Services

Three areas of work. One standard for evidence.

We build the product, the platform underneath it, and, where it helps, the governed use of AI on top. Each section below sets out who it's for and exactly what you receive.

01Product engineering

Fintech product engineering

We build and extend brokerage, wealth and investment platforms: the account models, order flows, ledgers and integrations underneath the client experience. Every state change is recorded in a form an auditor can follow.

Who it's for

  • Brokerages and wealth managers modernising a core platform
  • Fintechs building on top of regulated custody or banking partners
  • Engineering leaders who need senior capacity for a defined build
Fig. 02 — Audit trail in the data modelIllustrative
1 · Order request2 · Validation3 · Execution venue4 · Settlement
Append-only event logwho · what · when · whyno updates, no deletes

Each step writes an immutable event before the next one runs, so any order can be replayed for a client, compliance or regulator request.

Deliverables

  • Core platform services

    Account, portfolio, order and cash-management services with clear domain boundaries.

  • Partner integrations

    Custodians, execution venues, market-data providers and banking partners, with retries, idempotency and reconciliation.

  • Audit trails in the data model

    Append-only records of every state change, queryable for client, compliance and regulator requests.

  • Reconciliation and exceptions

    Daily position and cash reconciliation, with exception queues your operations team can work from.

  • Client, adviser and back-office tools

    Web applications built on the same services and permissions model.

  • Tests, decisions and runbooks

    Automated test suites, architecture decision records and operational runbooks handed over with the code.

02Platform and compliance

Platform and compliance engineering

We design and run cloud foundations where controls are enforced in code rather than in spreadsheets, so audit evidence is produced by normal engineering work.

Who it's for

  • Firms moving regulated workloads to AWS
  • Teams preparing for a SOC 2 audit or a regulator inspection
  • Platform teams who have outgrown a single account or cluster
Fig. 03 — Layered controlsIllustrative
Organisation guardrailsSCPs
Account baselineIAM · Config
Network boundaryPrivate only
Workload policyOPA · GitOps
Records retentionWrite-once

A change passes every layer on its way to production. Any layer can reject it, and every decision is logged as evidence.

Deliverables

  • Multi-account AWS

    Landing zone with separated workload, security and log-archive accounts, service control policies and central identity.

  • Kubernetes and GitOps

    Clusters where every change is a reviewed commit, with a deploy history you can hand to an auditor.

  • Private connectivity

    PrivateLink, transit networking and partner VPNs, so regulated traffic never crosses the public internet.

  • Policy-as-code

    Guardrails written as tested code, applied in the pipeline and checked continuously in each account.

  • Records-retention storage

    Write-once storage with retention periods set to your regulatory and contractual obligations.

  • Audit-evidence readiness

    Control mapping, automated evidence collection and SOC 2 readiness preparation ahead of your independent audit.

We prepare you for an audit. Certification and attestation reports are issued only by your chosen independent auditor.

03Applied AI

Applied AI for financial services

A focused offer for firms that want to use language models without losing track of what was sent, what came back and who approved it.

Who it's for

  • Firms evaluating LLMs for internal tooling
  • Risk and compliance teams writing an AI usage policy
Fig. 04 — Governed model accessIllustrative
Internal toolAI gateway · policy, redactionPrompt + response logPrivate endpointModel provider

Every request goes through one gateway that applies your usage policy and keeps a reviewable record.

Deliverables

  • Secure LLM integration

    Models reached through private endpoints, with prompt and response logging and data-loss controls.

  • AI usage governance

    Usage policy, model inventory and a review process your risk committee can run.

  • Workshops

    Half-day sessions for engineering, risk and compliance teams on practical, governed use.

Next step

Not sure which area fits?

Describe the system and the obligation it has to meet. An engineer will reply with how we'd approach it, or who would be a better fit.