Most AI projects in regulated financial services fail before the model is even called. The data is fragmented. The systems of record disagree. The governance to deploy AI safely doesn't exist. We solve that first.
The Honest Diagnosis
Three named failure modes. We're not gentle about why each one is wrong, and we're explicit about what we do instead.
Twelve-month discovery. Two-hundred-page requirements doc. RFP. Vendor selection. Eighteen-month build. By the time it ships, the requirements have changed, the sponsoring exec has moved on, and the system is another silo. Three to four year cycles for monolithic platforms ship into a market that has moved.
Established vendors are adding AI commentary or assistants to platforms that hold one slice of your data. The AI describes that slice. It doesn't unify, doesn't reconcile, doesn't reason across systems. You're paying for a feature that solves nothing because the underlying data is still fragmented.
A new wave of vendors is selling autonomous AI agents that 'do everything.' In regulated financial services, letting an LLM decide control flow is the wrong model. You can't audit it. You can't certify it. You can't put it in front of a regulator.
The Approach
The data foundation gets built first. The deterministic spine controls everything that runs on top. LLMs sit at the edge, doing scoped reasoning work over verified data. This is the architecture that makes enterprise AI auditable.
Layer 01
The unglamorous prerequisite. Built first.
Most financial services firms have multiple systems of record that disagree by latency, by reconciliation gap, or by integration design. Before any AI is useful, the data has to be unified, reconciled, and trusted. We build this first, using AI-native engineering tools that ship in months instead of years.
Layer 02
The orchestration layer is code, not an LLM.
The part nobody else articulates. Workflow control, retries, error handling, audit logging, and approval gates are deterministic code. LLMs are called as bounded tools by the spine. They don't drive. This is what makes the system auditable, certifiable, and regulator-ready — and it's the difference between a demo and production AI in regulated environments.
Layer 03
LLMs do what they're best at — bounded reasoning over verified data.
Each LLM call has a tightly scoped role: explain this attribution, classify this exception, draft this report section. Model choice is interchangeable based on the task and the data sensitivity. We don't lock you into a single AI vendor, and we don't let LLMs touch unverified data.
In regulated financial services, you need to explain to a regulator (FMA, RBNZ, APRA) exactly what your AI did, why, and on what data. The deterministic spine makes that possible. Pure agentic systems do not. This is why we built the architecture this way, and why we won't compromise on it.
The Pattern
If two or more of these describe your situation, we should talk.
“We have multiple systems of record that disagree, and our team is reconciling them in spreadsheets.”
“The AI features in our existing platforms describe one slice of our data. We need something that reasons across systems.”
“We're considering a multi-year platform RFP for something that won't ship in time, and we don't want to wait.”
“We have an AI mandate from the board, but our risk and compliance functions don't trust the agentic-AI vendors.”
“We need AI that can be audited end-to-end and certified for regulated use.”
AI-First Delivery
Enterprise data foundations and AI orchestration are months of senior engineering. Anyone who tells you otherwise is selling a demo, not a system. What we change is the alternative.
Big 4 / traditional consulting
18 to 24 months
12-month discovery, 200-page requirements doc, 18-month build. By the time it ships, the requirements have moved.
Monolithic platform RFP
3 to 4 years
Selection, procurement, implementation, go-live. You're solving today's problems with delivery years out.
ShiftCurve
Months, not years
Phase 1 lands a working data foundation you can audit before committing to phase 2. No big-bang. No 18-month wait.
The same multi-agent orchestration platform we deploy to clients is how our team itself runs. Engagement state, shared context, synthesis, and AI-multiplied work all live in the orchestration substrate — not in Slack threads, Notion docs, and email forwards.
We dogfood the deterministic spine before we sell it. If you want to see what enterprise AI orchestration looks like in practice, you're already looking at it — it's how this engagement will run.
The people scoping the problem build the solution. Decisions land in shared context the same day they're made. No 200-page requirements doc, no waterfall handoff, no learning your business on your dime.
We pay for them. They fund our margin, not yours. We're transparent about that — it's why we can ship enterprise-grade work in months instead of years, and it's why our pricing reflects modern build economics rather than legacy consultancy day rates.
Phase 1 lands a real working artefact you can evaluate before committing to phase 2. Go/no-go gate at every phase boundary. You can stop at any gate. We don't ask you to commit to a multi-year build before any code has been written.
Governance & Compliance
Specific to NZ and AU financial services regulation. Not generic enterprise-grade vapourware.
Native data residency controls, consent management, breach notification handling. Built for the regulation, not retrofitted.
Audit trail by default. Full reconstruction of any AI decision from inputs, prompt, model version, and output. Aligns with the FMA's evolving expectations on AI in advice and investment workflows.
For Australia-touching firms: operational risk management, data security, third-party risk, supplier register integration.
SOC 2 Type II in progress on the orchestration platform. Architecture is SOC-2-shaped from day one — not a retrofit project.
Open-source models, dedicated hardware on your network, no external API calls. Sensitive data never leaves your building.
Frontier models on enterprise tier with zero data retention DPAs. SOC 2 Type II infrastructure, signed data processing agreements.
Sensitive data processed locally, general reasoning routed to cloud models. Configurable per task and per data class.
What we don't do
Engagement Model
Each phase ends with a working deliverable you can evaluate before committing to the next. Go/no-go gates between every phase. You can stop at any gate.
Phase 1
Connectors, reconciliation, time-series storage, audit, RBAC, deployment. The unglamorous prerequisite for everything else. Lands a working data foundation you can audit before committing to phase 2.
Phase 2
Deterministic spine, workflow orchestration, executor sandboxing, eval harness, the bounded LLM capabilities your team actually uses. Built on the verified data foundation from phase 1.
Phase 3
Penetration testing, regulatory review, UAT, training, runbooks, monitoring. Then either clean handover to your team, or ongoing retainer for new modules and continuous improvement.
Why Us
Two decades inside the systems we now build alternatives to. Charles River implementation for an NZ asset manager. Margin lending platform design at a major NZ broker. Cash management at a global bank. Post-trade STP across multiple platforms. We know where the data breaks happen because we've lived inside these systems.
Open-floor team coordinated through OpenClaw, AI-multiplied senior engineers, no offshore handoff overhead. The architecture we deploy for clients is the architecture we run our own work on. Same substrate, same discipline.
We don't resell Snowflake. We don't have a kickback from Anthropic. We're not a Microsoft Gold Partner. We pick the right tools for your problem and tell you why. If a frontier model is the right call we'll say so. If an open-source model running on-premises is the right call we'll say that too.
Filter
We're going to be honest about who we're a fit for and who we're not. It saves everyone time.
Not a price sheet. Not a brochure download. A conversation about your data, your systems, and what you're trying to make AI do for your firm.
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