Early Stage Investment Opportunity

POMA AI - The Context Engine

Every company's competitive advantage lives in their knowledge—contracts, policies, customer data, domain expertise. Getting that knowledge into agentic AI context—properly and at scale—is the problem everyone talks about. Poor ingestion and chunking waste up to 99% of computational power while agents hallucinate. POMA AI solves this, enabling intelligent knowledge orchestration for agentic systems.

Funding Round: Total 1.5 Mio € CLA, 500k € left
Convertible Terms: 8 Mio € Cap, 25% Discount

RAG Pipeline Economics: Where Quality Is Won or Lost

Retrieval Augmented Generation (RAG) looks simple on paper — ingest documents, retrieve relevant passages, then answer with correct context. In practice, the quality ceiling is set much earlier, at the chunking layer. Most failures are system design problems, not LLM IQ problems.

RAG Pipeline

Data Sources

PDFs, pics & video, contracts, xls, tickets, transcripts, wikis, code — the raw context that defines the knowledge boundary of your system.

Ingestion

Parse, normalize, and clean raw documents. Extraction quality here propagates directly into chunk and embedding quality downstream.

Chunking

The highest-leverage decision in the pipeline. Determines what each vector "means" and what evidence can ever be found at query time.

Vector Store and Retrieval

Ranks chunks by similarity — often with hybrid search and reranking — before passing raw context to the LLM for synthesis. Bad retrieval = hallucination risk.

Notable Companies

Sharepoint, Google Drive, Confluence


Llamaparse (LlamaIndex), Unstructured.io, Mistral OCR, POMA AI


POMA AI. No other players with proprietary tech, naive linear chunking only


MongoDB, Pinecone, qdrant, weaviate


The reality: enterprise RAG pipelines break on hard queries while token waste explodes

The Core Tradeoff

Precision vs. context continuity. Smaller chunks improve retrieval precision but lose surrounding context. Larger chunks preserve continuity but introduce noise that degrades similarity matching.

Why Chunking Is the Quality Gate

Every downstream component — the retriever, reranker, and LLM synthesizer — operates on whatever evidence chunking surfaces. Bad splits produce confident but wrong answers. The retriever can't fix what was never retrievable.


Naive Chunking Kills Context And Creates Orphans

The Model Doesn't Say "I Don't Know" - It Hallucinates

Getting It to Work Costs Too Much Time and Money


In 2026 alone, enterprises are projected to waste over $10 billion (and its equivalents in wasted energy and avoidable CO₂ emissions) annually on inefficient document processing methods.

As of April 2026, the top individual user on Meta's internal 'Claudeonomics' leaderboard consumed 281 billion AI tokens in a 30-day period

"Compute costs are more expensive for us than a lot of other things."

— Winston Weinberg, Founder & CEO, Harvey

The POMA AI Context Engine Solves This

One API Call. The Entire Retrieval Stack.

Key differentiator: Pipelines based on POMA AI deliver more relevant context and have a lower total cost of ownership — not marginally, but structurally so.

1

Document Ingestion

Any format, at scale, with free ingestion on all paid tiers.

2

Intelligent Chunking

Proprietary and patented, context-preserving chunking. No meaning lost at boundaries.

3

Embedding + Storage

Managed vector storage. No infrastructure to provision or maintain.

4

Precise Retrieval

Accuracy-per-token: fewer tokens needed, lower cost, correct answers.

Superior Recall Per Token Proven

In our officeqa benchmark, traditional solutions either do not find the answer due to context window overflow or require more than 4x the token budget. Only POMA AI does and delivers high accuracy per token.

Note: for some questions, naive chunking and unstructured.io require a token budget above 1 mio tokens, which in production leads to context window overflow, resulting in the correct context not being found.

Full benchmark: https://github.com/poma-ai/poma-officeqa

The Stakes Are Too High for Context to Fall Short

In legal, finance, healthcare, and regulatory a wrong answer isn't an inconvenience. It's a liability. These are the sectors where retrieval accuracy has a price tag and it's exactly why many systems are stuck in POC.

$10.4B Legal AI market by 2035

28.3% CAGR. LLM-based drafting, automated compliance, and AI-assisted litigation tools.

$10.3B AI in finance market by 2029

38.1% CAGR. Data analysis, automation, KYC, AML, risk reporting, loan files, insurance claims.

