Hivel (hivel.ai)

Hivel (hivel.ai) — Cross-organization measurement of AI's realized effect on software engineering, joined to delivery outcomes. The AI Impact Analytics product links AI tool consumption (seats, tokens, credits, model usage) to measured outcomes - cycle time, rework, hotfix resolution, coding time and delivery health - and segments engineers into power, regular, occasional and inactive AI-user cohorts so the correlation between heavy AI use and faster, cleaner shipping can be tested directly. The SURGE framework structures this as Spend / Utilization / Recovery / Gain / Efficiency, i.e. whethe…

Hivel (hivel.ai) offers (Alternative, Fundamental, Sentiment) — Not disclosed; inferred from architecture rather than a stated figure. Each deployed organization contributes continuous pull-request, review, deploy, defect and work-item events plus per-engineer AI consumption records and periodic experience survey responses, retained historically since AI rollout. Across a client base that includes multi-thousand-engineer enterprises, the corpus plausibly reaches tens of millions of engineering events with AI-utilization attribution. The differentiated property is that consumption (seats, tokens, credits by model) and outcome (cycle time, rework, defects) are already joined on one cohort-normalized basis.

Primary buyer use: board-level AI ROI justification, AI budget allocation across tools, identifying where AI seats are bought but not used, and R&D cost capitalization for finance. Information-frontier use: an aggregated cross-company panel of AI seat utilization versus realized delivery gain - the closest available measurement of whether AI coding spend actually converts into output. Concretely that supports estimating the true elasticity of engineering output to AI tooling, the diffusion curve of AI adoption by cohort across firms, whether defect and rework rates deteriorate as AI share rises (the quality counterweight most vendor claims avoid), engineering labour demand displacement, AI inference consumption per unit of delivered software, and - via the capitalization module - a systematic read on how much software development cost is being capitalized versus expensed

The data is with 3 years of history.

Coverage spans US; Application Software; alternative, fundamental, sentiment; equities.

The supplier's own access route is a contact form. Request a sample through Brickroad to start.