FAQ

Generic tools respond to prompts. CPGPro agents are designed around specific jobs-to-be-done, templates, rules, and CPG context. The goal is operational output you can trust and reuse, not “pretty language” that still requires rework.

It means the agent is designed for a defined role, dataset set, output format, and workflow. Inputs are structured, outputs are standardized, and quality is enforced via rules, templates, and review gates.

Executive-ready summaries, category outlooks, account plans, promo recaps, pricing diagnostics, competitive reads, distribution analysis, and planning inputs. Outputs are designed to drop into the artifacts teams already use (slides, trackers, briefs).

No. RPA is best for rigid, deterministic clicks. Agents handle ambiguity, interpretation, synthesis, and structured writing with guardrails. Many teams use both: RPA for mechanical tasks and agents for analysis and deliverables.

We align agents to your cadence and artifacts: AOP inputs, QBR narrative, category review decks, retailer scorecards, promo post-mortems, and weekly business reads. The value comes from repeatable output on a schedule, not one-off prompting.

A defined use case, target output format (template), and the minimum data needed for that workflow (e.g., syndicated extracts, shipment/depletion views, TPM exports, retailer POS, assumptions). We start small and expand once outputs are validated.

We use governed templates, business rules, standardized calculations/definitions, and controlled vocabularies (your language). Outputs are structured and traceable, reducing drift that happens with ad-hoc prompting.

The goal is the opposite: reduce rework by using your exact formats and rules. Early on, teams do a tight review loop to calibrate. After that, the review becomes fast approval instead of heavy editing.

Yes. We design workflows with optional review/approval gates so teams decide what is draft, what is publishable, and what requires escalation. Humans stay accountable for external-facing content.

Role-based access, controlled inputs, governed templates, and audit-friendly workflow patterns. We design for least-privilege access and align to how enterprises typically separate duties between creators, reviewers, and approvers.

By default, we do not use customer data to train public models. How data is handled depends on the deployment approach and your governance requirements. We’ll document the specific data flows for your chosen setup.

It means the agent layer is designed to work with different LLM options. We focus on your workflows, rules, and outputs rather than locking you into one model vendor.

We narrow scope, enforce structured inputs, require traceable calculations where applicable, and use templates and rule checks. The most reliable systems don’t “ask the model to be careful”—they design the workflow so errors are harder to create and easier to detect.

We only request the minimum required for the targeted workflow. Many pilots start with sanitized, aggregated, or export-based views rather than direct system access. We expand access only when the business case is proven and governance is in place.

Many teams see useful outputs in weeks, not quarters. The fastest path is to start with high-frequency, repeatable work where the cost of delays and rework is obvious.

A business owner who knows the workflow, a target artifact (deck/brief/template), and access to the minimum data inputs for that workflow. IT involvement depends on whether you start via exports or deeper integration.

Pricing is typically aligned to the agent/workflow scope and the operational footprint (teams, outputs, cadence). The goal is simple: predictable cost tied to measurable capacity and output, not vague usage meter surprises.

We expand by workflow and team: add adjacent outputs, increase cadence, onboard the next function, and standardize governance. The best rollouts avoid “big bang” and compound wins in controlled steps.

The common failures are: unclear use case, bad inputs, no ownership, and no definition of “done.” We avoid them by starting narrow, enforcing templates, validating with real users, and defining success metrics before expansion.