Matter economics intelligence
Connect AI activity, work, time and billing to see what changed in delivery, and what that means for staffing, investment and price.
Recorded drafting time ran below the firm's historical reference for comparable matters.
Linked signals
128
Comparables
4
Needs review
2
AI activity, work, time and billing sit in different systems. That makes it hard to assess delivery effort, staffing and cost, or decide where further AI investment needs stronger evidence.
Signals can be grouped into legal work units and compared with available historical references. Evidence coverage and known gaps should be reviewed in context.
Evidence links depend on the connected sources and available records. Assumptions and coverage limits matter alongside any headline number.
Comparisons can use a firm's available historical matter data. Data access, processing, and storage depend on the agreed deployment and connected sources.
Link
Where source data permits, relate activity from AI tools and work systems to matter and billing context.
Review
Distinguish recorded activity, estimates, and human-reviewed attribution, with gaps visible.
Inform
Use available evidence to inform delivery, staffing, cost, AI investment and pricing decisions.
Illustrative reviews show how available evidence can inform delivery, investment and pricing questions. Source links and coverage require validation.
Review available AI activity, work, recorded time and billing together. Compare delivery effort, staffing and cost context, with evidence gaps visible.
See it on a sample matterComparable match found
Matched to firm historical corpus
Rate variance detected
Above comparable median range
Delivery pattern identified
Source links need matter-specific review
A review document can bring available AI activity, work, and billing context together. It supports discussion and does not prove that AI caused a fee outcome.
See it on a sample matterReference
£30,000
Proposed fee
£21,000
Below reference
30%
Reference is an observation from comparables, not an AI-free control. The gap is an observation, not a confirmed AI saving.
Review where AI activity appears in matter work and where evidence is missing. Use that context to inform adoption, training and investment priorities, without treating usage as proof of return.
See it on a sample matterIdentify conflicting signals and missing context for professional review. Attribution and estimates need scrutiny before they inform a decision.
See it on a sample matterWhere is AI contributing to matter work, and what should we invest in next?
Bring available activity and matter economics evidence into adoption, training and investment reviews. See what it supports and where more validation is needed.
These are evidence categories, not customer performance statistics.
Observed
Signals directly recorded by a connected source.
Evidence label
Estimated
A value inferred from available records and stated assumptions.
Evidence label
Reviewed
Attribution checked by a person, with its basis recorded.
Evidence label
Data access, processing, and storage depend on the agreed deployment and connected sources. Before any customer data is connected, review the permissions, fields, and handling arrangements for that deployment.
Scoped access
Requested permissions and data flows should be reviewed for each source and deployment before connection.
Documented signals
Fields collected depend on the connected source and configuration. Agree the signal scope before any customer data is connected.
Human review
Treat estimates and attribution as decision support. Review the supporting evidence before relying on a signal.
A vendor dashboard reports activity within its own product. Alpodo is designed to bring available evidence from multiple connected sources into a common matter-level review. What can be linked, and with what confidence, depends on the data available.
Timing depends on the deployment approach, permissions, source availability, and validation required. We scope those dependencies before agreeing a timeline; data does not necessarily flow immediately after authorization.
Security suitability depends on your requirements and the deployment design. Before connecting customer data, review the requested permissions, fields collected, processing locations, retention, and subprocessors with us. We will clarify the applicable data handling and security materials for the proposed deployment.
We are developing and validating integrations for selected legal, AI, and Microsoft 365 sources. Availability and signal coverage vary by connector, configuration, and access. CSV or API-based approaches may be possible for other sources after scoping.
We review your questions and walk through how Alpodo brings activity, matter context, and economic evidence together. Connecting your environment or showing live data is not part of every demo; we will scope that with you in advance.
We can discuss a sample-data walkthrough or a scoped pilot. Whether customer data can be used, and what approvals and setup are needed, depends on the proposed deployment.
Where source data supports it, activity from different tools can be related to matters and work units. We show available economic context, confidence, and unresolved gaps; tool activity alone does not establish economic impact.
We distinguish recorded observations, estimates, and human-reviewed attribution. A link between AI activity and a matter is not, by itself, proof that AI caused an economic outcome.
Bring a delivery, staffing, cost, investment or pricing question. We can walk through a sample matter and discuss what evidence a scoped review would require.
Identify relevant sources, access requirements, and data handling for the proposed deployment.
Explore a sample matter and assess what the available evidence can and cannot support.
Discuss how evidence could inform delivery, AI investment and pricing decisions.