Decision infrastructure for product teams

Product-Market Fit Requires a Product-Market View.

Latent Spark turns fragmented product, market, customer, competitor, and internal signals into one living product-market view, so every forecast, workflow, integration, and agent can reason from shared context.

Product-market fit is not a static milestone; it is a moving relationship between what you are building, who it is for, what alternatives exist, and how the market is changing around it. Latent Spark gives your systems a shared understanding of that relationship, continuously updated as new evidence arrives from customers, competitors, product work, research, and internal decisions. It is the context layer that helps every downstream system reason from the same view of the market, and the substrate that lets AI agents and internal tools act on grounded, traceable understanding instead of inferring from fragments.

From fragmented signals to product-market clarity.

Latent Spark

Probabilistic atlas of structured entity space. Maintains entity belief states across features, sentiment, sales, psychographics, and identity. Evidence from text, images, telemetry, market data, and tenant streams is fused by natural-parameter addition, queried by KL divergence, and decoded into counterfactuals.

Scope

Headless API, MCP, and GraphQL surfaces for system, workflow, and agent integration.

Availability

Pre-wired into Iridae Decision Infrastructure. Also available as an individually licensable enterprise component.

Use weak signals before they become obvious.

Latent Spark holds every product, segment, competitor, and theme as a belief state: what seems true, with confidence, evidence, and what remains uncertain. Early signals shape the model without becoming settled facts; good signals sharpen, weak or contradictory ones make it visibly fuzzier; feeds that stop teaching get throttled. As evidence accumulates, belief states sharpen and stabilize, with confidence grounded in the trajectory, not a single moment’s reading.

Understand fit as a relationship, not a research artifact.

Latent Spark connects product behavior, customer feedback, competitor movement, market structure, and internal decisions into one updating view. The evidence flows in from the systems your team already uses (telemetry, support, sales calls, Slack, Jira, docs, and market data), combined with shared industry corpus and your private context. Forecasts, experiments, plans, workflows, and agents all reason from the same product-market reality.

Explore what the product could become before you commit to becoming it.

Broaden an uncertain concept, constrain a product slice, move toward a segment, blend two market positions, or ask which shipped offerings your offering could plausibly become. Latent Spark shows how comps, assumptions, forecasts, and evidence needs shift under each hypothetical.

Turn internal debate into evidence without pretending every message is a fact.

Latent Spark tracks how product ideas form, fork, merge, drift, and settle across Slack, Jira, docs, research notes, and roadmap activity. Converged commitments sharpen the model; unresolved debates stay tentative; contradictions reduce confidence instead of becoming tidy summaries.

Not retrieved context. Updating belief.

Latent Spark isn’t RAG, an embedding index, or a graph database of market entities. Those retrieve and summarize what has already been represented. Structure is found at ingest, not after. Latent Spark holds living belief states with confidence, contradiction, time, and provenance from the first byte, returning not just the evidence behind any belief, but what that evidence means: where uncertainty remains, what is starting to change, which hypotheticals are worth exploring.

The headline question most others reduce to is: which existing entities are most like this one? Comparable customers, competitor lookalikes, segment matches, market positions, returned with confidence reflecting how well each is understood, whether wide for a still-forming product or tight for a settled one.

It replaces work that today gets outsourced or stitched together by hand (research retainers, competitive intel subscriptions, market synthesis decks, analyst POV documents) with a living model of product-market reality that updates the moment evidence shifts.

Model product-market reality

  • Product, customer, competitor, segment, and theme entities
  • Features, attributes, sentiment, sales, psychographics, and identity factors
  • Confidence and uncertainty on every belief
  • Weak signals without premature certainty
  • Contradictions surfaced as uncertainty
  • Versioned belief-state trajectories
  • Provenance from first byte of ingest
  • Shared industry corpus plus private context
  • Cold-start population priors
  • Quality and uncertainty-reduction scoring
  • Drift and staleness tracking
  • Downstream belief-state provider for forecasts, plans, experiments, workflows, and agents

Query the market as a space

  • Comparable and lookalike retrieval
  • Intent-conditioned similarity
  • Substitute, narrow-variant, deep, and robust comps
  • Uncertainty-aware comp sets
  • Asymmetric KL-based retrieval
  • Soft clustering of market positions
  • Product-market adjacency discovery
  • Emerging theme and weak-signal detection
  • Counterfactual blends
  • Directional edits
  • Hypothetical product states
  • Unexplored-region discovery
  • Product-market shift detection
  • Query fuzziness and constraint editing

Turn messy signals into evidence

  • Product telemetry and usage signals
  • Customer feedback and support data
  • Sales calls and objections
  • Research notes and survey outputs
  • Market and competitor data
  • Store pages, reviews, community signals, and commercial data
  • Slack, Jira, GitHub, docs, and file uploads
  • Multi-modal evidence fusion
  • Natural-parameter belief updates
  • Semantic trajectory filtering
  • Fork, merge, and drift detection
  • Evidence bundles from converged internal discussions
  • Contradiction handling and confidence adjustment
  • Full lineage from raw artifact to belief update

Standalone, built to compose.

Latent Spark deploys standalone as the context layer behind your apps, agents, and dashboards, or as part of Iridae’s Decision Infrastructure, where its belief states feed Anchored Horizon’s comp retrieval, Patient Cartographer’s plan simulation, and Subtle Beacon’s experiment design. The same picture of reality reasons through every downstream system.

Explore Decision Infrastructure