LLM-powered products, RAG systems, custom ML pipelines, and computer vision — built for real users with real latency budgets. 15+ AI products shipped, evals in CI, monitoring in production.
Most AI projects die between prototype and production. The demo wows the CEO; six months later there's no monitoring, no evals, hallucinations are eating customer trust, and the bill from OpenAI is three times the forecast.
We build AI products like we build any other production system: with evals, observability, cost monitoring, fallback paths, and an incident runbook. Prompt engineering is treated as code — versioned, tested, reviewed.
Everything needed to take your project from idea to production — and keep it running.
OpenAI, Anthropic Claude, Llama, Mistral, Gemini. Multi-provider abstractions, fallback routing, structured outputs, function calling, streaming.
Vector databases (Pinecone, Weaviate, Qdrant, pgvector), hybrid search (BM25 + dense), reranking, citations, chunking strategies that actually work.
Customer support agents, sales assistants, internal copilots. Tool use, memory, persona consistency, guardrails against jailbreaks.
Object detection, OCR, image classification, document understanding. Fine-tuning on YOLO, ViT, CLIP, Segment Anything. On-device inference where it fits.
Fine-tuning LLMs on your domain data, LoRA adapters, embedding model training, classical ML for tabular data.
Eval suites in CI, prompt regression tests, hallucination detection, PII redaction, jailbreak resistance, output filtering.
Same playbook on every project. Predictable timeline, fixed cost, daily communication.
Is this actually an AI problem? Sometimes a SQL query beats a fine-tuned model. We tell you honestly when AI isn't the answer.
Build a working prototype in 2-3 weeks with a measurable eval suite. Test against baselines: GPT-4, Claude, your existing solution.
Cost monitoring, latency budgets, retries, fallbacks, caching, structured logging, prompt versioning. Boring infrastructure that keeps things running.
Production evals, drift detection, user feedback loops. Models and prompts get versioned and reviewed like any other code.
We're framework-agnostic — we pick what fits your project, your team, and your hiring market.
Every LLM feature ships with a regression eval suite. We don't 'feel' that a prompt got better — we measure it.
Per-feature, per-user cost tracking. We've cut LLM bills 60% on takeover projects through caching, model routing, and prompt compression.
We'll tell you when an LLM is the wrong tool. Some things rules-engines, search, or a SQL query do better, faster, and cheaper.
They shipped our RAG-based support copilot in 9 weeks. The eval suite they built is now used by our internal ML team for every prompt change. Deflection rate hit 47% in month two.VP Engineering, B2B SaaS, Series B
Tell us what you're building and we'll come back within 24 hours with a fixed-cost plan, timeline, and the team we'd assign to it.