FAQs
A practical package that procurement and leadership can use:
Use-case prioritization + ROI hypothesisData feasibility assessment (sources, gaps, quality risks) Target architecture on your stackPilot scope, timeline, and KPI planSecurity/governance checklist and operating model.
Dashboards describe the past. Rule engines hard-code yesterday’s assumptions. vdd .ai agents are decision systems: they fuse internal operational data + external signals (when relevant), learn patterns, simulate trade-offs, and produce actions/recommendations with guardrails—then integrate into the workflows where decisions actually happen.
Recommend mode (human-in-the-loop approvals)Assisted execution (agent triggers actions within policy limits)Autonomous loops (fully automated for scoped decisions) You control approval thresholds, escalation rules, and kill-switches.
That’s the normal case. Our core strength is data wrangling at enterprise scale: entity resolution, product matching, semantic modeling, quality controls, and governance. We can start with a narrow scope, deliver value fast, and harden the foundation as we expand.
Typically 2–6 weeks for a scoped pilot (one domain, defined KPIs), depending on system access and data readiness. Full roll outs scale by business units, geographies, and complexity.
Most enterprises run a mix of ERP, POS, CRM, WMS/TMS, OMS, finance, HR, and custom apps. We connect through APIs, CDC, ELT/ETL tools, file drops, event streams, and warehouse-native approaches—depending on your standards.
Yes. We can deploy in your environment (AWS/Azure/GCP) and/or directly on Snowflake/Databricks depending on your architecture and security requirements. Data residency and network controls are handled upfront.
We align with enterprise patterns:
- RBAC/ABAC aligned to your identity provider
- Least-privilege access and environment segregation
- Encryption in transit/at restSecrets management
- Audit logs for data + agent actions
- We design security so it’s not an afterthought—it’s part of the delivery
We implement:
A governed semantic layer (definitions that don’t drift by team)Data quality checks and lineage tracking Approval workflows for agent actionsPolicy-based constraints (what an agent can/can’t do) Full traceability from input → decision → output → business impact
We instrument production like a critical system:
- Data freshness/quality monitors
- Model/decision drift signals
- Performance SLAs (latency, throughput)
- Cost observability (compute + API usage)
- Alerting + incident playbooks
- You get a control plane for reliability—not just a model.
We define ROI like an operator:
- Baselines + counterfactuals
- A/B tests or holdout groups when possible
- KPI ladders (leading + lagging indicators)
- Executive dashboard with value attribution
- If value can’t be measured, it’s not “production.”
We’re model-agnostic. We select models based on accuracy, latency, cost, and risk profile, and we architect for swapability. Your business logic, semantics, and governance remain stable even if models change.
Yes. We provide decision transparency appropriate to the domain:
- Drivers and contributing signals
- Constraints applied (guardrails)
- Confidence/uncertainty indicators
- “Why this, not that” comparisons
This is essential for adoption in pricing, supply chain, and executive reporting.
Most data projects deliver pipelines and dashboards. Executive DIaaS delivers a board-ready semantic layer: consistent KPIs, governed definitions, trusted lineage, and executive-grade reporting—built to power both analytics and agents.
Yes. We provide an operating model that can include:
Managed operations (monitoring, incident response)
Continuous improvement (new features, optimization)
Data/model governance reviews
Performance and cost tuning
You can run it with your teams, with ours, or in a hybrid model—depending on maturity and preference.
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