Every AI action traced to a known identity, screened in the request path and logged for audit.
A known identity
Each workload acts under its own scoped identity.
Screened in the path
Content safety and PII checks on every request.
A trail to audit
Every action recorded, with sources a reviewer can check.
Scoped in the first working session · Fixed price · The control design is yours
A written policy cannot tell a reviewer which identity an assistant uses, what its tools can reach or where an answer came from. The path the request runs can.
Scope every identity
Least-privilege tool access, aligned with your access model.
Ground every answer
Governed data, with the source carried to the reader.
People sign consequential actions
A person reviews where an action carries weight.
Four stages place the controls along the path one AI workload’s requests already take.
Give the workload its own identity and least-privilege tool scopes, aligned with your access model.
You receive
Identity design
The workload identity and the exact tools and data it may reach.
Accelerators, agents and products we build and maintain ourselves, brought into an engagement wherever they fit.
Agent runtime · Guards the path
The runtime every Kit agent runs on, built to act inside the systems you already run.
Plugs into Screen and ground
Explore more about BlumcoreThis service puts controls around one AI workload; the estate’s access model and the agent build sit next door.
Setting the estate’s ownership and access model?
That is Data GovernanceBuilding a new agent and its release gate?
That is Agentic AICutting the platform’s capacity bill?
That is Data Estate Optimization
A clinical documentation platform
Healthcare · Clinical AI
Notes and referral letters were costing doctors hours every month, and no patient data could be left behind. We built the controls into the path every consultation takes: voice held ephemerally and encrypted, with no protected health information stored after a session; access controlled through Microsoft Entra ID and Microsoft Purview; and a confidence score and source provenance on every output for the clinician to check. Documentation errors fell 60% in post-visit audits, and 80% of clinicians adopted it within four weeks.
The assistant runs on Azure AI Foundry: speech models transcribe the consultation, GPT-4 generates referral and patient-education letters from therapeutic guidelines, and clinicians query through a Power BI Copilot interface.
An AI trust layer is the set of controls a request passes through on its way to an answer or action. It gives each workload its own scoped identity, screens content and PII in the request path, grounds answers on governed data with their sources, keeps human sign-off on consequential actions and records every action for audit. The controls run in the workload, not only in a policy document.
Show the reviewer the path the request runs, not just the quality of the answer. A review asks which identity the assistant uses, what its tools can reach and where its answer came from. The trust layer makes each of those explicit: a workload identity with least-privilege tool scopes, screening in the request path, grounding with sources, and an audit trail of what the workload did.
Yes. A running workload is assessed against the controls it already has, and the missing ones are placed along the path its requests take. A new agent gets the same control design in its Blueprint phase instead. Either way the work covers one AI workload, and its timing is scoped in the first working session, at a fixed price.
Data governance sets the estate’s ownership and access model; the trust layer applies controls to one AI workload’s requests. Governance decides which groups may see which data. The trust layer aligns the workload’s identity and tool scopes with that access model, then adds content safety, PII screening, grounding, human sign-off and audit in the request path.
You keep the guardrail and identity design: the workload identity, the screening and grounding rules, which actions need a person to sign off and what the audit trail records. For a new agent it comes out of the Blueprint phase and is yours even if you stop there. Blumcore, our agent runtime, can carry the policy, guardrails, redaction and audit log in the path.
Natural next step
With the workload operating under its controls, measure and tune the estate that carries it.
Operational events become live KPIs and alerts, with a path from each event to an action.
Incoming data is checked at the boundary and lands as reusable feeds in a governed lakehouse.
Governed semantic models on Power BI or Looker, with metric definitions your team can inspect.
Bounded agents that pass an evaluation gate before release, with scoped tool permissions and an agreed operating plan.
An inspection or prediction task, taken from labeled data to a measured model with an operating review loop.
Book a 30-minute working session to trace the identity, data, tools and sign-off behind one AI action.
