Poka Labs

What do they actually do

Poka Labs builds an AI-first tool that automates two repetitive workflows for chemical manufacturers: converting incoming RFQs into quotes, and creating/updating production schedules. The web app connects to existing inboxes, price lists, spreadsheets, and ERPs so teams can work from real emails and files instead of re-entering data or cleaning it first (product, homepage).

For quoting, users forward an RFQ email/PDF or connect a team inbox. The system extracts requirements (product, quantity, delivery), matches them against price lists and catalogs (even with inconsistent SKUs), applies the seller’s pricing logic, and generates a draft quote that can be written back to the ERP or reviewed by sales—cutting manual entry and speeding response time (product, homepage demo). For scheduling, planners import ERP exports and historical batch data; the platform analyzes cycle times, equipment/operator variability, inventory, and constraints to generate a schedule, track batches, surface delays, and automatically update the plan as conditions change (YC profile).

Today they appear to be in early commercial use with pilot deployments and a small team; they do not publish a customer roster, but their live app and demo flows are public (YC profile, product).

Who are their target customer(s)

• Sales reps / commercial teams at chemical manufacturers handling RFQs and quotes: They spend hours copying details from emails and PDFs into ERPs or pricing sheets, struggle with inconsistent formats/SKUs, and miss or delay responses when volume spikes or data is messy.
• Production planners and schedulers at chemical plants: They constantly rework schedules by hand when batches slip, equipment availability changes, or inventory moves, and lack an automated way to keep plans current with shop-floor reality.
• Plant or operations managers: They own delivery performance and capacity and face firefighting when sales commitments don’t reflect real constraints, causing missed dates and unpredictable utilization.
• Small-to-mid sized specialty chemical manufacturers without data engineering resources: They can’t afford long ERP/data-cleanup projects and need tools that accept PDFs, spreadsheets, and inbox inputs and plug back into existing systems quickly.
• Compliance and quality teams in regulated chemical production: They maintain manual batch records and audit trails and need digital, validated capture to reduce compliance risk and prep for audits.

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

• First 10: Run high‑touch, paid pilots via warm introductions (founders’ network, YC) at specialty chemical firms; connect to inboxes and price lists to deliver clear quoting or scheduling wins and secure written references/case studies (product, YC).
• First 50: Use early case studies for targeted outbound to similar SMBs and work with ERP consultants/distributors as partners; standardize onboarding checklists/templates and package a one‑plant pilot that converts to multi‑plant rollouts, tracking metrics like quote turnaround and on‑time delivery (product).
• First 100: Productize integrations and onboarding (prebuilt connectors, self‑serve demos/playbooks), invest in content on measured wins (e.g., win‑rate scoring), and add channel coverage by region/ERP ecosystem; supplement direct sales with trade shows and a partner/referral program to scale pilots into paid rollouts (blog, YC).

What is the rough total addressable market

Top-down context:

Two adjacent global software markets frame the opportunity: MES/production execution platforms (~$15–17B in 2023–2024, ~11% CAGR to ~$30B by 2030) and CPQ/pricing (~$3.2B in 2025, mid‑teens CAGR) (Grand View Research – MES, Persistence – CPQ). Poka Labs targets a narrower slice at the intersection of quoting and capacity‑aware scheduling for chemical manufacturers.

Bottom-up calculation:

In the U.S. there are ~11,128 chemical manufacturing facilities, 68% SME-owned (~7,570). If 30–40% are specialty/process plants suitable for RFQ-to-schedule automation, that’s ~2,300–3,000 U.S. targets; extending to NA/EU yields ~5,000–7,000 targets. At an estimated $40k–$80k ACV per plant for quoting+scheduling, TAM is roughly $200M–$560M in NA/EU and ~$300M–$850M including some APAC (CISA facility count/SME share).

Assumptions:

• 30–40% of chemical facilities are relevant specialty/process operations for Poka Labs’ workflows.
• Average ACV per plant for quoting+scheduling is $40k–$80k based on comparable ops software budgets for SMB manufacturers.
• NA/EU relevant facility count is ~2–2.5x the U.S.; limited inclusion of APAC for the higher range.

Who are some of their notable competitors

• Siemens Opcenter: Enterprise MES/APS used by process manufacturers for scheduling and electronic batch/quality records. Overlaps on shop‑floor scheduling and MES; typically deeper ERP/automation integrations with longer implementations.
• PROS: Enterprise CPQ and price optimization for manufacturers. Overlaps on complex quoting, pricing rules, and deal scoring; focused on pricing science rather than tying quotes to live plant schedules.
• Poka (poka.io): Connected‑worker platform for frontline manufacturing (digital work instructions, forms, issue tracking). Overlaps on digitizing shop‑floor records; oriented to operator knowledge vs. RFQ-to-schedule automation.
• Katana MRP: Cloud MRP/ERP with inventory and drag‑and‑drop scheduling for SMBs. Overlaps on basic scheduling; generalist across light manufacturing, not focused on messy RFQs or chemical‑specific constraints.
• Citrine Informatics: Materials and chemistry AI platform for R&D and formulation. Competes for AI budgets in chemicals; focused on product development, not sales quote automation or production scheduling.