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Matter economics intelligence across your firm. Review delivery and billing patterns, identify matters that need attention and see the evidence and confidence behind each signal.
Matter-level
evidence and attribution
Independent
vendor-neutral measurement
Confidence-aware
signals and unresolved gaps
Enterprise
governed data boundaries
84%
Strong enough for review
Activity connected to economics
Decision signal
Pricing review recommended. Two attribution gaps remain open.
See which AI signals link to a matter and work context, which sources are missing, and where attribution needs review.
Trace each signal to its source and attribution path. Separate observed activity from inferred effects before using it in a decision.
Bring matter hours, billing, and comparable work alongside AI evidence when the record supports a pricing or post-matter decision.
Compare matters and work patterns using your firm's own data, with the limits of each comparison made visible.
Identify matters with strong evidence, conflicting signals, or missing context so teams can focus validation where it matters.
A supporting view for partners and legal operations teams: monitor evidence coverage without turning uncertain signals into firm-wide claims.
Independent visibility into AI tool signals, attribution coverage, and shadow IT. See what each source contributes to the matter evidence record, what remains unresolved, and where validation is needed.
Market context below is attributed. We publish no firm-level results we cannot substantiate.
Usage
activity recorded by connected sources
Spend
license costs supplied or available from connected data
Coverage
sources and records represented in the review
Gaps
unresolved or unavailable evidence
Assigned seats and recorded usage may not tell the same story. Compare the license and activity data available to you, and review the limits before making renewal decisions.
When every AI vendor claims ROI, firms need an independent view of what the available evidence can support. Alpodo connects tool activity to matter and billing context without relying on vendor claims.
References to tools in available sign-in, vendor, or billing data may identify activity that warrants follow-up. These signals are not a complete inventory or a compliance finding.
Portfolio fields and measures depend on the data available from each connected source.
Microsoft Copilot
187/240 active
£86,400/yr
SpendHigh
Seat signalLegal Research AI
41/45 active
£67,500/yr
SpendHigh
Seat signalDocument Review AI
4/12 active
£18,000/yr
SpendLow
Seat signalBrief Analysis AI
22/30 active
£36,000/yr
SpendModerate
Seat signalAI Assistant
2/8 active
£19,200/yr
SpendLow
Seat signalBring available license, seat, renewal, and spend records into a portfolio view. Coverage depends on the sources connected and the data supplied.
Compare assigned seats with recorded activity where both are available. Differences can prompt review, but do not by themselves establish waste or underperformance.
Where a suitable and sufficiently supported comparison set exists, compare spend and utilisation with appropriate context. Peer benchmarks are not available for every firm or measure.
Review how available vendor activity may relate to matters, what economic context is available, and where the evidence remains incomplete.
Review connected M365 sign-in signals, vendor data, or billing references where configured. These may surface activity for investigation, but do not establish unauthorized use on their own.
Monitor connected sources, missing records, and unresolved tool signals within your firm's data boundaries.
2 detected
AI tools with active user sessions but no license record. Potential compliance and spend leakage risks flagged automatically.
4 tools found
AI tool references in billing narratives and time entries that don't appear in vendor APIs or M365 logs. Complete visibility into off-radar adoption.
5 tools
Tools with connected license, usage, and billing signals: integrated into the Alpodo evidence ledger with coverage and unresolved gaps visible.
We connect to your existing stack and show you what the available data supports, what remains unresolved, and where validation is needed. In one session.