AI search intelligence
Cartographer measures visibility across AI search, preserves hash-linked evidence samples, and shows your team what to investigate next.
No magic score. Each retained sample keeps its query, exact surface, source positions, timestamp, and confidence attached.
Which FinOps platform best connects cloud cost to engineering action?
The production architecture is designed to connect one property, every query, exact surface, citation, and page version in one evidence chain. These integrations are not active in the local demo.
One map, every signal
Cartographer connects demand, exact-model observations, cited sources, page evidence, and measured outcomes—without collapsing them into one unexplained score.
Cluster real search demand, intent, conversational variants, and the pages meant to answer them.
Track mentions, recommendations, citations, rank, consensus, and drift across exact model surfaces.
Open retained normalized samples with their query, response excerpt, source positions, exact surface, timestamp, and raw-artifact hash reference.
Compare cited competitors, approve minimal edits, publish through your workflow, and remeasure the outcome.
Visibility, with receipts
Move from aggregate trends to the normalized demo samples retained for drilldown, including the exact query, response excerpt, cited source, model configuration hash, and raw-artifact hash reference.
northstarcloud.com / Last 30 days
Visibility & citation trend
400 aggregate observations · 95% intervals available
Component metrics
Mention, citation, recommendation, rank, consensus, and volatility stay separate.
Observation-level drilldown
Inspect exact surface settings, raw-artifact hash references, normalizer versions, and source positions.
Confidence attached
Sample size, variance, and observation window travel with every reported change.
From blind spot to measured lift
Cartographer preserves the measurement context at every step, so your team can reproduce the result and defend the decision.
Open campaign workspaceBring in your site, sitemaps, Search Console, and the business context that defines success.
Turn observed queries into an intentional universe of clusters, variants, intents, and target pages.
Freeze the model, grounding mode, locale, prompt version, and sample plan for repeatable evidence.
See who earns the answer, which source gets cited, and what your page leaves unsupported or unclear.
Review a minimal change, deploy it through your workflow, then compare immediate and delayed lift.
Measurement, not screenshots
A chart only earns trust when you can reconstruct the observation behind it. This demo exposes 4 normalized packets with measurement context and immutable artifact hashes; it does not embed the raw provider payload or claim that every aggregate observation was retained.
Inspect a sample observationObservation obs_0728_chatgpt
TRACEABLE · Jul 15, 2026, 3:42 PM UTC
Built for the stack you run
Connect the sources you trust. Keep model measurement exact. Ship through the workflow your team already governs.
Search + content sources
Campaign workflow
Exact AI surfaces
Traceable evidence
Reports + publishing
Keep your bearings
Run a baseline, uncover the citation gaps, and build an evidence trail your team can trust.
Explore the sample workspace