Agentic AI in Six Weeks: A Deep Research Capability for a Global Aerospace Leader

Industry Aerospace / Manufacturing

Key
Technologies Agentic AI, agent workflow orchestration, Retrieval-Augmented Generation (RAG), multi-step reasoning, synthesis, sentiment analysis

Services Enterprise AI

Delivered in Six Weeks

We shipped an agent-driven research workflow on schedule in six weeks — proof that advanced AI behaviors move fast when the outcomes are clear and the scope is controlled.

Agentic, Multi-Source Research

The capability handles multi-step reasoning, synthesis, and sentiment analysis across internal and external sources to answer complex analytical questions.

Scoped for Executive Impact

Instead of chasing broad "AI research," we targeted a tightly scoped, high-value use case tied directly to executive decision-making.

The Challenge

After we helped the client turn its internal RAG chatbot into a production-ready foundation, they set a higher bar: bring the kind of "deep research" experience people now expect from tools like ChatGPT into their own platform — and point it at real, business-critical questions.

So they brought us back for a second round of agentic AI development: an agent-driven workflow that pulls from internal and external sources, synthesizes what it finds, and answers complex, high-value analytical questions for the people who make business-critical decisions.

We designed, built, and grounded that capability in six weeks.

Advanced AI behavior, grounded in real decisions

Turning a chatbot into a genuine research assistant is a different class of problem than answering one question at a time. It takes multi-step reasoning, synthesis across sources, and an architecture that fits the organization's environment, risk posture, and timeline — without drifting into an open-ended, hard-to-measure "AI research" effort.

The client needed advanced agentic AI delivered quickly, but anchored to a use case where success was concrete and easy to measure.

Our Approach

Rapid iteration on complex AI architecture

We started by assessing the client's existing internal AI platform and laying out clear, enterprise-ready architectural options for agent workflows and orchestration. Through daily working sessions with their stakeholders, we moved fast but stayed disciplined — testing ideas, making pragmatic engineering calls, and landing on an approach that fit the client's environment, risk posture, and timeline.

The capability we delivered pulls from internal and external sources, synthesizes what it finds, and answers complex, high-value analytical questions — applying multi-step reasoning, synthesis, and sentiment analysis to work that used to take significant manual effort.

Targeted AI use cases with executive relevance

Rather than chase broad "AI research," we worked with the client to pinpoint one tightly scoped, high-impact use case: supporting sell-side analyst research. Together we defined two specific questions the capability had to answer — grounding the work in real executive decisions and making success easy to measure.

The Results

Agentic AI that scales past the pilot

We delivered the deep research capability on schedule in six weeks, proving that advanced agentic AI behaviors — multi-step reasoning, synthesis, and sentiment analysis — come together quickly when the outcomes are clear and the scope is controlled. That early, working proof point built confidence and momentum, and it put the client in a position to expand its AI capabilities safely and at scale.