MAS.Ai is now delivered only to existing paying customers. Further development has stopped, and new development work is focused on Entriva.ai.
Current direction — 6 October 2026
I expect AI on the buyer's side to reduce demand for many human consultative activities. I will therefore no longer build my future business around those services.
MAS.Ai's human consultative side will be discontinued or replaced by AI and robotic systems. Existing paying customers will continue to receive their service.
The new development focus is Entriva.ai: a program intended to find customers from signals and sell services through proactive outreach agents and defensive agents that meet the buyer's AI. The target is a sales process without human involvement.
Read the new Entriva.ai working problem
Read the fieldnote explaining the decision
Where the MAS.Ai question started
The starting question was simple:
Can observable changes in a company indicate that a legal need may be forming before the company actively starts looking for a law firm?
The question remains open. The work below records the MAS.Ai thinking that led to a working service; it is retained as the history behind the current delivery.
Why I find the problem interesting
Most legal business development begins relatively late. The question we started exploring was whether some of the events that eventually create legal demand are visible earlier.
The early version of the idea was too simple
A single event is usually ambiguous. The problem gradually changed from “Which events predict legal work?” toward “Which combinations of observations make a particular commercial hypothesis worth investigating?”
What MAS.Ai was designed to detect
We are not trying to predict that a company will buy legal services. The practical question is whether enough has changed around an organization that it deserves earlier commercial attention.
The MAS.Ai working model
Observable company change → possible commercial pressure → signal context → convergence / contradiction → possible legal need → law-firm relevance → lawyer relevance → decision whether to investigate or engage → observed outcome → learning
What we observed through the work
- Single public events are often too ambiguous to be commercially useful on their own.
- Combinations of independent signals appear more useful than raw signal counts.
- A possible buyer need and supplier fit need to be evaluated separately.
- Law-firm fit and individual-lawyer fit can diverge.
- The closer we get to learning from outcomes, the more important identity, lineage and evidence become.
- A rejected commercial action does not automatically disprove the original need hypothesis.
Questions that remain open
How many observations are enough? Which combinations generalize? How should timing be represented? How should need and activation outcomes be separated? Which outcomes genuinely resolve which predictions?
What we deliberately do not publish
The purpose is to explain the problem and how our thinking is developing, not to publish proprietary weights, customer-specific logic or the full engine.