External retrieval adapters
Connect production vector stores and measure retrieval on an independently maintained evaluation set.
What exists now, what comes next, and what's deliberately out of scope.
Product roadmap
Every delivered item opens its working surface. Future integrations describe direction, without promising a delivery date. Experience revision 2026-10-05.
Frame an initiative, inspect data and build evidence, govern a release and revisit realized value.
Explore firsthand and studied scenarios, with source context and direct links to each instrument.
Edit a portfolio, constrain the budget and compare funding and recommendation changes against a pinned baseline.
Inspect a discounted cash-flow bridge, exact assumptions and the driver that changes the funding case.
Separate current scores from a controlled projection of the steps toward a readiness gate.
Choose a resolution per skill gap and inspect the modeled cost and delivery consequence.
Change decision weights and inspect vendor fit, concentration, renewal and exit exposure.
Inspect the modeled evidence loop, findings, controls and governance decisions.
Pressure-test a routing threshold against quality and cost using an authored evaluation dataset.
Follow a program through related instruments while keeping the narrative and next decision visible.
Connect production vector stores and measure retrieval on an independently maintained evaluation set.
Add authenticated storage, versioning and permissions beyond browser-local scenarios and exported files.
Connect monitoring, evaluation stores, model registries and approved enterprise tools.
Reconcile modeled ROI with finance-approved quarterly outcomes and attributable adoption evidence.
Confidential client data, cloud provisioning, full model training frameworks and enterprise authentication remain outside this demonstration. Existing scenarios, exports, simulated governance controls and local calculations work without those integrations.
Inspect architecture and handoff contractsSimulation boundary
This portfolio demo uses deterministic, client side engines and sample data to show enterprise AI delivery mechanics without confidential data or cloud infrastructure. Some signals, including production telemetry and incident and regression history, are modeled. The architecture is built around handoff contracts so real integrations can replace modeled signals later.
Implemented / deterministic
Modeled / simulated