Solutions · AI & Agents
Consented customer data for your AI agents
An agent is only as good as the data behind it: Sfere gives AI one resolved profile per customer, consented, updated in real time and reachable over an API, with permissions checked at the source so automation acts only on data it is allowed to use
- One resolved profile
- Real-time signals
- Consent at the source
- Data stays in your region
Interactive · The redaction pass
Watch the guardrail fire before the prompt exists
An agent’s question is simple: “what can I use about this customer?” The answer is decided at the data layer, not in the prompt. Flip the customer’s permissions and the model’s region, then run the pass: fields are kept or redacted in real time, and only the scoped, permissioned packet ever reaches the model
Customer permissions (flip to revoke)
Residency
7 of 9 fields reach the model
“What can I use about this customer?”
GET /v1/customers/cu_8f3a21/context
scope: customer consent · serving region: US
governance gate · evaluated at the data layer
customer_profile cu_8f3a21
one resolved profile · demo data
- customer_id identifier · pseudonymous cu_8f3a21 released to the model
- full_name string · pii region-bound Maya Chen released to the model
- email string · pii region-bound maya@example.com released to the model
- home_city string · geo · coarse Austin released to the model
- geo_point lat/long · precise region-bound 30.2672° N · 97.7431° W released to the model
- top_category attribute · score Running · 0.92 released to the model
- browsing_now event · live Trail shoes · 41s released to the model
- lifetime_value number · USD $1,284.00 withheld: the customer has not consented
- rfm_segment attribute · enum “champions” withheld: the customer has not consented
// 2 withheld · no consent · never sent to the model
Why context is the hard part
The retrieval surface AI should be reading from
Agents and models ground on the live data platform underneath: a resolved, consented, real-time customer graph, not a lake of unowned, unlabeled records
See it scoped in the demo above-
One resolved profile to read
An agent reads context from a single first-party profile per customer instead of reconciling three systems that disagree about who someone is. The customer graph is the retrieval surface, already stitched
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Current signals, not last night’s
Ground a model in what a customer is doing now. A real-time event layer keeps context fresh to the moment, so an agent reasons over the present instead of a snapshot that expired at export time
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Consent enforced at the source
An agent only ever sees data a customer has consented to. Permissions live at the data layer, not in prompt instructions the model can drift from, so automation stays inside the lines by construction
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Data the model can’t leak
Customer data stays in your environment, so agent context never leaves the boundary it is governed in. The boundary is enforced where the data sits, not left to where an agent happens to run
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Attributes an agent can filter on
Typed identifiers and attributes give an agent structured fields to condition on, so retrieval is a query against governed data rather than a scrape of whatever a customer once typed
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Composable through the API
Reach the customer graph over a REST API and SDKs. Agents, models, and next-best-action logic plug into governed context without a bespoke data project standing in the way
Governed context
An agent grounded on off-limits data is a liability, not a feature
Automation fails quietly when it acts on records that are stale or that a customer never permitted. Sfere keeps the customer graph fresh and permission-aware at the data layer itself, so an agent reasons over current, consented context. The guardrail is where the data sits, not a line in a prompt the model can wander past
- Retrieve from one consented, first-party profile per customer instead of fragmented, conflicting sources
- Serve signals in real time so an agent’s decision reflects the current moment, not a stale batch
- Enforce consent and data residency at the data layer, so automation acts only on permitted data
- Expose the graph through an API and SDKs for agents and models to compose against
system: “…do not use this customer’s location or value data.”
✕ drifts past the instruction
How an agent reaches the graph
Resolve, scope, serve, act
Governance runs before an agent ever sees a field, so the context returned is already the context it is allowed to use
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01
Resolve the customer
Fragmented touchpoints are already stitched into one first-party profile, so an agent asks about a person, not a device or a cookie
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02
Check what’s permitted
Consent and residency are evaluated where the data lives, so the context returned is already scoped to what a customer allowed
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03
Serve fresh context
The profile carries current signals from a real-time layer, so the agent grounds on now rather than a snapshot from last night
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04
Act through the API
The agent reads governed context over the API and drives the next action, with the data trail queryable rather than opaque
Ground your agents on data you can defend
Walk the customer graph, the consent model, and the API surface with our team, and see how an agent reads real-time, permitted context instead of a stale, unowned scrape