Our ML & LLM models are trained on millions of titles and billions of transactions, then benchmarked and retrained continuously. Every update ships at no extra cost, with no migration project.
Why DemandSens, and not a generic tool?
Because forecasting book sales is not a generic problem. It's a craft with its cycles, its weak signals, its hierarchies (title → series → collection → publisher), its channels (mass market, specialty, bookshops, online), its industrial constraints. DemandSens is built for that, and only for that.
These aren't slogans: every pillar is measurable, verifiable in a demo, and inscribed in our contractual commitments.
Our ML & LLM models are trained on millions of titles and billions of transactions, then benchmarked and retrained continuously. Every update ships at no extra cost, with no migration project.
100% of our R&D is dedicated to editorial workflows: book lifecycle, hierarchies (title/series/collection/publisher), title-by-title forecasting, allocation down to the point of sale. No features imported from other industries.
Minimal learning curve, fast onboarding, daily usage without heavy training. The tool adapts to your existing processes, not the other way around.
Give sales, ops and finance teams explainable, factual forecasts so they act faster and more accurately. Every recommendation is traced and justified no black box.
Three frequent confusions we want to clear up upfront so we're talking about the same thing in a demo.
Power BI, Tableau, Looker build dashboards from your data, but can't forecast or recommend. DemandSens starts from domain models (referents, comparables, weak signals) and produces actionable decisions.
Excel remains the universal tool of editorial teams but it has no memory, no governance, no AI. DemandSens absorbs Excel inputs when needed, but centralizes decisions in a shared, traced repository.
Many AI tools produce numbers without explanation. With us, every recommendation exposes its signals: referents used, similarity distance, factor weights. The user can accept, adjust, or refuse fully informed.
No big bang, no lock-in. A 3-phase approach starting with a short pilot on your data, with your users that commits to nothing further until value is demonstrated.
Prove value on a limited scope.
Turn the pilot into an operational system.
Long-term value creation.
No big bang. Go / No-Go decision after 6 weeks.
Business outcomes before full deployment.
From one entity to a full group perimeter.
Built for ISBN-level decisions.
Depending on your context, you'll enter the suite through a different module. All share the same data foundation and the same business logic.
Group cockpit
For Whom: Mid-market publishing groups.
What: Pilots the sales cycle (Print Run · Allocate · Prospect) and N+1 budget (Budget) for an entire group.
When: When Excel-based steering can no longer keep up with growth.
Self-serve, per title
For Whom: Authors, agents, small publishers.
What: Manuscript analysis, metadata, comparables, marketing assets by title.
When: Before signing a title, or for an isolated launch.
Market data
For Whom: Data partners (Circana, NielsenIQ, MediaControl…).
What: Aggregation and shaping layer for market data. Powers Core and BookInsight.
When: Continuously, in the background.