Under the hood, written for a technical reader
Sova reads unstructured customer language with large language models and turns it into ranked, evidence-linked decisions. Here is the pipeline, honestly, including where the models sit and how we keep them accountable.
Four stages from raw text to a ranked decision
Each stage is a place where a model does work a person could do, only across everything at once.
Ingestion and normalization
Sova pulls unstructured text from your sources, tickets, call transcripts, reviews, NPS verbatims, and cancellation reasons. Each item is normalized, de-duplicated, and tagged with its source, account, and timestamp, so every sentence keeps a link back to where it came from.
Model reading and clustering
Language models read each item the way a person would and group it by what is actually being said, not by keyword. That is why single sign-on, SSO, and SAML collapse into one theme, and why a polite review and an angry ticket about the same gap end up together. Embeddings drive the clustering; a reading model labels each cluster.
Evidence extraction
For every theme, Sova extracts the exact sentences that support it and checks that each quote appears verbatim in the source before it is ever shown. A claim with no traceable quote does not survive. This is the step that makes a decision defensible in a roadmap meeting.
Ranking
Decisions are scored by how much signal supports them, how recent it is, the weight of the accounts involved, and the model's confidence. The result is a short ordered list, not a word cloud, with a confidence figure you can question and the evidence attached.
Every step goes to the model that fits it
Sova is not tied to one provider. A thin routing layer sends each step to the model that balances quality against cost, and swapping a model is a config change, not a rewrite.
As the frontier moves, the routing table moves with it. You get the benefit of a better model the day it lands, without changing how your team works.
Kept honest by design
Machine reading at scale is only useful if you can trust it. These are the checks that make Sova's output something you can defend.
No quote without a source
Every evidence quote is verified as a verbatim substring of a real signal before it is shown. Hallucinated quotes are dropped.
Confidence is visible
Each decision carries a confidence figure and a signal count, so you can weigh it rather than take it on faith.
Your data stays yours
Your signals produce your decisions and nothing else. They are not used to train shared models. See the Security page.
Graceful degradation
If a model is unreachable, Sova falls back to a deterministic engine and tells you, rather than failing silently.
Why this needs modern AI
Take the models away and you are back to tagging tickets by hand. Every ranked, evidence-linked decision Sova produces depends on machine reading at scale.
Start turning customer signal into decisions
Connect one source free and see your first ranked decisions with the evidence behind them. No credit card.