Context Engineering
.png)
Context engineering is the practice of deliberately assembling and structuring the information a pursuit team receives before and during a proposal effort, so that decisions, writing, and positioning are grounded in verified facts rather than assumptions or memory.
Why the term matters in AEC pursuits
Most proposal failures are not failures of writing. They are failures of context: the team did not know about the prior relationship with the client, missed a fee dispute from three years ago, or wrote to a project scope that had already shifted. The word "engineering" is intentional here. Assembling context is not passive retrieval; it requires deliberate selection, sequencing, and filtering of what information reaches the team, when, and in what form. In a two-week proposal timeline, a team that starts with structured context on the client, the project type, and the relevant firm experience compresses days of internal archaeology into hours.
What context engineering looks like in a pursuit workflow
Practically, it means pulling the right inputs before the kickoff call, not during it: past performance on comparable project types, the pursuit history with this specific client, which staff members have direct relationships, and any prior debriefs from losses on similar work. For federal pursuits requiring an SF-330, that means having Section F project examples pre-qualified against the NAICS code and evaluation criteria before anyone opens a draft. For QBS-governed work under the Brooks Act, it means surfacing qualifications data in a form the selection committee will recognize, not in the form it was originally filed.
The institutional knowledge problem context engineering solves
AEC firms accumulate enormous amounts of pursuit-relevant knowledge across proposals, project closeouts, CRM notes, and the heads of senior staff. Almost none of it is findable under deadline. A BD director with 25 years at a firm may carry accurate, detailed client intelligence that exists nowhere in a system; when that person is traveling during a shortlist preparation sprint, the team improvises. Context engineering treats that institutional knowledge as infrastructure to be captured and surfaced systematically, not recalled on demand. Kantiv is built around this problem specifically: it captures context from proposals, project data, and client history, then surfaces the relevant subset when a new pursuit begins, so the team starts from a verified baseline instead of a blank page.
Related terms