SigmaEra for Data & Analytics
Every dashboard you build misses where decisions happen.
Your warehouse has the outcomes and none of the reasoning. SigmaEra projects the unstructured half of the business — meetings, documents, decisions — into a queryable entity and relationship graph alongside it.
Every dashboard you build misses where decisions happen.
The arc a data leader walks with SigmaEra: what the warehouse structurally cannot hold, how the unstructured half gets modelled, and what governance that carries with it.
- 1The recurring question
Your warehouse has outcomes and no reasoning
You can show exactly what happened and never why. Every important "why" question ends with someone going to ask a person, which is the definition of a gap in your coverage.
What you have to answer
- Where does the reasoning behind our numbers actually live?
- Can any of it be modelled, or is it structurally out of reach?
- What would it take to query it?
The layer that answers it
Canonicalized unstructured content
Transcripts and documents are canonicalized into a consistent structure — speaker turns, chunks, entities, relationships — and projected into a graph, so the unstructured half becomes a modelled surface rather than a file store.
What changes for you
The half of the business that has never been queryable becomes a source you can actually join against.
- 2The credibility test
Extraction you would actually trust
You have seen enough free-form extraction to be sceptical. Anything that produces a different schema every week is not a data source, it is a liability with a nice demo.
What you have to answer
- Is extraction constrained, or open-ended?
- How stable is the schema over time?
- Can I trace a derived fact back to its evidence?
The layer that answers it
Closed pattern catalog with provenance
Entity and relationship projection runs against a closed pattern catalog rather than free-form extraction, and derived artifacts carry provenance back to the evidence that produced them.
What changes for you
You get a stable, traceable source rather than a generative one — which is the difference between something you can build on and something you have to caveat.
- 3The access question
A query surface is an access surface
The moment you expose a graph over sensitive conversations, you have created a new way to reach content — and if it does not respect existing permissions, you have built a bypass.
What you have to answer
- Are graph reads filtered by the same rules as everything else?
- What happens when clearance is unset?
- Is the isolation provable, or application-level?
The layer that answers it
Filtered reads, fail-closed
Graph and search reads are filtered by tenant, scope, and sensitivity clearance, all failing closed. Postgres is the layer that can prove scope isolation today; for search and graph the interim mitigation is conservative — personal and team evidence is excluded from the shared graph rather than filtered inside it.
What changes for you
You can expose a query surface without it becoming the way people get to things they should not see — and you know precisely where the current limits are.
- 4Over time
A source that improves rather than decays
Most data assets rot. Schemas drift, owners leave, and the pipeline nobody understands becomes the pipeline nobody touches.
What you have to answer
- Does this get richer with use or just larger?
- What happens to derived data when a source is deleted?
- Who owns the model as it evolves?
The layer that answers it
Lifecycle-aware derived data
Deletion cascades from source through classifications, entities, signals, findings and reports, with tombstones making erasure ordering-independent — so the derived layer stays consistent with its sources rather than accumulating orphans.
What changes for you
The asset compounds instead of rotting, and it does not slowly fill with derived facts whose evidence no longer exists.
Control register
Every layer above is documented there in full, including the threat model and the control-status register.
Beneath the controls, the platform itself.
The five capabilities every SigmaEra deployment runs, read through the lens of this role.
Five stages · Protect → Emerge- 01
Protect
Every graph read is filtered by tenant, scope, and sensitivity clearance, so a query surface does not become a way around access control.
- 02
Orchestrate
Transcripts and documents are canonicalized into a consistent structure — turns, chunks, entities, relationships — instead of a folder of files.
- 03
Automate
Entity resolution and relationship projection run continuously, with a closed pattern catalog rather than free-form extraction that drifts.
- 04
Compound
The graph deepens with every meeting, and derived intelligence carries provenance back to the evidence it came from.
- 05
Emerge
Relationships surface that no schema anticipated — the connection between two initiatives that only exists in how people talk about them.
The Compound Effect
Intelligence that builds on itself.
- 10×
- faster strategic risk detection — surfaced from meeting and workflow data before it reaches the board.
- 1
- company-specific model — your corporate knowledge compounds into a private intelligence layer that gets smarter every week. Source: Platform security white paper — Model processing & training
- 10–25%
- reduction in redundant organizational work — duplicate initiatives identified and consolidated automatically.
Stop leaking data. Start compounding intelligence.
See How Data Teams Use SigmaEra
- Runs air-gapped inside your own boundary
- Full audit trail on every interaction
- 100 agents, one control plane