tag-insights: /case-study/agentic-presale-automation-aws

The customer A Thailand-based enterprise cybersecurity consulting and solutions provider. The company aligns its practice to the NIST Cybersecurity Framework and ISO/IEC 27001, has delivered more than 500 cybersecurity projects, and partners with global security vendors including Palo Alto Networks, CrowdStrike, Fortinet, Check Point, CyberArk, Splunk, Zscaler, SentinelOne, Imperva, Trellix and Devo. Roughly 90% of its staff are technical practitioners. AWS Partner: G-Able Public Company Limited   The challenge The customer’s presale function had become the constraint on revenue. Producing a proposal for an AWS-based cybersecurity engagement required a senior engineer to work manually through requirement analysis, inventory mapping, man-day effort sizing, pricing, proposal writing and solution design. That sequence consumed roughly five architect man-days per proposal, and the elapsed turnaround from brief to submitted proposal ran to 10.5 days. Three problems followed from that. Speed capped pipeline coverage. The number of opportunities the team could pursue in a quarter was limited by how fast proposals could be produced, not by how many opportunities existed. Sizing and pricing varied between engineers. Standards and rate assumptions lived in individual heads rather than in a governed system, so two engineers could size materially similar scopes differently. Manual pricing carried real financial risk. AWS list prices change. A figure assembled by hand against a stale price is either a loss absorbed at delivery or an uncomfortable conversation with the customer. Left unaddressed, the immediate exposure was lost opportunities to faster competitors and margin erosion on underpriced deals. The longer-term exposure was an inability to scale presale in step with demand — the same senior architects who write proposals are the ones who deliver billable work, so presale volume and delivery capacity compete directly for one scarce resource.   Why this needed an agentic solution Proposal production is not a single generative task. It is a sequence of dependent decisions: understand the requirement, select an architecture, size it, price it against live rates, write it, then route it for approval. Each step needs different knowledge and different tools, and a mistake early propagates silently to the end. A single large-language-model prompt cannot do this reliably, because the model has no way to look up a current AWS price and no structural reason to refuse to invent one. What the work needs is an agent that plans, calls real tools against real data, and is prevented by design from producing a number it   The […]