Enterprise AI, delivered inside your own tenancy.
Most organisations already own capable AI tooling. What stalls is turning generic licences into process-specific workflows that pass a governance review and can prove a result. We do that work in the place your data already lives — your Microsoft tenancy, your cloud perimeter, your access controls.
We extend what you already own. We do not ask you to replace it.
If your organisation runs Microsoft 365, we build on the Microsoft substrate — Copilot Studio, Azure AI Foundry and Power Platform — wherever it is sufficient, and only write custom components for data that genuinely sits outside it.
Why this matters commercially
- Your existing licences keep working. Nothing is ripped out or re-procured.
- Your IT function retains the platform relationship it has already invested in.
- Data flows through infrastructure your security team has already approved.
- No second AI platform to govern, fund or explain to the board.
Why this matters to a reviewer
- Deployment happens inside your tenant, under your identity and access model.
- Per-document access control and a complete audit trail on every agent action.
- Human-in-the-loop is mandatory for anything compliance- or contract-critical.
- We hand over a runbook and training so the capability stays in-house.
Audit first. Pilot second. Both measured.
A pilot without a baseline cannot prove anything, which is the most common reason internal AI programmes lose executive support. We establish the numbers before we build, and report against them afterwards.
AI Implementation Audit
3–4 weeks, paid, department-level. We inventory the real document and data landscape, map 5–8 candidate processes, assess current assistant adoption honestly, and check governance and data-readiness before anything is built.
Measured baseline
For the top 2–3 use cases we record time spent, error and rework rates and cycle times as they are today. This is the number the pilot will later be judged against — measured, not estimated.
Gated pilot
8–12 weeks, one process. One or two working agents integrated with your existing stack, demonstrable end-to-end, grounded on your data, with an explicit scale-or-stop recommendation at the end.
Capability transfer
Runbook, training pack and integration notes so your staff can operate and extend the solution. A vendor that cannot be replaced is a liability, not a partner.
What you receive from Phase 1
- Data & document landscape map — sources, volumes, formats, languages, access-control state.
- Use-case shortlist (5–8) with a scored prioritisation matrix across value, feasibility, data-readiness and governance risk.
- Baseline metrics pack for the top 2–3 cases.
- Governance & data-residency memo covering PDP Law, cross-border data, human oversight and audit requirements.
- Pilot specification with success criteria, scope and an effort estimate range.
- Executive readout for the sponsoring office — 30 to 45 minutes.
What a general-purpose assistant does, and does not, cover
General assistants are genuinely useful for summarising, drafting and meeting notes. They are much weaker at executing a specific process end-to-end against your systems of record. The table below is deliberately unflattering to the do-nothing option.
| Capability | General assistant alone | Assistant + purpose-built agents | Bespoke platform |
|---|---|---|---|
| Summarise & draft | Strong | Strong | Strong |
| Grounding on your documents | Partial, permission-dependent | Full, with per-document ACLs | Full |
| End-to-end process execution | Weak | Strong | Strong |
| Cross-system workflow (ERP, DMS, SharePoint) | Limited | Designed for it | Designed for it |
| Multilingual works documentation | Inconsistent | Tuned per language pair | Tuned |
| Audit trail & human oversight | Platform-level only | Per-action, designed in | Per-action |
| Data residency control | Follows your tenant | Inside your perimeter | Depends on vendor |
| Time to first measurable result | Varies, rarely measured | Weeks, against a baseline | Months |
| Cost model | Per seat, already paid | Marginal, on existing licences | New platform commitment |
Governance and data residency
Indonesian and European regulatory expectations are both in scope for organisations operating across those markets. We design for the stricter of the two rather than treating compliance as a later problem.
Data handling
- Work performed inside your tenancy and cloud perimeter wherever possible.
- No client documents leave approved jurisdictions.
- Aligned to Indonesia's Personal Data Protection Law (UU 27/2022) and its implementing rules.
- GDPR-consistent handling for EU-linked entities, including data-processing agreements before any access.
Control and oversight
- Per-document access control inherited from your existing permission model.
- Full audit trail of agent actions, queryable by your own team.
- Human-in-the-loop mandated for contract, compliance and financial outputs.
- Read-only or sample access in Phase 1 — no production writes during discovery.
What a first engagement realistically looks like
We are a small, independent firm. We are not going to tell you we can run an enterprise-wide transformation from a standing start, and we do not think you should buy one. The realistic entry point is a bounded, department-level piece of work that produces evidence.
Bounded scope
One department, one audit, one named business sponsor. Small enough to complete without a transformation programme, substantive enough to be worth reading.
Vendor onboarding respected
Security questionnaires, insurance and liability checks, DPAs and data-processing review are expected and planned for. We would rather spend the weeks up front than discover them later.
Deliverables are verifiable
Every Phase 1 output is a document or a measurement you can inspect and challenge. Nothing is contingent on taking the next phase.
Bilingual delivery
Deliverables in English as standard, with French and Bahasa Indonesia support where the working group needs it.
Start with the audit. Decide on the pilot after.
If you would like the scope, deliverables and timeline for a department-level AI implementation audit in writing, we will send them.