AI that actually ships.
We build production LLM products — RAG pipelines, agentic workflows, voice and vision features — grounded in your data and shipped behind your login. Not demos, not chatbots.
The things we'll
actually ship.
- Retrieval-augmented generation over your internal knowledge
- Agentic workflows with tool-calling and deterministic evals
- Voice agents with OpenAI Realtime, ElevenLabs and Whisper
- Vision pipelines for OCR, moderation and product tagging
- LLM observability, token budgets and offline evals
- Compliance-aware prompting and PII redaction
Common
questions.
What kind of AI products do you build?
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Production AI features wrapped in evals, tracing, cost ceilings and rollback — not demos. RAG-grounded chatbots, AI co-pilots (RealProfits, Wholosophy), agentic workflows, voice and vision features. We work across OpenAI, Anthropic Claude, Google Gemini and open-source models.
Do you do RAG, fine-tuning, or both?
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RAG first for almost every problem — it's faster to ship, easier to evaluate and easier to update. We reach for fine-tuning when the problem is genuinely about style, domain-specific structured output, or hard latency targets that RAG can't meet.
How do you handle hallucinations and AI safety in production?
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Eval sets before launch (50-200 input/output pairs scored on every prompt change), online evals on a rolling traffic sample, PII redaction at the boundary, safety classifiers before and after generation, and feature-flag-controlled rollback on every prompt. Detailed in our insights essay on shipping LLM features.
What does an AI development project typically cost?
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AI-feature engagements start at $40–80K for a focused production feature (8–12 weeks, senior AI + product engineer). Full AI-native product builds range $150K–$600K. We never charge for time spent on demos that won't survive production.