Automating a market-sensitive process with multiple decision points using Unframe

The customer

A mid-sized European ingredients producer (≈ €600M revenue) selling into food, fragrance and nutrition customers across 30+ countries. The commercial team manages ~12,000 active SKU-customer combinations and re-quotes pricing weekly.

The challenge

Their quote-to-price process involved 9 hand-offs across CRM, ERP, a pricing spreadsheet, a raw-material index feed and an approval workflow in email. Each quote required:

• Multiple decision points: margin floor, customer tier, contract clauses, payment terms, regional approvals.
• Contextual awareness: pull customer history, open contracts, current cost basis, competitor benchmarks.
• Frequent market-driven changes: oil, dairy and energy indexes shifted weekly, invalidating prior quotes.

Average time to issue a quote: 3.5 days. Error rate: ~7%. Lost deals due to slow response: estimated €4–6M / year.

The approach — agentic automation

Pure RPA was not enough: the process was too variable for fixed rules. We designed a hybrid solution on the Business Automation Platform, combining deterministic robots, AI agents and human-in-the-loop approvals.

1. 1. Process discovery with Task Mining & Process Mining

   Captured the as-is process across SAP, Salesforce and Outlook. Identified the 4 decision points worth automating and the 2 that must stay with humans.

2. 2. Deterministic orchestration with Studio + Orchestrator

   Robots fetch customer master data from SAP, open contracts from Salesforce, and the latest cost basis from the pricing data warehouse. All structured, rule-based steps.

3. 3. Contextual reasoning with Agents (Autopilot / Agent Builder)

   An AI agent reads the RFQ email, classifies intent, reconciles SKU variants, and proposes a price band using the latest market indexes and competitor benchmarks. The agent calls an LLM through Unframe's AI Trust Layer with grounded context — no free-form hallucination.

4. 4. Human-in-the-loop with Action Center

   Quotes above margin thresholds or for strategic accounts are routed to a commercial manager with the full reasoning trail. One-click approve, adjust or reject.

5. 5. Continuous re-pricing trigger

   When indexes move beyond a defined band, an Unframe trigger re-runs impacted open quotes and notifies sales reps — keeping pricing aligned with market reality without manual sweeps.

6. 6. Governance & observability

   Unframe Insights tracks SLA, agent decisions, override rates and €-impact, feeding a weekly review loop with the commercial leadership.

Why this approach (and not pure RPA or pure LLM)

• Pure RPA would break every time pricing rules or market indexes changed.
• Pure LLM agent would lack reliable system access, audit trail and governance.
• Unframe agentic automation combines the determinism of robots, the judgment of AI agents, and the accountability of human approvals — exactly what a market-sensitive, multi-decision process needs.

Where to start

How to spot a good automation candidate

Any area exhibiting these characteristics — across the business, the process or the organization — is a strong candidate for intelligent automation.

The more boxes a process ticks, the higher the return. Unframe agentic automation is designed for exactly these zones — where pure RPA stalls and pure AI lacks control.

Prioritize

Impact vs. Feasibility matrix

Once you have a list of candidates, use an Impact & Feasibility matrix to decide where to start. Score each use case on two axes:

• Impact — how much will it improve the process or benefit the business (time saved, errors avoided, revenue protected, customer experience)?
• Feasibility — how easy is it to deliver today, given data availability, system access, process stability and skills?

Start in the top-right quadrant — high impact, high feasibility — to deliver visible value fast, then reinvest the savings into the strategic bets that require more groundwork.