AI features that solve real problems.

An AI tooling project in 2026 typically costs $5,000-$150,000+ depending on scope. A single AI feature MVP (RAG chatbot, document classifier, semantic search over your knowledge base, drafting assistant) runs $5,000-$25,000. A production-grade AI integration with proper eval framework, prompt versioning, fallback provider hedging, and observability runs $25,000-$80,000. Custom ML pipelines with fine-tuning, custom embeddings, or agentic workflows run $80,000-$200,000+. LLM API costs are separate and scale with usage — budget $200-$2,000/month for most SMB workloads. Our default LLM stack is Claude (Sonnet or Opus) with GPT fallback, prompt caching for 50-90% cost savings on repeat context. We build ONLY when the problem is a genuine AI fit — poor fits (exact-accuracy tasks, high-stakes decisions without human-in-the-loop) we'll tell you upfront.

Depends on the problem. Great fits: classification, summarisation, semantic search, drafting, document processing. Poor fits: exact-accuracy tasks, low-latency real-time systems, high-stakes decisions without human-in-the-loop. We advise honestly.

Depends on your use case, latency needs, budget, and data sensitivity. OpenAI for general use. Anthropic (Claude) for long-context and instruction-following. Open source for cost or on-premise. Often a mix. We help you decide.

Structured prompt engineering with versioning, A/B testing, and evaluation harnesses that quantify improvement. We don't "vibe check" prompts to production — every change is measured against a test set.

Every AI feature ships with cost monitoring, per-user quotas, cheap-model fallback for common cases, and caching where safe. AI costs get out of hand only when nobody's watching — we make sure someone is.

Yes. Tool-using agents with defined action spaces, guardrails, and human-in-the-loop approval where appropriate. Not autonomy for the sake of it — agents that solve a specific business problem better than a fixed workflow.