Zeit AI

What do they actually do

Zeit AI builds ZeitMind, an enterprise tool that connects to spreadsheets, ERPs, CRMs, and databases, profiles and links messy tables, and lets business users ask questions in plain English. It generates explainable reports that show how results were reached and can turn findings into small operational dashboards or apps for day‑to‑day use (site try it).

Delivery is via short, engineer‑led engagements rather than pure self‑service: a forward‑deployed engineer connects systems, cleans and models the data, and ships the first analyses and apps—promising results in weeks and “no internal IT required.” Live connections keep outputs updated automatically (implementation site). The product is live and used by teams like procurement, controlling/finance, and logistics, with public demos and testimonials published on their site and social channels (success stories YC Launch).

Who are their target customer(s)

• Procurement / sourcing teams: They stitch together supplier lists, invoices, and ERP exports to find price leaks or negotiation levers; they need reliable, explainable summaries to prepare for supplier talks.
• Finance controllers / FP&A teams: They repeatedly rebuild Excel reports and reconcile messy spreadsheets for month‑end close or ad‑hoc questions, slowing decisions and consuming senior analyst time.
• Inventory and logistics managers: They must detect waste, stock imbalance, and margin risk across fragmented warehouse/system tables, but current workflows require manual joins and guesswork.
• Consultants and auditors: They reconcile many client files and must produce traceable, explainable findings; manual file wrangling and weak provenance make audits slow and error‑prone.
• Business analysts / central BI teams at mid‑to‑large companies: They face long backlogs because each analysis requires cleaning and modeling messy tabular data, and often lack the IT bandwidth to deliver fast, repeatable answers.

How would they acquire their first 10, 50, and 100 customers

• First 10: Use founder/YC networks and early demo leads to run high‑touch, week(s)‑long engineer‑led pilots that connect systems, clean tables, and deliver an explainable report with measurable impact; capture testimonials and ROI (site success stories YC).
• First 50: Package early wins into 2–3 vertical playbooks (procurement, controlling/finance, logistics), hire a small forward‑deployed team to execute standardized installs, and run targeted outbound using case studies to shorten cycles (site success stories).
• First 100: Productize the playbooks with templates, common ERP/CRM/database connectors, and guided onboarding to reduce engineer time per account; add channel partners (consultancies/BI firms) and publish clear pricing tiers (site roadmap hints).

What is the rough total addressable market

Top-down context:

Near‑term, practical TAM aligns to data preparation (~$6.5B), data integration (~$5.9B), and procurement analytics (~$8.7B), implying roughly $20–25B with overlap considerations (IMARC data prep Gartner data integration FBI procurement analytics). Longer‑run expansion into broader business analytics is on the order of ~$96.6B+ (IMARC business analytics).

Bottom-up calculation:

Illustrative bottom‑up: assume ~120k department‑level buyers globally across procurement, FP&A, and logistics at mid‑to‑large firms, and an average $175k ACV per deployment (multi‑connector, explainable analytics + light apps). That yields ~120k × $175k ≈ $21B, consistent with the top‑down view.

Assumptions:

• Targetable buyer = a departmental deployment (procurement, FP&A, logistics) at mid‑to‑large enterprises; ~120k such departments globally.
• Average ACV ~$175k reflects enterprise data connections, explainability, and light operational apps plus limited onboarding support.
• Overlap among categories and multi‑department expansion are expected; figures are directional and exclude replacing full BI stacks.

Who are some of their notable competitors

• ThoughtSpot: Search‑driven analytics for governed cloud data; strong NLQ for curated warehouse datasets. Overlaps on natural‑language Q&A and explainability; Zeit focuses earlier on messy spreadsheets/ERPs and engineer‑led outcomes (ThoughtSpot product Zeit).
• Alteryx: No/low‑code data prep and analytics automation for analysts (visual workflows). Overlaps on table cleanup and reusable reporting; Zeit delivers engineer‑assisted deployments and turnkey explainable analyses/apps for non‑technical teams (Alteryx data prep Zeit implementation).
• Microsoft Power BI (Q&A / Copilot): Widely used BI with NL Q&A/Copilot over curated models/datasets. Overlaps on NL interface; Zeit emphasizes profiling/linking messy exports and shipping small operational apps quickly (Power BI Q&A Zeit try‑it‑out).
• Tableau (Ask Data / Tableau AI): Visualization‑first analytics; NL works best with curated data sources. Overlaps on NL access; Zeit tackles messy pre‑work (profiling, linking, provenance) and delivers explainable, actionable outputs without heavy in‑house data engineering (Tableau NL guidance Zeit).
• Google Cloud Dataprep (Trifacta): Interactive data wrangling with profiling and AI‑assisted transforms. Overlaps on cleansing and schema matching; Zeit combines cleanup with NL analysis, explainability, and short engineer‑led deployments that ship operational apps (Dataprep AI features Zeit implementation).