Hivel (hivel.ai)

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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. whether an organization's system converts AI budget into output. Around it sits a broad engineering-telemetry surface: DORA delivery metrics, SPACE developer-experience metrics, pull-request review and merge cycles, coding hotspots, code churn and tech-debt quadranting, work-item detail, R&D cost visibility and capitalization for finance, plus team-health and burnout indicators. Historical cohort trends are retained since AI rollout, and a named enterprise reference (Freshworks) reports throughput, rework, feature-count and time-to-market outcomes.

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Coverage

Universe, instruments and categories.

Industry: Application Software
Instruments: equities
Categories: AlternativeFundamentalSentiment
Regions: US
Sample tickers: MSFTGOOGLMETAAMZNORCLCRMNOWACNCTSHWDAY

Dataset card

Type: not stated
Format: Multimodal, predominantly Tabular event and time-series telemetry: AI-tool consumption records (seats, tokens, credits by model), issue-tracker and version-control events (pull requests, review and merge timings, work items), outcome labels (defects, hotfixes, rework), survey-based developer-experience and team-health responses, and derived scorecard aggregates (Hivel Score, Hivel Quadrant, Investment Profile). Consumed through dashboards and framework reports rather than an advertised public data API
Volume: 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
Users: Not a registered-user business - a B2B platform sold to engineering organizations, so the population is tracked engineer seats rather than registered users. Scale proxy: at least one named enterprise deployment with a published results case study (Freshworks) and clients large enough to require CTO, VP Engineering and CFO-level solutions, implying tens of thousands of tracked engineer seats across a client base in the tens to low hundreds (inferred from ~58 employees, seed funding and enterprise reference customers).
History: 3 years
Update frequency: not stated
Growth: Active - a current framework product (SURGE) is being actively marketed with a gated download as lead capture, the AI Impact and AI code-telemetry modules are recent additions to an already broad platform, and enterprise reference results are published. The company presents as post-seed and expanding its module surface rather than maintaining a static product
Launched: ~2023-2024 for the company and its core engineering-analytics platform (provider record shows a seed round of $1M and a company profile created in that window, and the firm presents as San Francisco-based with an established module catalogue), with the AI Impact / AI-telemetry line newer still (~2025-2026, reflecting the emergence of AI coding tools as a measurable spend category)
Delivery: not stated
Entity mapping: not stated
Sample: not stated
Point-in-time: not stated
Licence: not stated

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