Reduction in manual time on a critical go-to-market process
Of engineering expertise encoded into a single AI knowledge assistant
Engineers self-serve answers previously locked in hundred-page reports
An engineering consultancy with more than 40 years of geotechnical and environmental engineering expertise faced a quiet but consequential risk: that knowledge lived in the heads of senior engineers nearing retirement and in hundred-page reports and project assessments that nobody outside the original team had time to read.
As experienced engineers transitioned out of the business, institutional knowledge was leaving with them, young engineers faced a long ramp, and adjacent teams across sales, marketing, and delivery had no efficient way to access the technical content their work depended on. The firm wanted to encode that expertise into a single AI knowledge assistant that could guide future projects, train younger staff, and democratize knowledge previously locked in long-form documents.
OneSix designed and built an AI knowledge assistant grounded in the client's own engineering content, drawing on its experience with data warehouses, retrieval-augmented generation, and large language models to customize an enterprise solution built on Azure AI Foundry and Microsoft Copilot.
A purpose-built pipeline indexes and organizes text, image, and PDF materials across the company's library of reports and project assessments, making the underlying knowledge addressable in real time. Engineers can query the assistant in natural language and receive accurate, cited answers, with the source material available for verification. An auxiliary agentic chatbot generates brand-aligned creative with the same citation discipline, cutting manual time on a critical go-to-market process by 90% and allowing the marketing team to move with speed and confidence.
The entire system is deployed within the client's existing cloud infrastructure, preserving data privacy and control. The result is a sanctioned, secure alternative to ad hoc personal GPT use, a material reduction in key-person risk as senior engineers transition out, and a translation layer running in parallel across sales, marketing, and delivery, giving each function direct access to the technical content their work depends on.
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