AI should earn its place in your business

Before anything is built we establish whether AI is the right response at all. The output is a decision document with numbers in it, not a slide pack.

• Process walkthroughs with the people who do the work, capturing current handling time, volume and rework rate as the baseline every later claim is measured against.
• A use-case register scored on business value, data readiness, risk exposure and implementation effort, with a recommendation to fund, defer or decline each entry.
• A readiness review of identity, permissions, document hygiene, data quality and integration surfaces, listing what must be fixed before a build can start.
• A costed business case covering build, run and licence cost, including inference or token estimates at expected and peak volume, with every assumption stated.
• A responsible-AI review naming the decision each system influences, the people affected and the human approval points that must exist.
• A written recommendation where automation, reporting or a process change would deliver more than a model would, including the cases where we advise against building anything.

We build in increments, and each increment is something a real user can try. Reference patterns are reused across engagements so you are not funding invention where a known pattern will do.

• Retrieval design covering chunking strategy, metadata, permission filtering at query time and index refresh cadence, tested against your actual document set rather than samples.
• Model selection benchmarked on your own tasks, with accuracy, cost and latency recorded per candidate and the architecture kept deliberately model-swappable.
• Prompts, tool definitions and orchestration logic held in source control, versioned and deployed through the same pipeline as the rest of the application.
• Agents scoped to an explicit tool schema with validated arguments, and every write into a business system gated behind confirmation or approval.
• Predictive models built with a documented feature set, a holdout evaluation and a retraining trigger defined before release rather than after drift appears.
• Integration through governed APIs and event-driven workflows instead of direct database access, so the AI layer can be replaced without touching the source system.

AI inherits every access problem your environment already has and adds a few of its own. Identity, data classification and prompt-level attack surface are treated as build work, not as a review at the end.

• Identity-based access so an assistant returns only what the signed-in user is already entitled to see, verified by testing retrieval with accounts at three different permission levels.
• Sensitivity classification and data-loss controls applied to the content in scope, with a documented list of repositories deliberately excluded and the reason for each exclusion.
• Prompt-injection defence covering untrusted content inside retrieved documents, tool allowlists and output filtering, with adversarial cases kept permanently in the evaluation suite.
• Logging of prompts, retrieved sources, tool invocations and approvals, correlated so a specific answer can be reconstructed during a review.
• A human approval point defined for every action with financial, contractual, safety or client-facing consequence, and enforced in the workflow rather than in a policy document.
• Support in assembling the evidence and control mappings your organisation needs when aligning to frameworks such as the Essential Eight or the Australian Privacy Principles.

An AI system people do not trust is shelfware with a licence attached. Adoption is planned from the first workshop and measured after go-live against the baseline we captured before it.

• Role-based enablement sessions built around the tasks each team actually performs, not a generic tool demonstration.
• A published statement of what the system is for, what it must not be used for, and what to do when an answer looks wrong.
• A pilot group with named participants, a working feedback channel and a fortnightly review of failed or unhelpful responses.
• Champions identified in each business area and briefed on the limitations as carefully as on the capabilities.
• Prompt and scenario libraries written in your terminology, covering the handful of tasks each role performs most often.
• A post-go-live measurement against the assessment baseline, reported to the sponsor with the original assumptions restated so the comparison stays honest.

Model behaviour, source content and user expectations all move after launch. We stay accountable for the system rather than handing over a repository and a diagram.

• Scheduled re-runs of the evaluation suite, with results trended against the previous baseline and regressions raised before users find them.
• Monitoring of latency, error rates, consumption and retrieval quality, with alerting on thresholds agreed at design time.
• Tracking of model deprecations and platform changes across Microsoft and AWS, with a tested migration plan prepared ahead of any retirement date.
• Content and index health checks covering stale documents, failed ingestion and permission drift after staff or project changes.
• Cost review against the business case, with per-feature attribution and a recommendation where spend is not earning its place.
• Monthly reporting to the sponsor covering usage, answer quality, incidents, cost and the next improvement we recommend.