$13.9B clinical documentation market by 2030

28.3% CAGR. AI-driven documentation systems and retrieval from clinical records, EHRs, insurance documents.

$12.3B RegTech by 2030

36.9% CAGR. Compliance management, fraud detection, regulatory radars.

POMA AI's Addressable Slice — Bottom-Up

  • SAM: ~9,000–16,000 EU SMEs/scale-ups in target verticals with document-heavy AI use cases
  • Enterprise upside: the large enterprise market unlocks via a sales-led motion

What Retrieval Failure Costs in These Verticals

  • Legal: A missed precedent or misquoted clause — €50K–€500K in malpractice exposure per case
  • Finance: An AML false negative — €1M–€50M in regulatory fines (EU AMLD)
  • Healthcare: A wrong clinical document retrieval — patient safety risk, GDPR liability
  • Regulatory: An inaccurate compliance report — license suspension, reputational damage
  • Plus: hundreds of man days to duct tape a leaky pipeline

At €0.00025/query, POMA AI's cost is trivial versus the downside it prevents.

Two Products. One Platform. Every Entry Point.

POMA AI meets customers where they are — whether they're building from scratch, augmenting an existing pipeline, or just need better document processing.

POMA Grill: Context Engine (CaaS)

Fully managed retrieval pipeline. Replaces the entire in-house ingestion, storage and retrieval stack. Per-query pricing with monthly tiers that auto-incentivize upgrades.

POMA PrimeCut: Ingestion+Chunking Only

Intelligent extraction and chunking starting at €0.003/page, pay-as-you-go. For teams that want POMA AI's chunking quality without touching their storage and retrieval layers. Immediate value, zero commitment.

CaaS - Simple Usage-Based Pricing

Free ingestion on all paid tiers. No hidden infrastructure fees. Upgrade economics are automatic — we scale with usage.

Healthy margins and pricing power once established in customer's tech stack.

Go-to-Market: PLG First, Sales-Led at Scale

POMA AI's GTM is engineered so that upgrade economics are automatic. Customers don't need convincing — the math does it for them. Developer adoption at the free tier seeds the commercial pipeline.

Phase 1 — Product-Led Growth (Now)

Developer self-serve. No credit card required for the free tier. Sign up in 2 minutes, integrate in 15.

  • Target: 100 free signups/month
  • 8% Free-to-Paid conversion


Phase 2 — Sales-Led (€899+ MRR)

Advanced and Enterprise tiers are sales-assisted. Annual contracts with 20% discount. Enterprise = full consolidation sale, replacing 2–4 in-house engineers.

  • Enterprise tech budget savings: €185K–525K/year per customer
  • Annual contracts create revenue predictability and reduce churn

Selected Early Customers — Live in Production

ki-ra.eu

RAG for Regulated SME

Talk to your data

ki-ra builds RAG pipelines for small to medium sized law firms, insurance brokers and other regulated players in a secure cloud environment allowing them to harvest their data for business intelligence.

20,000+ pages ingested/month

HeyData

Compliance Automation — Regulatory Vertical · heydata.eu

Regulatory document retrieval for 2,000+ SME compliance customers

HeyData's compliance platform covers GDPR, NIS2, and ISO 27001 for SMEs. POMA AI ingests 3,000–5,000 pages/month of regulatory frameworks, audit documentation, and legal guidelines — powering accurate compliance Q&A across HeyData's customer base.

3,000–5,000 pages ingested/month · Regulatory vertical

Heuristiq — Channel Partner

Channel Multiplier · RAG for Regulated Industries · heuristiq.co

AI strategy and RAG implementation specialists for regulated industries — legal, compliance, and PE-backed companies. Heuristiq builds RAG pipelines for POMA AI's core target verticals and actively recommends POMA AI to their client base as the managed retrieval layer.

4

Early Customers

Live in production

10-20K

Pages/Month

Ingested per customer

3

Verticals

Regulatory, Finance + Sales Intelligence

2

Channel Partner

Specialist RAG advisors

Unique Competitive Positioning

Chunking is where the context pipeline is won or lost. It's the only stage no competitor has solved at production quality — except POMA AI.

🟢 = core competency
🟡 = part of offering but not core competency
🔴 = not part of offering

Competitor deep dive:

Three-Layer Competitive Moat

POMA AI's defensibility isn't a single feature — it's three compounding layers that get harder to replicate with every customer added.

