Tessera has been in active use for six months. The architecture is stable. The data is growing. The capabilities are expanding. It is time for an honest accounting of where things stand.
What Works
Trimodal retrieval delivers. The combination of vector, graph, and lexical retrieval produces relevant results at eighty-seven percent accuracy, which is sufficient for production use. The verification layer catches most of the remaining thirteen percent before I see it.
Incident Replay is the standout feature. It has compressed my pre-remediation context-building from twenty to forty minutes down to two to five minutes in most cases. Over six months, that is hours recovered. But more importantly, the quality of the context is better because Tessera surfaces precedents I would not have remembered.
Life orchestration saves cognitive overhead. The morning briefings, commitment tracking, and pattern observations have measurably reduced the number of things I need to hold in my head. I no longer worry about dropped commitments because I trust the system to catch them.
The air-gap is non-negotiable and works. No data has left the system. No external dependencies have caused downtime. The local model is less capable than cloud alternatives, but the capability gap matters less than I expected because the retrieval quality compensates.
What Does Not Work
Systemic pattern detection is weak. Tessera excels at single-incident matching but struggles to identify patterns across multiple low-salience incidents. The enrichment pipeline needs to be smarter about linking related events that do not share obvious surface-level connections.
Salience scoring is inconsistent. The initial scoring algorithm over-weights recency and under-weights consequence. Foundational decisions from years ago are sometimes buried beneath recent but trivial artifacts. The scoring model needs retraining with a larger set of manually evaluated examples.
Generation quality bottleneck. The local seven-billion-parameter model produces adequate but not excellent output. Long-form synthesis, especially for Action Planning queries, sometimes lacks the coherence and depth that a larger model would provide. This will improve as open-source models continue to advance.
What Comes Next
The next six months will focus on three priorities. First, improving enrichment quality, particularly decision identification and cross-incident pattern linking. Second, upgrading to a larger local model as hardware prices decline and open-source options improve. Third, building the collaborative layer that allows Tessera to support not just me but teams I lead, with appropriate access controls and privacy boundaries.
The collaborative layer is the biggest architectural challenge. The current system is purely personal. Extending it to support team-level context while maintaining individual privacy boundaries requires a fundamentally different access control model. I have the design. Building it is the work of the next phase.
Six months ago, Tessera was an idea and a set of design documents. Today, it is a system I use every day, that makes me measurably more effective, and that is specifically calibrated to how I think and work. The architecture is sound. The data quality is improving. The capabilities are expanding. This is working.