We build AI systems that run in production, handle real workloads, and create measurable business value. Autonomous agents. Semantic search. RAG pipelines. Multi-model orchestration. The kind of AI that ships.
There's a pattern we see over and over. A company gets excited about AI. They hire a consultant or task an internal team to build a proof-of-concept. The demo works beautifully in a conference room. Everyone's impressed. Then it goes to production and everything falls apart.
The chatbot hallucinates critical information. The agent makes decisions nobody authorized. Costs spiral because nobody modeled inference economics. The system that worked on 100 test queries collapses under 10,000 real ones. According to recent industry research, only 11% of AI agent pilots actually make it to production. The rest die somewhere between "cool demo" and "real product."
This isn't because AI doesn't work. It's because production AI is a fundamentally different challenge than demo AI. The gap between "impressive in a meeting" and "reliable at scale" requires deep expertise in systems architecture, cost optimization, safety guardrails, and the unglamorous work of making things actually work.
Demo AI optimizes for "wow." Production AI optimizes for reliability, cost, safety, and maintainability. These are different engineering challenges that require different approaches.
We've shipped production AI systems across multiple industries: semantic search engines for development teams, AI-powered image editing for content creators, intelligent automation for marketing agencies. What we've learned is that the difference between success and failure comes down to a few key principles.
The first question isn't "should we use GPT or Claude?" It's "what specific problem are we solving, and is AI actually the right solution?" Sometimes the answer is yes. Sometimes a well-designed traditional system is faster, cheaper, and more reliable. We're honest about that upfront because building the wrong thing well is still building the wrong thing.
We don't build demos and then figure out how to scale them. Every system we design considers: How will this handle 10x the current load? What happens when the model hallucinates? How do we monitor quality? What's the cost per request at scale? These questions get answered in the architecture phase, not as afterthoughts.
AI systems need boundaries. Clear input validation. Output filtering. Human-in-the-loop checkpoints for high-stakes decisions. Rate limiting and cost controls. Our systems are designed to fail gracefully when they encounter edge cases-because in production, edge cases are inevitable.
Different models excel at different tasks. Claude for nuanced reasoning and long-form content. GPT for broad capability and function calling. Gemini for multimodal and speed. We architect systems that route requests to the right model for the job, optimizing for both quality and cost.
You can't improve what you don't measure. Every AI system we build includes comprehensive observability: latency tracking, quality scoring, cost monitoring, and drift detection. This isn't just for debugging-it's how you prove ROI and identify optimization opportunities.
Agents that take action, not just generate text. We build systems that can research, analyze, execute workflows, and make decisions within defined boundaries. The explosion of frameworks like OpenClaw shows the appetite for autonomous AI-but production agents require careful design around safety, authorization, and auditability that most implementations miss.
Our agent architectures include:
RAG is how you give LLMs access to your proprietary data without fine-tuning. But the difference between a RAG system that works and one that hallucinates is in the implementation details: chunking strategy, embedding model selection, retrieval architecture, and prompt engineering.
We build RAG systems that:
Traditional vector RAG works well for semantic similarity, but struggles with relational questions ("who reports to whom?"). GraphRAG builds knowledge graphs that capture relationships. We help you choose the right architecture-or combine them-based on your actual query patterns.
Search that understands intent, not just keywords. We build semantic search engines that let users find information using natural language queries-critical for knowledge bases, documentation, and internal tools where traditional keyword search falls short.
Our semantic search implementations include:
No single model is best at everything. We design systems that intelligently route requests based on complexity, cost constraints, and capability requirements. A simple classification might go to a fast, cheap model while a complex reasoning task goes to a more capable one.
Our multi-model systems handle:
Sometimes you don't need a standalone AI system-you need AI capabilities embedded in an existing product. We integrate AI features that feel native: intelligent autocomplete, content generation, automated categorization, anomaly detection, and conversational interfaces.
We work across the major providers and maintain deep expertise in each:
The backbone of any RAG or semantic search system:
We built KeenDreams to solve a problem we experienced firsthand: as engineering teams grow, institutional knowledge fragments across Slack, Jira, GitHub, and documentation. New hires spend weeks figuring out "how things work." Important decisions get lost in old threads nobody can find.
KeenDreams is a semantic search engine for software development teams. It indexes code, documentation, conversations, and project history, then lets engineers search using natural language: "Why did we choose Postgres over MongoDB for the user service?" or "What's the deployment process for the billing system?"
AI is powerful, but it's not always the right solution. Here's how we think about when custom AI development makes sense:
We'll tell you if AI isn't the right solution for your problem. Building the wrong thing well is still building the wrong thing. Our goal is solving your problem, not selling AI services.
A focused AI feature or integration typically takes 4-8 weeks from kickoff to production. More complex systems (full RAG pipelines, agent platforms) usually require 8-16 weeks. We scope projects carefully upfront so there are no surprises.
Project costs depend on complexity, but most AI development engagements fall in the $25,000-$100,000 range. We also factor in ongoing inference costs-a system that's expensive to run is a system that won't get used. We model total cost of ownership, not just development cost.
It depends on your use case. We typically recommend Claude for reasoning-heavy tasks, GPT for general-purpose applications, and Gemini for multimodal or cost-sensitive workloads. Many production systems use multiple models. We'll help you choose based on actual requirements, not hype.
Yes. We integrate with existing systems rather than requiring you to rebuild. AWS, GCP, Azure, Cloudflare, on-premise-we meet you where you are.
We design systems with data privacy in mind from the start. This might mean using models with data processing agreements, self-hosting inference, or architecting systems so sensitive data never leaves your infrastructure. We'll discuss your specific requirements during discovery.
All LLMs can hallucinate-generate plausible-sounding but incorrect information. Our systems include guardrails: retrieval-grounding (RAG), output validation, confidence scoring, and human-in-the-loop workflows for high-stakes decisions. We design for the failure modes, not just the happy path.
Let's discuss your use case and design a system that actually ships.
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