More intelligence.
Clearer control.
Build access, evaluation, observability, and human oversight into your AI deployment from the start.
Explore ↓Every request.
A visible decision path.
Make access, policy, routing, and evaluation part of the same operating loop. These are illustrative controls configured for each deployment.
Start with the requester.
Establish who is asking, which workspace they belong to, and the tools and data they can access before retrieving context.
Apply the policy before the model.
Choose an approved model and budget for the task. Sensitive-data handling and routing rules are defined with your team.
Match the gate to the impact.
Read-only assistance and consequential actions need different controls. A proposed external change can require an accountable reviewer.
Leave a useful record.
Record the request path and evaluate answer quality, tool use, latency, and spend without turning the audit trail into another disconnected system.
Confidence comes
from what you can inspect.
Our engineering engagements define the controls your workflow needs and implement them within your stack. The design follows the sensitivity of your data, the actions an agent can take, and the operating responsibilities of your team.
Source-grounded answers
Trace business answers to the systems and records behind them.
02 /Bounded actions
Define approved tools, escalation thresholds, and human review.
03 /Operational visibility
Normalize telemetry, validate readiness, and preserve lineage.
04 /Reviewable reasoning
Inspect cached triage hypotheses alongside replayable incident evidence.
Agree on the control model.
Access & data boundaries
Identify which users and services can read each source. Agree on deployment location and model-provider data flows.
Quality & evaluation
Build representative examples and agree on what correctness, latency, and failure look like.
Actions & approvals
Set the permitted action scope, review points, escalation rules, and recovery procedure.
Operations & ownership
Agree on logs, alerts, support responsibilities, and the evidence needed for handoff.
Your environment sets the terms.
EdgeTelemetry deploys in the customer’s infrastructure. Company Brain uses the customer’s own Anthropic account with direct billing. We scope hosting, identity, model access, and integration requirements for each engagement; certifications and regulatory requirements must be confirmed for the specific deployment.
Visibility and control
across the workflow.
Usage, budgets & model access
Instrument model usage and cost by workflow or team. Agree on budgets, limits, and model-access policies, then implement the required controls within the deployment.
Define the operating requirements ↗Gateway & routing integrations
Create a controlled path between applications, tools, and model providers. Scope authentication, request policies, routing, retries, and the visibility your operators need.
Explore integration engineering ↗Guardrails & sensitive data
Define how untrusted input is handled, which data can leave each boundary, and where filtering or redaction is required. Restrict tool permissions and evaluate adversarial inputs before expanding action scope.
Explore bounded agent workflows ↗Session traces & review
Make workflow steps, retrieved evidence, tool actions, and exceptions inspectable. Define logging and retention around the customer’s data policies and operational needs.
Explore agentic operations ↗Start with the work
that matters.
Bring us the workflow, the systems behind it, and the outcome you need. We will help define a practical path into production.
Talk to our team ↗