ShiftCurveEnterprise IntelligenceFinancial Services

Enterprise AI starts with the data, not the model.

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.

Start the conversation Read the Approach

The Honest Diagnosis

Why most enterprise AI in financial services fails.

Three named failure modes. We're not gentle about why each one is wrong, and we're explicit about what we do instead.

The Big 4 trap

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.

The AI-bolted-on trap

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.

The agentic-everything trap

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

Three layers. Built in order.

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 Data Foundation

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.

Connector architecture into your existing systems of record
Automated reconciliation across sources with exception reporting
Time-series storage for full backward-view reconstruction
Audit trail by default: every record, every change, every actor
RBAC, SSO, and data residency controls from day one

Layer 02

The Deterministic Spine

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.

Workflow orchestration with deterministic control flow
Tool and executor sandboxing with strict scope boundaries
Idempotent retries, transactional state, full replay capability
Approval gates for high-stakes actions
Full audit logging: every decision, every input, every output
Model routing by task class and data sensitivity

Layer 03

Scoped LLMs

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.

Prompt scoping discipline: every production prompt has a bounded contract
Model routing by task class: frontier models for complex reasoning, smaller models for classification, on-prem models for sensitive data
Eval harness for every production prompt, not vibes
Hallucination guardrails: outputs validated against the verified data foundation
On-prem inference option for fully air-gapped environments

Why the deterministic spine matters in regulated work

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

The problems we recognise.

If two or more of these describe your situation, we should talk.

01
“We have multiple systems of record that disagree, and our team is reconciling them in spreadsheets.”
02
“The AI features in our existing platforms describe one slice of our data. We need something that reasons across systems.”
03
“We're considering a multi-year platform RFP for something that won't ship in time, and we don't want to wait.”
04
“We have an AI mandate from the board, but our risk and compliance functions don't trust the agentic-AI vendors.”
05
“We need AI that can be audited end-to-end and certified for regulated use.”

AI-First Delivery

Why we ship faster than the alternatives.

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.

How we compress the timeline.

We coordinate through OpenClaw

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.

Senior team, no offshore handoffs

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.

AI-native engineering tools

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-gated delivery

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.

See the full delivery model

Governance & Compliance

Built for the regulation, not retrofitted.

Specific to NZ and AU financial services regulation. Not generic enterprise-grade vapourware.

NZ Privacy Act 2020

Native data residency controls, consent management, breach notification handling. Built for the regulation, not retrofitted.

FMA expectations

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.

APRA CPS 230 / 234

For Australia-touching firms: operational risk management, data security, third-party risk, supplier register integration.

SOC 2 trajectory

SOC 2 Type II in progress on the orchestration platform. Architecture is SOC-2-shaped from day one, not a retrofit project.

Deployment options.

Fully on-premises

Open-source models, dedicated hardware on your network, no external API calls. Sensitive data never leaves your building.

Enterprise API

Frontier models on enterprise tier with zero data retention DPAs. SOC 2 Type II infrastructure, signed data processing agreements.

Hybrid

Sensitive data processed locally, general reasoning routed to cloud models. Configurable per task and per data class.

What we don't do

A short list, plainly stated.

Train on your data
Sell your data
Keep your data more than 30 days after an engagement ends, except where the law requires it or for our audit records
Use models without contractual zero-retention

Engagement Model

Phased delivery. Real artefacts at every stage.

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
Data Foundation
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
Orchestration + Scoped LLMs
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
Hardening + Handover
Penetration testing, regulatory review, UAT, training, runbooks, monitoring. Then either handover to your team under the licence set out in your Schedule, or an ongoing retainer for new modules and continuous improvement.

Why Us

Three things that make this work.

We've sat in your seat.

22 years 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.

AI-first as we scale.

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.

Vendor-neutral.

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

Who this is not for.

We're going to be honest about who we're a fit for and who we're not. It saves everyone time.

Retail-facing fintech. We're institutional and enterprise.
Firms looking for a cheap AI-bolted-on demo. We build foundations.
Organisations that want a 200-page requirements document before any code is written. That model is what we're explicitly replacing.
Crypto, personal finance, or consumer-facing financial products.
Engagements where the data foundation work isn't taken seriously. Without that, nothing else works, and we won't pretend otherwise.

Start with a conversation.

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.

Start the conversation