Technology covers which model runs and where it is deployed. This page is about what happens inside a single case: how the operator finds the right passage in your own documents, how it holds your decision framework as something it can actually trace, and how it plans, acts and checks itself rather than answering in one shot.
A model that answers from memory alone will eventually state something false with total confidence. AutoServa never lets a case run on memory. Every answer is retrieved from your own material first, the same way a new hire would go and find the actual contract instead of quoting one from recollection.
A contract, an invoice and a support thread break into meaningfully different pieces. We chunk by clause, by line item, by message, not by a fixed character count that can cut a clause in half.
Semantic search finds what a passage means. Exact keyword search finds the specific invoice number or clause reference it contains. AutoServa runs both, because an embedding can drift on the one figure that actually decides the case.
The first pass returns what looks related. A second pass re-ranks against your decision framework, so the passage that actually governs the case surfaces first, not the one that merely shares its vocabulary.
Nothing is stated without a pointer back to the exact page, clause or line it came from. That citation is what a reviewer checks, and it is what the evidence trail records.
Retrieval quality is measured against real cases from your queue that the operator has not seen, not a public benchmark that has nothing to do with your documents.
Retrieval finds the right passage. The graph is what lets the operator understand how that passage relates to everything else, your entities, your rules, your precedent, without flattening all of it into one undifferentiated pile of text.
A vendor connects to its contracts, a contract to its clauses, a clause to the precedent that interpreted it. The graph holds these connections as data, not as something the model has to re-infer from raw text on every case.
The thresholds, constraints and escalation rules captured during Codify are not prose. They are nodes and edges, which is what makes the framework something both the operator and your team can actually trace, not a document that only means what one person remembers.
When the operator gives an answer, the path it took through the graph is the reason. That is a real explanation you can walk through case by case, not a plausible sentence generated after the fact to justify one.
When your team decides something new, that decision becomes a node connected to the relevant rule. The graph grows with your business day to day. The model underneath does not need retraining for the framework to learn.
This is also what the "consolidate the decisions" step on the homepage becomes technically. See what the operator absorbs
Agentic is a specific claim, not a marketing word. It means the operator decides its own next step inside a case, rather than following one fixed prompt-and-answer script. Four things have to be true for that claim to hold, and all four are what we build.
Reading the incoming document, checking it against the framework, verifying with a second system, drafting the response. Whatever your queue actually involves, decomposed the same way a trained member of staff would, not answered in one pass.
The operator decides whether this case needs the ERP, the CRM, a browser session, or just the documents already retrieved, rather than running a fixed integration script that assumes every case has the same shape.
Before one step's result is used to start the next step, it is tested against the decision framework it was given. A result that fails that check does not silently carry forward.
The same boundary that governs a person's role governs the operator's. Workflow Guard is where that boundary is enforced technically; here, the point is that the operator is built to look for the edge, not to push through it.
Retrieval, the graph and the agent loop are not three separate features. On a real case they run as one pipeline, in this order, every time.
The case pulls the specific passages it needs from your documents, ranked against your framework, not the whole archive.
The retrieved material is placed against the relevant nodes: which rule applies, which threshold is closest, which precedent is nearest.
The operator calls the system the step actually requires, API first, browser where nothing else exists, under its own named credentials.
Every action is tested against approval chains, spending ceilings and segregation of duties before it executes, not after.
What was read, which rule applied, what threshold was hit, and what precedent it followed are written to the evidence trail before the case is considered closed.
A case that reaches the edge of the framework stops there and goes to the named person who owns that decision, with everything above already attached.
Steps four and five are Workflow Guard and the evidence trail. Read how governance is enforced
The best way to judge a retrieval and reasoning architecture is to watch it read a case from your actual queue. Bring your technical lead and a handful of real, awkward examples to the first session.