A curated multi-section prompt framework that transforms a single company name into a comprehensive 13-section technology diligence briefing in minutes — with live-data tool calling, seller-aligned insights, and a structured output designed for account executives.
Before an enterprise account executive walks into a first call, they need deep knowledge of the prospect's technology stack, financial signals, strategic priorities, and executive landscape. Getting that right requires hours of research — which most sellers don't have time to do well.
An AE preparing for a first call with a Fortune 500 company needs to check LinkedIn, 10-K filings, recent news, job postings, technology press, and the company's own website. Doing it right takes half a day. Most sellers shortcut it.
Some sellers are excellent researchers; most aren't. The quality of pre-call intelligence varies wildly across a sales team. There's no repeatable, standardized output format that every seller uses to walk into a meeting prepared the same way.
Generic AI summaries give you a company overview. They don't tell you which of your service lines has the highest probability opportunity, what the evidence is, and what the specific entry point conversation should be. The connection from research to sales motion is left as an exercise for the reader.
Manual research often misses real-time signals: a stock at a 52-week low signaling cost pressure, a CFO change six months ago, a recent acquisition that just created a cloud sprawl problem. These are the conversation openers that make cold calls warm.
Even well-researched briefings rarely include a stakeholder map: who is the economic buyer, who is the technical champion, who controls procurement, who will block the deal. This information exists in public sources — but synthesizing it takes expertise.
When research teams do produce briefings, they're often 20-page PDFs that sellers don't read before calls. The format isn't built for how sellers actually consume information — skimmable sections, prioritized opportunities, clear next actions.
The platform ships two prompt configurations deployed on the same Azure AI Foundry infrastructure. Each mode has a distinct system prompt, a different output structure, and different tool orchestration rules tuned to the research task.
General-purpose pursuit intelligence for consulting and advisory teams. Analyzes any company for technology transformation opportunities, cloud maturity gaps, AI/ML adoption signals, data platform needs, and integration challenges. Produces a layered research brief that feeds into proposal and business case development.
Rackspace-specific seller enablement tool. Every section ends with an explicit RACKSPACE SELLER INSIGHT that names the specific service line, the evidence for the opportunity, the recommended entry point, and the competitive risk. Output is a complete 13-section briefing with a Top 3 Focus Areas summary and full Citation Index.
The agent's quality is not primarily determined by the model — it's determined by the prompt. The Seller Diligence prompt is a precision-engineered specification covering role definition, execution rules, tool orchestration, output schema, and quality constraints. Each layer solves a specific failure mode observed in unstructured AI output.
Claude uses five tools during report generation. Each tool has explicit usage rules in the system prompt — preventing token waste, controlling cost, and ensuring the right data sources are used for each section.
CTO — Jane Mitchell (appointed March 2024) joined from AWS where she led the Enterprise Migration team. Her hire is a strong signal of cloud-first intent. [Acme Press Release, Mar 2024]
CFO — Robert Chen (since 2022) has made public comments on "optimizing IT infrastructure costs" in three consecutive earnings calls, citing cloud spend as a target area. [Q3 2025 Earnings Call Transcript]
Acme posted 14 open roles tagged "cloud infrastructure" and "AWS" in Q1 2026, concentrated in the new CTO's engineering org — suggesting active build vs. buy evaluation. [LinkedIn Jobs, Apr 2026]
VP Infrastructure — David Park previously ran managed services procurement at a prior employer and has a public track record of vendor consolidation. [LinkedIn Profile]
CTO background in cloud migration + CFO cost pressure + VP Infra's procurement track record = classic managed services motion. Open cloud infrastructure roles signal active investment with limited internal expertise. Lead with a FinOps assessment offer to CFO; position managed cloud to CTO as a talent multiplier, not a cost — she needs her team on innovation, not ops.
GET /api/v1/chat/progress/{request_id} at 500ms intervals.
As each tool call fires, an event is written to _progress_store in memory,
keyed by a UUID sent at request start. The UI renders live status cards:
"📰 Fetching news — CompanyName cloud AI…", "🌐 Reading investor relations page…".
stock-data (5 min), news-articles (1 hour),
website-content (24 hours). A second research session on the same
company within the cache window uses cached data — reducing API costs and latency.
get_company_news endpoint fans out to four sources in parallel:
NewsAPI, GNews, Bing News Search, and MediaStack. Results are deduplicated by URL
(exact match) and title similarity (first 50 characters), then sorted by
publishedAt descending. The resulting set is the most recent unique
articles across all sources — not just what one API returned.
POST /api/v1/chat endpoint handles both agents. The request
body includes a prompt_type field ("pe" or "rxt"),
which routes to either PE_SYSTEM_PROMPT or RXT_SYSTEM_PROMPT.
Both use the same tool definitions, the same Cosmos DB cache layer, and the same
Azure AI Foundry model — but produce structurally different outputs because the
system prompt specifies a completely different output schema and section sequence.
The primary ROI is time recovered. But the secondary effect — consistency and depth — is often more valuable: every seller on the team walks into every first call with the same quality intelligence, regardless of their individual research skills.
The agent's intelligence isn't in the model — it's in the specification. A well-engineered system prompt is a behavioral contract that produces consistent, structured, seller-aligned output at scale.
Azure AI Foundry · Claude Sonnet 4.5 · Azure Functions · Cosmos DB · Python 3.11