AI Development for
AI First Startups
AI first startups need infrastructure built by people who live in production AI every day, not generalists who just discovered LangChain. We ship agent systems, RAG pipelines, and MCP integrations that survive real load. The same hands that built widely used open source tools (CodeSight, claude-rank, Ultraship) build your AI product.
Where AI First Builds Actually Break
AI native products have a specific failure surface that generalist development shops do not see until production. Here is what we have learned from shipping AI in real environments.
Competitors Are Already Shipping
The AI category cycles in weeks, not quarters. By the time most generalist agencies finish their framework evaluation phase, three competitors have launched, raised, and started shipping v2. You need a partner who is already running in production, not still reading the docs.
LLM Integration Is Deceptively Hard
Anyone can wire up a chat completion. Streaming responses, structured outputs, retry logic, fallback models, context window management, prompt caching, token cost monitoring, and proper observability all add up fast. The first attempt looks like it works until real users find the edges.
RAG and Agent Architecture Have Real Decisions
Chunking strategy, embedding model selection, vector store (pgvector vs Pinecone vs Weaviate), retrieval reranking, query rewriting, hybrid search, agent orchestration patterns. Every decision compounds. Rebuilding a poorly architected RAG at Series A costs months.
Token Costs Destroy Margins Quietly
Without semantic caching, model routing by complexity tier, prompt compression, and batch processing, your inference bill grows linearly (or worse) with usage. Several AI startups discover at 1,000 MAU that their unit economics are negative and the fix is a multi-week rewrite.
Generalist Developers Lack Production AI Reps
A senior backend engineer can write Python. A production AI engineer knows when to use structured outputs, how to design eval harnesses, when MCP makes sense, and how to debug a hallucination cycle. Reps in this specific domain matter more than years of general software experience.
The Surface Area Keeps Expanding
MCP, computer use, multi-agent coordination, prompt caching, extended thinking, cache breakpoints, file API, batch inference, tool use, structured outputs. New primitives ship every quarter. You need a partner working in this space daily, not someone catching up via blog posts.
How We Build Production AI
We bring production AI experience from dozens of shipped systems plus our own open source tools that ship to thousands of developers every week.
Production AI Agent Expertise
We have built agent systems in production across orchestration, tool use, memory, planning loops, multi-agent coordination, and human-in-the-loop patterns. The patterns are not theoretical, they ship in our own open source projects and in client products.
Production RAG That Does Not Hallucinate
Properly chunked, embedded with task-appropriate models, retrieved with hybrid search and reranking, evaluated continuously. Built on pgvector for cost or Pinecone for scale based on your traffic profile. Includes eval harness so you can measure regressions, not just hope for them.
Cost Optimized LLM Routing
Semantic caching cuts duplicate inference. Model routing sends simple queries to Haiku and complex reasoning to Opus. Prompt caching reduces token cost on long system prompts by up to 90 percent. Most clients see 40 to 70 percent inference cost reduction after the optimization pass.
MCP and Tool Use Built In
Model Context Protocol implementation when it fits your architecture. Tool use patterns for clean integration with external systems. We build the infrastructure that future-proofs your AI surface area against the next twelve months of platform evolution.
Observable AI From Day One
Every system ships with tracing (Langfuse, Helicone, or custom), structured logging, eval harnesses, and prompt versioning. When a user reports a weird response in production, you can find the exact prompt, the exact context, the exact model, and the exact output. Debugging is not guesswork.
AI Products That Survive Demo Day
We ship systems that hold up under real concurrent load. Demo day or investor preview does not collapse on the first multi-user session. Several AI native clients have closed seed rounds in the weeks after the launch we delivered.
Production AI We Ship Publicly
The founder is the author and maintainer of several widely used open source AI tools. The same hands that wrote these will build your product.
CodeSight
Static analysis tool that compresses codebases for LLM context. 15 detectors, 11 AST extractors, knowledge mode, MCP server. Used by AI developers to fit large repos into prompt windows.
claude-rank
AI search visibility scanner with auto-fix. 10 scanners, 170+ rules, 372 tests. Free forever. Used by founders optimizing for ChatGPT, Perplexity, and Google AI Overview citation.
Ultraship
All-in-one workflow plugin: code review, SEO, AEO, GEO, Lighthouse, security, Playwright, GSC integration. Designed around proper Claude Code subagent orchestration.
Services for AI Native Stacks
Every option treats AI as a first class citizen, not as a chat widget bolted onto a CRUD app.
AI Agent Development
Custom agents, multi-step orchestration, tool use, memory. Built with Claude Agent SDK or LangChain depending on your stack.
AI MVP Development
AI native MVP in 14 days. LLM core flow, RAG retrieval, agent automation, observability, all production grade.
AI Integration Services
Add LLM features to an existing product. Useful when you have a working product and want to layer AI without a full rebuild.
MVP Development
Full stack MVP with AI built into the core workflow rather than bolted on. $7,499 fixed price.
POC Development
7 day proof of concept to validate your AI approach with real users before committing the full MVP budget.
170+
Rules Shipped in claude-rank
40-70%
Typical Cost Reduction
3
Public AI OSS Projects
Day 1
Eval Harness Built In
Ship AI Built by AI Engineers
Book a 30 minute technical scoping call. We discuss your architecture, your models, your cost profile, and your eval needs. You walk away with a fixed quote and a delivery date in writing.
Scope Your AI BuildProven Results
Real projects. Real numbers. See what we delivered.
SaaS MVP Shipped in 14 Days: From Napkin Sketch to Paying Customers
$4,200 MRR in month one
How a solo founder went from idea to $4,200 MRR in two weeks with a project management SaaS built on Next.js, PostgreSQL, and Stripe.
Two-Sided Marketplace MVP: From Zero to 200 Listings in 3 Weeks
200 listings, 47 bookings in month one
How we built a services marketplace connecting local contractors with homeowners, complete with booking, payments, and review system.
Mobile App MVP: Cross-Platform Fitness Tracker in 2 Weeks
1,200 downloads in first week
A React Native fitness tracking app with workout logging, progress photos, and social challenges, shipped to both app stores in 14 days.
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