AI products, end-to-end — not prompts pretending to be features

This is the work we’re known for. Fully automated AI SaaS platforms, from napkin sketch through production deployment. Multi-agent pipelines, human-in-the-loop review, payment and identity integrations, admin tooling, customer dashboards — all of it, shipped. Our flagship is ClaimDone.com.au, an AI legal-workflow product we designed and built. It’s in production, processing real matters, earning revenue.

What a full AI SaaS build looks like

• Multi-agent AI pipelines — orchestrated flows where specialised agents classify, extract, generate and verify. Claude, GPT, open-weights, or mixed; glued together with code you can read.
• Human-in-the-loop review — queues, audit trails, disagreement logging, override paths, re-training loops. Because “the AI gets it right most of the time” is the failure mode, not the success one.
• Integrations that earn their keep — Stripe / Airwallex for payments, identity verification (Frankie, GreenID, Sumsub), e-signature (DocuSign, Adobe Sign, PandaDoc), document generation, CRM and ERP hooks.
• Customer dashboards — self-serve UX with real-time status, history, permissions, team management, audit logs that users can actually read.
• Admin & ops consoles — the back-office tooling the team who runs the business actually needs. Case management, exception handling, model-output review, analytics.
• Evals, observability, cost controls — eval suites for every prompt, dashboards for latency / cost / accuracy, cost ceilings per account. Production-grade, not demo-grade.

How we scope it — three phases, stop between each

1. Discovery & prototype (4–6 weeks) — problem framing, user interviews, architecture spike, working prototype of the core loop. Fixed price. You can stop here with a functional demo and a plan, and never commit to the rest.
2. MVP (12–16 weeks) — production-grade core product, first paying users, integrated payments and identity, real eval suite, real observability. Fixed price. You can stop here with a live SaaS and a growing user base.
3. Scale (ongoing, quarterly milestones) — feature expansion, performance work, compliance uplift, additional integrations. Fixed scope per milestone; month-to-month retainer between them.

You don’t sign up for the full $500K on day one. You commit to phase one, and decide at each seam whether to continue.

Typical investment

• Focused internal tool (one workflow, one team, one integration) — from $80K, 8–12 weeks.
• Production SaaS MVP (multi-tenant, payments, auth, basic admin) — from $180K, 16–20 weeks.
• End-to-end automated platform (like ClaimDone) — around $500K, 6–9 months across three phases.

Every number above is all-in: design, engineering, project management, hosting setup, the works. Ad-hoc hours aren’t billed on top.

What’s new in the last 12 months

• Model Context Protocol (MCP) — Anthropic’s open standard for wiring LLMs into tools. We’ve built production MCP servers; most teams haven’t yet. Speed and reliability win.
• Long-context models (1M+ tokens) — changed which problems need RAG and which don’t. Fewer vector databases, simpler systems, where the use case allows.
• Agentic commerce — AI agents that complete multi-step transactions on behalf of users. Early, but real — we’ve shipped for a few clients already.
• Voice interfaces — realtime voice agents for support, triage and booking, built on infrastructure you own.

What we won’t do

We won’t ship a chatbot wrapper and call it a product. We won’t use “AI magic” to paper over undefined requirements. We won’t skip the boring observability work because “the demo looks great.” We won’t train on your customers’ data without their specific, informed consent. Reliability is what separates an AI product from an AI demo — and demos don’t make money.