gingerlabs
The Vercel for AI Engineers
Best-in-class developer experience for building production AI systems
Company Preread
1. The Problem
95% of AI agent deployments fail in production. The issue isn't the models—it's the infrastructure.
AI engineers spend 40-60% of project time on data prep, prompt debugging, and context management. Building production RAG systems takes 6+ months when it should take weeks.
Prompt Engineering is Broken
Small changes break everything. No versioning, testing, or systematic improvement. Teams spend weeks in trial-and-error loops.
"Prompting is very brittle—change the prompt to accommodate one edge case and another will break."— Bindu Reddy, CEO of Abacus.AI
RAG Pipelines are Nightmares
40-60% of time goes to data prep. Every company rebuilds from scratch. Retrieval fails silently with wrong chunking and irrelevant contexts.
Context Management Fails
LLMs lose track in long conversations. No memory across sessions. Multi-turn workflows bloat, inflating costs and triggering failures.
Enterprise Requirements Kill Prototypes
Security, compliance, permissions, and integrating 150+ data sources are full software projects.
2. Our Solution
gingerlabs provides four core tools in one platform:
1. Prompt Training Framework
Automated ML-based optimization with version control, A/B testing, and regression detection.
Impact: 40% reduction in hallucinations
2. Enterprise RAG Engine
150+ built-in connectors, smart chunking, hybrid search, and permission-aware retrieval.
Impact: Production-ready in hours, not months
3. Context Optimizer
Intelligent compression and hierarchical memory management.
Impact: 50K tokens → 5K tokens at 95% accuracy
4. Agent Memory System
Persistent memory across sessions with episodic and semantic storage.
Impact: Long-term context retention over months
Why This Works:
- •Speed: Production-ready in weeks vs 6+ months
- •Cost: 50-70% reduction in inference costs through smart caching
- •Enterprise-ready: Security, compliance, and audit logs built-in
3. Market & Traction
Market
Enterprise AI reaching $300B by 2030 (40% CAGR). Target customers spend $50K-500K+ annually. Comparables: LlamaIndex ($45M Series B), Pinecone ($138M), LangChain ($35M Series A).
Market Segmentation
By Company Size & Maturity
| Segment | TAM | Team Size | Annual Spend | Key Pain |
|---|---|---|---|---|
| Enterprise | $120B (40%) | 5-50+ AI eng. | $500K-$5M+ | Legacy, compliance, scale |
| Mid-Market | $90B (30%) | 2-10 AI eng. | $100K-$500K | Limited resources, speed |
| Growth Startups | $60B (20%) | 1-5 AI builders | $50K-$150K | Speed to market, gaps |
| SMB/Long Tail | $30B (10%) | Small teams | $10K-$50K | (Deprioritize) |
Total TAM: $300B by 2030
By User Persona
| Persona | Population | Growth | Core Need | Primary Hook |
|---|---|---|---|---|
| AI/ML Engineers | ~300K | 35% YoY | Prompt opt., RAG | "Cut prompt time by 60%" |
| Full-Stack Devs | ~2M | 40% YoY | Fast integration | "RAG in one afternoon" |
| Data Scientists | ~500K | 25% YoY | GenAI framework | "Scikit-learn to LLMs" |
| Technical PMs | ~200K | 30% YoY | Rapid prototyping | "Test before engineering" |
| Platform/DevOps | ~150K | 28% YoY | Security, compliance | "SSO, audit logs, SOC2" |
Primary Target: AI/ML Engineers + Full-Stack Developers
Note on TAM: Total Addressable Market represents annual spend on AI infrastructure and tooling—including LLM APIs, vector databases, prompt management, RAG systems, and development platforms—projected for 2030.
Validation
50+ hours of research with AI engineers. 15+ companies in active discussions. 3 design partner commitments from healthcare tech, fintech, and SaaS.
Customer Feedback
"Enterprise RAG is way more engineering than ML. Most failures aren't from bad models - they're from underestimating the document processing challenges, metadata complexity, and production infrastructure needs.
The demand is honestly crazy right now. Every company with substantial document repositories needs these systems, but most have no idea how complex it gets with real-world documents."— Reddit user
"Prompts are terrible! Prompts perform differently for different models. They grow to become a collection of edge cases. They contain multiple components – all in one string."— Drew Breuing, Lead Data Scientist, Overtune Maps Foundation
4. Why We'll Win
Core Engineering Focus
We solve the bottlenecks that break AI agent flows: high-accuracy retrieval, precision context management, and ML-based prompt optimization. This is deep infrastructure work.
Battle-Tested Enterprise Expertise
Built 25+ production AI pipelines for 15+ clients at Yardstick (founder). Shipped advanced techniques: HyDE retrieval, ensemble methods, vLLM optimization, tabular data parsing, domain-specific embeddings, GPU deployments. Debugged accuracy failures, scaled to 1K+ concurrent users, handled complex document structures.
Strong Technical Foundation
IIT backgrounds with deep CS fundamentals, AI/ML expertise, and research orientation. We understand both the theory and the brutal reality of production systems.
Cost Optimization Built-In
Intelligent model switching, smart caching, and context compression reduce inference costs by 50-70%. Every feature is designed to make AI economically viable at scale.
5. The Round
We're raising our pre-seed round
Use of Funds
- •65% Engineering - Beta build and design partner validation
- •20% Go-to-Market - Sales, content, community
- •10% Operations - Legal, infrastructure, security
- •5% Research - Thought leadership
6-Month Milestones
- •Close 5-7 POCs at $50K each ($250K-$350K revenue)
- •Convert 1-3 POCs to annual contracts at $500K ACV ($500K-$1.5M ARR)
- •Validate product-market fit across key features
- •Establish repeatable sales process
12-Month Milestones
- •Close 10-20 POCs at $50K each ($500K-$1M revenue)
- •Convert 5-10 POCs to annual contracts at $500K ACV ($2.5M-$5M ARR)
- •2-3 production case studies demonstrating ROI
- •Establish market leadership in AI engineering tooling
Why Now?
AI engineers moving from experiments to systematic workflows. 12-18 month head start before comprehensive workbenches emerge. Our team has the battle scars—now we're building the solution.