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Hybrid Retrieval: Graph, Vector, and Lexical Verification

by iqentity | Apr 6, 2024 | Development, Tessera

Retrieval is everything. If Tessera cannot find the right precedent for a new situation, nothing else matters. I spent this month building a hybrid retrieval system that prevents the failure modes I have seen in every other approach. Why Single-Method Retrieval Fails...

Building the Decision Graph: Signals, Constraints, and Outcomes

by iqentity | Apr 4, 2024 | Development, Tessera

The graph is taking shape. Two months of ingested data, and the lattice structure is emerging naturally from the decision extraction process. The patterns are real. The Node Structure Each decision node contains: the decision itself (what was chosen), the signals that...

The Ingestion Pipeline: Treating Every Artifact as Evidence

by iqentity | Apr 2, 2024 | Development, Tessera

The ingestion pipeline is operational. Three weeks of building, testing, and refining against real data from my corpus. The results are better than expected, but the challenges were different from what I anticipated. Pipeline Architecture Stage 1: Format...

Cross-Domain Coherence: Why Consistency Beats Breadth

by iqentity | Mar 27, 2024 | Development, Tessera

Something emerged from the early extraction runs that validates the entire approach. The data confirms what the psychological assessments suggested: the same decision engine operates across all domains. What the Data Shows When I analyze how I handled legal risk...

Extracting Decisions, Not Text: The Ingestion Philosophy

by iqentity | Mar 22, 2024 | Development, Tessera

I spent the last two weeks building the extraction pipeline, and the design philosophy is paying off. Every artifact in my corpus is treated as evidence, not truth. The distinction matters. How Extraction Works An email is not an opinion to be stored. It is decision...

The Verification Layer: Why Tessera Must Check Its Own Work

by iqentity | Mar 21, 2024 | Development, Tessera

Every system that generates output from retrieved information will sometimes get it wrong. The language model will hallucinate. The retrieval system will surface irrelevant context. The fusion layer will weight the wrong source. These are not bugs. They are properties...
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