Most "AI consulting" engagements end with a slide deck and a Notion page. We end ours with a deployed feature your customers actually use. We build production AI software — LLM applications, retrieval-augmented generation systems, agentic workflows, and deployed ML models — for teams that need AI to work reliably, cost-predictably, and without hallucinating their way into a customer-support fire. We have also spent the last three years cleaning up other people's AI-generated codebases, so we know what bad AI architecture looks like from the inside.
What We Offer
We design, build, and operate AI software end-to-end. That means picking the right model and the right pattern (RAG vs. fine-tuning vs. tool-calling vs. agentic), wiring it into your existing application, building the eval and observability infrastructure that catches regressions before customers do, and managing the cost and reliability tradeoffs that determine whether the feature ships profitably or quietly bleeds margin. We work with OpenAI, Anthropic, Google, AWS Bedrock, and self-hosted open-weight models — model-agnostic by design.
Key Capabilities
- LLM application development: Production-grade chat, copilot, summarization, classification, and structured extraction features built into your existing app with Next.js, Python, or whatever your stack already runs.
- RAG and vector search: Document ingestion, chunking strategy, embedding pipelines, and vector stores (Pinecone, Weaviate, pgvector, Turbopuffer) tuned for your actual content — not the demo dataset.
- Agentic workflow orchestration: Tool-using agents built with LangGraph, the OpenAI Agents SDK, or custom orchestration, with deterministic checkpointing and human-in-the-loop where the stakes warrant it.
- ML model deployment (MLOps): Training, packaging, and serving custom models on SageMaker, Vertex AI, or self-hosted infrastructure, with proper versioning, A/B testing, and rollback.
- Eval and observability: LangSmith, Helicone, Arize, and Langfuse integration — plus custom eval harnesses that test on your real data, not generic benchmarks. We measure quality, latency, and cost on every release.
- Multi-model gateways: Vercel AI Gateway, OpenRouter, or custom routing layers that let you swap models, add fallbacks, and negotiate pricing without rewriting application code.
- Production prompt engineering: Versioned prompts, structured outputs (JSON schema, Zod, Pydantic), guardrails, and the hallucination-mitigation patterns that come from actually shipping these systems.
Our Process
1. Discovery & Architecture
We start by being honest about whether AI is the right answer. For some problems, a rules engine, a search index, or a classic ML model is faster, cheaper, and more reliable. When AI is the right call, we design the data flow, pick the model strategy, estimate per-request cost, and identify failure modes before we write code. The output is a one-page architecture and a costed pilot scope.
2. Design & Prototyping
We build a working prototype in 2-4 weeks against your real data, with a minimal eval set scored by domain experts. This is where most teams discover their AI feature works great on five hand-picked examples and falls apart on the long tail. We surface those failure modes early so the product decision happens before the engineering investment.
3. Development & Integration
Production build includes the boring parts that determine whether the feature survives — caching, rate limiting, prompt versioning, structured output validation, fallback models, cost budgets, abuse detection, and observability. We integrate with your existing auth, billing, and logging. Every release runs through an automated eval suite before it touches production traffic.
4. Launch & Support
We launch behind a feature flag to a small cohort and watch the metrics that matter — answer quality, latency p95, cost per request, refusal rate, and user satisfaction. Most clients keep us on for ongoing eval expansion, model migration as new releases drop, and the periodic prompt and retrieval tuning that AI systems require. Models change every quarter; your software should not have to.
Industries We Serve
- Healthcare: Clinical decision support, ambient documentation, prior-auth automation, and drug interaction detection — built with HIPAA, BAA, and audit-trail requirements as architectural inputs, not afterthoughts.
- EdTech: Adaptive learning, content generation, automated assessment feedback, and tutor-style chat with the safety, FERPA, and age-appropriate guardrails that K-12 deployments demand.
- Manufacturing: Predictive maintenance, computer vision for quality inspection, document and SOP search, and operator copilots integrated with MES and historian data.
- B2B SaaS: Copilots, in-product agents, document understanding, and structured data extraction features for vertical SaaS platforms in legal, finance, real estate, and operations.