Peppr AI

What do they actually do

Peppr AI is a company-wide knowledge platform that continuously pulls information from the tools where teams work—such as Slack, Jira, Confluence, and GitHub—and organizes it into a searchable, “living” knowledge base. It summarizes activity, links people to projects and decisions, and surfaces signals like blockers and project state via a team dashboard and natural‑language queries (e.g., “what is blocking frontend?”) (Peppr website, YC profile).

Today, teams install connectors/SSO, let Peppr ingest messages, tickets, docs, and commits, and then use the generated summaries, docs, and suggested owners to get context quickly and reduce manual status-gathering. The product supports enterprise needs like SSO, RBAC, audit logs, and private cloud/on‑prem deployments, with custom integrations for larger rollouts (Peppr website).

Who are their target customer(s)

• Engineering managers at Series B+ startups and enterprise product teams: Project context is split across Slack, GitHub, and Jira, so teams miss blockers, duplicate work, and spend time chasing status instead of shipping. Peppr centralizes updates and flags issues early (Peppr website, YC profile).
• Product managers running multi-team roadmaps: Progress and decisions are scattered across tickets, docs, and chat, making true project state and dependencies hard to see until late. They need an up‑to‑date, cross‑tool view to prevent surprises.
• Internal knowledge/documentation owners (knowledge ops/IT): Docs go stale and search is poor, while managing access and compliance is manual. They want continuous ingestion, better answers, and enterprise controls to keep knowledge current (Peppr website).
• Support and incident-response leads: Incident context and postmortems are fragmented across channels, slowing resolution and making root-cause knowledge hard to reuse. They need faster retrieval and synthesized timelines.
• People/Onboarding owners at fast-growing companies: Institutional know‑how is fragmented and undocumented, so new hires take longer to ramp and rely heavily on colleagues for context.

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

• First 10: Founder‑led pilots with warm Series B+/enterprise contacts (YC alumni, investor intros). Install connectors, deliver 1–2 quick wins (e.g., surfaced blockers, auto‑summaries), and convert pilots to paid with a short‑term discount tied to a reference/case study.
• First 50: Hire an enterprise AE to run outbound to similar eng/PM buyers, standardize a short ROI playbook and demo script, and leverage initial references. Add marketplace listings (Slack/Jira/GitHub) and targeted webinars to increase qualified inbound.
• First 100: Build a small sales + success pod focused on multi‑team deals, create verticalized templates (engineering, product, incident response), and formalize security/procurement (SSO, SOC/SLA). Expand partnerships in Atlassian/Slack/GitHub ecosystems and run referral incentives and upsell plays.

What is the rough total addressable market

Top-down context:

Peppr competes within the knowledge‑management software market, estimated at about $20.15B in 2024, which serves as an upper bound on available budget for products in this category (Grand View Research).

Bottom-up calculation:

Start from ~377,000 large companies worldwide (250+ employees), estimate the share that are targetable (tech‑forward, multi‑tool workflows), multiply by average seats per deployment and a blended seat price. For example, at 1% penetration (3,770 companies) × 100 seats × $49/month, the illustrative ARR is roughly $222M (Statista, Peppr pricing).

Assumptions:

• Share of the 377k large companies that use Slack/Jira/GitHub‑style tooling and would buy Peppr.
• Average deployment size (e.g., 100–500 seats) within engineering/product/support orgs.
• Blended effective price per seat (enterprise discounts vs. listed $49/mo).

Who are some of their notable competitors

• Glean: Unified enterprise search across 100+ apps with a company knowledge graph, AI assistant, and emerging agent tooling; notable for breadth of connectors and enterprise footprint (Glean product).
• Stack Overflow for Teams (Stack Internal): Private Q&A and long‑form articles with verification workflows and integrations; notable as a developer‑centric, expert‑validated knowledge system used widely in engineering orgs (Teams).
• Atlassian Confluence: Widely adopted team wiki/docs platform with deep Jira integration and growing AI features; notable incumbent for documentation and project context in engineering organizations (Confluence features).
• Notion: All‑in‑one workspace for docs, databases, and projects with Notion AI; notable for flexible workflows and broad adoption across teams, now adding automation/AI features (Notion AI).
• Guru: Knowledge‑card system with verification, browser extension, and Slack surfacing; notable for audited, bite‑sized answers used in onboarding and support workflows (Guru features).