AI Strategy & Implementation
Your team is building AI features. The hard part isn't the technology—it's figuring out which problems AI actually solves, which models to use, and how to build for production instead of demos. We've built (and debugged) enough LLM systems to know what works and what burns money.
Deep Expertise in Modern AI
Large Language Model Integration
Choosing between models from OpenAI, Anthropic, Google, or open-source alternatives isn't about features lists—it's about what works for your specific use case and budget. We've been in the trenches figuring out which models handle your workload best, how to prompt them effectively, and what to do when things go wrong. The right model matters, but knowing how to use it (and having a fallback) matters more.
Context Engineering & RAG Systems
RAG sounds simple: stick your docs in a vector database, retrieve relevant chunks, feed them to an LLM. Then you realize your chunking strategy is wrong, your embeddings don't match how users actually ask questions, and you're retrieving 20 chunks when the LLM only needs 3. We've rebuilt RAG systems where the fix wasn't better embeddings or a different vector database—it was rethinking how documents get split and indexed in the first place. The difference between technically correct and actually useful.
AI Agents & Multi-Agent Orchestration
Agents fail in creative ways. They call the wrong API. They loop forever trying to verify their own work. They complete the task but expose sensitive data in logs. Building reliable agents means constraining them enough to prevent disasters while giving them enough autonomy to be useful. We build agent systems that can reason, use tools, and collaborate—with guardrails that catch failures before they impact users. The result: AI agents that complete multi-step workflows reliably, with full audit trails and error recovery built in.
Production Architecture & Performance
Your AI demo works great. Now scale it—100k users? 1m? 5m?—and watch what breaks: costs explode because you're sending too much context, responses slow to a crawl during peak hours, the LLM starts hallucinating on edge cases you didn't test. We design systems that handle API outages, rate limits, and cost spikes. This means prompt caching (cut tokens by 50%), streaming (users see results faster), fallback mechanisms (when the primary API goes down), and intelligent batching. Production AI means building for these realities, not optimizing for the demo.
AI Monitoring & Observability
You can't debug what you can't see. When your LLM feature starts failing, you need to know: Was it a bad prompt? Wrong context? Model API issue? Jailbreak attempt? We build monitoring that tracks token costs (before you get a surprise bill), quality metrics (so you know when responses degrade), latency (under 2 seconds or users bounce), and user feedback (the only metric that actually matters). If something breaks at 2am, you'll know exactly what and why.
AI Security & Governance
LLMs will happily leak customer data, ignore safety instructions, or hallucinate compliance policies if you let them. We build secure systems with input validation (catch prompt injection), output filtering (prevent data leakage), PII detection and redaction (before it hits logs), and proper audit trails (know who asked what). Working in healthcare or finance? We handle HIPAA, SOC 2, and regulatory requirements so your AI system doesn't become a compliance nightmare.
AI Strategy & Technical Advisory
Not every problem needs AI. Sometimes simple automation works better. Sometimes you should buy instead of build. We help you make decisions that matter: which use cases to tackle first, which models actually fit your budget and latency requirements, whether that fancy vector database is worth it (usually not for datasets under 10k documents). We've seen enough AI projects to know what works, what's overhyped, and what's worth the investment. Honest technical guidance from someone who builds this stuff, not sells it.
Why Work With Us on AI
We've Been Here Before
We've debugged agent loops calling the same API 47 times per request. We've refactored prompt templates burning $3k/day on unnecessary tokens. We've implemented guardrails for LLMs confidently hallucinating company policies. We've optimized RAG systems from 32k tokens down to 4k without losing accuracy. That hands-on experience means we know what breaks in production, not just what works in demos.
Business First, Technology Second
The hardest part of AI isn't the technology—it's figuring out where it actually helps. We start with your business problem, not the latest AI trend. Sometimes the answer is a simple regex, not an LLM. Sometimes you need the full multi-agent system. We tell you which is which, even if it means less work for us. Your AI investment should solve real problems, not just check boxes.
Production-Ready, Not Just Proof-of-Concept
Demo working? Great. Now answer these: What happens when the API goes down? How do you handle requests in Spanish when you trained on English? What's your cost at 100x scale? How do you version prompts without breaking production? We build systems with answers to these questions built in, not bolted on later when they become emergencies.
Transparent About Costs & Tradeoffs
AI gets expensive fast. Your POC costs $0.50 per request. At 100k users, that's $50k/day. We're upfront about these numbers. Want the flagship model? Here's what it costs at scale. Maybe a smaller model gets you 80% of the quality for 10% of the price. Maybe prompt caching cuts your bill in half. Maybe you don't need a vector database at all. We help you make informed decisions about where to spend and where to save.