The Sharedo Audit
Configuration, compliance, key dates, security, and the book itself — extracted from the data, measured against the standards, and returned as findings you can act on and defend.
Illustrative composite — synthetic demonstration corpus. Real scores are computed per engagement from disclosed indicators.
Why this audit
This is not a five-minute health check. It is the productised form of a deep-insight capability proven end-to-end on a live production engagement — extraction, AI lenses, evidence trails, and a disclosed health score.
Every headline number traces to an extraction-script output with an as-of date. Nothing is asserted that cannot be shown.
The AI lenses run on an on-premise stack — a local model on local hardware. Client data never leaves a controlled environment.
Observed measures are separated from model-derived inferences, and configuration deviations cite a specific rule identifier — defensible to a board or a regulator.
Under Alterspective Assure the pipeline re-runs on a schedule, so you watch a health-score trend rather than commission a one-off snapshot.
The offering
The tiers ladder by data sensitivity — the entry tier touches no matter content at all, so it starts fast; each step up reaches deeper into the book and justifies more authority.
Data: Configuration only — no matter content, no personal data
Work types, phases, forms, option sets, and permissions measured against our Sharedo configuration standards. Dead and sparse fields, mixed-representation defects, and orphaned config surfaced. The fastest tier to authorise — it touches no matter data.
Data: Admin and audit APIs — staff activity, no client matter content
Who actually uses the platform: logins, feature and portal usage, and how the workload is distributed across fee-earners. Concentration measured with a Gini coefficient, dormant licences identified.
Data: Matter titles, key dates, phase history — full authority gate
What the book actually is versus how it is configured. Velocity, stuck queues, reopen rates, and limitation triage — plus embedding-based segmentation that finds the distinct kinds of work hidden inside a single configured type.
Data: ODS parties and connections — full authority gate
Operational Data Store (ODS) entity duplication with a no-auto-merge guardrail, connection integrity, inactive-party hygiene, practice-management-system reconciliation, and acted-both-sides screening signals.
Data: Free-text file notes — the most sensitive corpus
Mines file notes for reserves, offers, and deadlines with an audited error rate, and adds the interactive application — ask your own book questions, with citations that check out. Layers on top of a Tier 3 or Tier 4 engagement.
Data: Annual subscription — scheduled delta re-runs
The scripted pipeline re-runs on a schedule, so you buy the delta: what changed since last quarter, and a health-score trend the board can watch. Every re-run defends the last quarter's remediation.
Tiers marked roadmap are proven on a live engagement and are being generalised into the repeatable pipeline; Configuration and Lifecycle lenses are available in the pipeline today.
Six audit domains
Select a tier to see which domains it covers, or open any domain for the specific checks, the method, a worked example, and the business benefit.
6 audit domains
The form-field estate, option-set hygiene, and orphaned config — measured against 19+ active Sharedo configuration standards.
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Can your Sharedo platform evidence the anti-money-laundering obligations that now apply to Australian law firms?
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The difference between a limitation date that is recorded and one that is actually managed.
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Who can do what, where the walls leak, and which privileged accounts should be switched off.
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What the book actually is, versus how it was configured — found by reading the titles, not the taxonomy.
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A cleaner party register — with a guardrail that refuses to merge two entities that only look alike.
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Ready to look closer
From the authority gate to the delta re-run, the method is transparent end to end. The sample report is a full audit rendered on synthetic demonstration data.