Governance your systems can enforce.
Sparq turns governance requirements into working controls across data, identity, AI platforms, and agent actions so the rules hold when real work starts.
Sparq turns governance requirements into working controls across data, identity, AI platforms, and agent actions so the rules hold when real work starts.
The Reality
Policies only go so far. If an unapproved model is reachable, an expired account still works, or an agent can take an action it shouldn’t, the rule is clear. The control isn’t.
Sparq moves governance out of documents and into the systems expected to enforce it.
Capabilities We Deploy
Proof In Production
Sparq brings governance into the same engineering discipline we use to move high-value AI workflows into production.
days to production-ready AI workflows
reduction in mortgage document processing time
reduction in manual analysis
How we work
We assess your data, access model, cloud environment, compliance requirements, and AI maturity.
We establish the rules, ownership, and approval paths your teams need to operate.
Access rules become identity policy. Approved models become enforced endpoints. Agent policies become action-level gates. Audit requirements become records written as the work happens.
The next use case inherits the foundation instead of rebuilding it.
Build governance into the architecture before the next use case reaches production.
Read the Latest
Most enterprise AI governance still gets built after the system ships, as an audit rather than an architecture decision. Dr. Zahra Timsah, CEO of i-GENTIC AI, explains why that sequence is backwards, and what governance by design looks like when it's done at build time instead.
Every data vendor is selling some version of an AI-readiness shortcut: a catalog, an ontology, a metrics store, a governed context layer. None of them ship with the thing a semantic layer actually depends on: an organization that has already agreed on what its own numbers mean.
Most Snowflake estates were architected for reporting. Agents require something structurally different: a semantic layer, governed access controls, data pipelines running at operational speed, and compute built for continuous workloads. Here’s what each gap costs, and what a production intelligence foundation looks like.
FAQ
Get the answers you've been looking for.