Free Ingestion Lock-In

Customers pay nothing to index — but switching means re-ingesting their entire corpus plus weeks of re-validation. Data network effect.

Proprietary, Patented Chunking as Core IP

Ingestion + intelligent chunking in a single managed pipeline. Not replicable from off-the-shelf components. Improves as the ingested corpus grows.

Customer Feedback Loop

Customer feedback on retrieved context quality. Aggregated across customers, this is a proprietary training dataset no new entrant can replicate without first acquiring customers.

Our Team

Experienced team already in place - focus on execution not on hiring.

Dr. Alexander Kihm

Founder, CEO & MD

Ph.D. Big Data Econometrics at German Aerospace Center

Serial entrepreneur:
Advo Assist, fairr (exit to Raisin)

25+ yrs coding experience

Business & Sales Team

Chief Commercial Officer & MD (signed)
Serial entrepreneur, PhD, 20+ yrs in startups

Annika, Head of Marketing
10+ yrs experience marketing & growth

Florian, Business Developer
7+ yrs experience

Jens, Finance & Legal (ext)
20+ yrs experience in finance, legal, startups (exit to Raisin)

Product & Engineering Team

Ryan, Director of Product
15 yrs product experience as founder and manager
Sepehr, Gen AI Developer
10+ yrs machine learning experience

Fabia, Senior Developer
PhD, 10+ yrs coding experience

Raffael, Senior DevOps
10+ yrs backend

Suat, Senior UX Dev
10+ yrs full-stack

Alexander, Sales Dev:
5+ yrs coding experience, BSc thesis on AI+RAG

Selected Team LinkedIn Profiles
Hiring Roadmap
  • Account Executive (Q4 2026)
  • Customer Success (Q1 2027)

Funding — Capital Stack & Use of Funds

Total Capital Available — ~€2.5M+

CLA Seed Round

€1.5M · €1M committed · €500K open · Rolling CLA
Terms: €8 mio cap, 25% discount

Status: CLOSING

Public Grants & Loans

~€900K · Invest program · Expected Q3-2026

Status: IN PROCESS

Cloud Credits

>$200K · Google Cloud · AWS · Azure · Stripe · Infrastructure runway through end of 2026

Status: SECURED

Cloud credits effectively eliminate infrastructure COGS through end of 2026 and beyond, extending cash runway materially.

Product & Engineering — 55%

CaaS offering & Ingestion API · Complete retrieval pipeline · Accuracy benchmarks

Go-to-Market — 30%

Developer marketing · Content & SEM/SEO · First sales hire at Business tier · Channel partner activation (like Heuristiq)

Operations & Compliance — 15%

Legal · Finance · GDPR compliance · Runway buffer

Investors (selection)

heartfelt.vc

Berlin-based early-stage VC

b# (b-sharp)

Berlin-based deep tech VC

Till Faida

Founder, Adblock · Serial entrepreneur & angel investor

Dr. Jan Wildhirth

Partner & Global Head of AI, Fieldfisher

All investors committed within current CLA round.

€500K remaining to close · Total round: €1.5M CLA · Runway to late 2027 incl. public funding

Next Steps & Timeline

We have a clear roadmap to rapid scaling with defined KPIs and milestones to track progress and prepare for our Seed Round in 2027.

H2 2025

✓ Company founded
✓ Launched beta program with German pilot customers

✓ Launched ingestion API for PDF and 50+ file formats
✓ Hired DevOps, Gen AI and BizDev

Q1 2026

✓ First revenue milestone

✓ Published benchmark and full go-live

✓ Patent granted

✓ Hired Director Product & Head of Marketing

Q2 2026

Close Funding Round

Launch CaaS

Sign CaaS Design Partners

✓ Hired CCO


H2 2026

First CaaS revenue

Identify key initial vertical for scaling

First advanced tier customer


2027

Business and Enterprise tier sales-assisted motion

Seed round ready

Ground-Floor Investment Opportunity

at the AI and Energy Inflection Point

POMA AI is positioned to transform how companies leverage AI by eliminating the massive inefficiencies in current document processing systems.

With patented technology, European data sovereignty, and a clear path to commercialization, we invite you to join us at this critical inflection point in AI adoption and Decarbonization.

Contact: Dr. Alexander Kihm