AI Development

Depends on your priorities. OpenAI and Anthropic have the strongest reasoning capabilities — best for complex tasks, comes with API costs and data-residency considerations. Self-hosted (Llama, Mistral) gives you data control and predictable costs — slightly weaker reasoning, requires infrastructure. Fine-tuning works when you have lots of high-quality task-specific data — most teams should start with prompt engineering + RAG before considering fine-tuning. We help you pick based on your actual constraints.

Prompt engineering: writing better prompts — cheapest, fastest, sufficient for ~70% of use cases. RAG: retrieving relevant context from your data and feeding it to the model — solves "the model doesn't know our private info" problem. Fine-tuning: training the model on your data — expensive, requires high-quality datasets, useful when prompt + RAG aren't enough. Most production systems use a combination. We start with the simplest approach that meets your accuracy requirements.

Three dimensions. (1) Accuracy: automated evaluation against test sets, human review on samples. (2) Cost: per-request costs, total monthly inference budget. (3) Latency: response time at p50, p95, p99 under realistic load. We instrument all three from day one and surface them in dashboards so you're not guessing whether the system is degrading.

Both. Most AI engagements are integrations into existing products — adding chat, search, summarization, or recommendation features to apps that already have users. We work with whatever stack you're on. Greenfield AI products are also welcome, but most of the high-ROI AI work today is enhancing what already exists.

Two layers. Our engineering fee covers the integration work (one-time + ongoing). LLM API costs (OpenAI, Anthropic, etc.) are pass-through — you pay the API directly or we bill you at cost. Typical small-volume features run $50–500/month in API costs. High-volume features can hit thousands. We optimize prompts, caching, and model selection to minimize API spend without sacrificing quality.

Yes. Includes input filtering (block prompt injection attempts, malicious queries), output filtering (block harmful or off-policy content), rate limiting, and audit logging. For customer-facing AI features, this is non-negotiable — most production failures we see at other shops come from skipping AI safety as "we'll add it later."

Traditional automation follows hardcoded if-this-then-that logic. AI agents make decisions dynamically — they can reason about novel inputs, use tools, ask clarifying questions, and handle edge cases the original developer didn't anticipate. Agents are more flexible but harder to predict and require more evaluation infrastructure. Use traditional automation for well-defined repeatable tasks. Use agents when inputs are messy or workflows need judgment.

Simple LLM integration (chat, summarization in an existing app): 2–4 weeks. Custom agent with tool use: 4–8 weeks. ML model training + production deployment: 6–12 weeks. AI-native product: 3–6 months. We give a fixed-scope proposal within 7 days of discovery so you know what you're committing to.