Build AI into your product

Frequently askedquestions

• What kinds of AI features do you build?

  We build production-grade AI features inside existing SaaS products: conversational chatbots and intelligent agents, retrieval-augmented generation (RAG) over private knowledge bases, LLM-powered search and summarization, content generation pipelines, and AI-enhanced UX patterns like contextual help, autofill, recommendations and predictive actions. We also handle workflow automation that wraps an LLM around an existing business process — extracting structured data from documents, drafting replies, classifying tickets — and we will tell you when a deterministic approach would beat the AI one.
• Do you build with OpenAI, Anthropic, or open-source models?

  All three. We choose per use case rather than per vendor. OpenAI and Anthropic models are usually the right answer for production features that need strong reasoning, broad capability and managed infrastructure. Open-source models (Llama, Mistral, Qwen and similar) are the right answer when data residency, cost-at-scale, latency, or fine-tuning on proprietary data outweighs raw capability. We design the integration layer so swapping providers later is a configuration change, not a rewrite.
• How do you handle data privacy when integrating LLMs into our product?

  We treat user data as default-private. That means: zero-retention API options when the vendor supports them (OpenAI and Anthropic both do), explicit per-feature data classification, redaction of PII before it leaves your infrastructure when feasible, and self-hosted or EU-region models when residency requirements demand it. We also help draft the user-facing AI privacy notice and DPA addenda your customers will ask for, and we will not ship a feature that would surprise your end users about what data goes where.
• What does a typical AI feature engagement look like?

  Most AI engagements start with a 1–2 week discovery: we map the use case, prototype against your real data, and produce a written estimate covering scope, milestones, and the risks we have identified. Build phases then run 6–12 weeks for a single feature, with weekly demos and an in-product staging deployment by week three. Engagements usually pair a senior full-stack engineer with an AI-focused engineer; design joins for any user-facing surface. We do not bill for the discovery if you choose not to proceed.
• Can you take over an AI prototype my team built and harden it for production?

  Yes — this is one of the most common engagements we run. A working prototype built with Cursor, Claude Code or a hackathon weekend usually has the right shape but the wrong foundations: leaky prompts, no evaluation loop, missing guardrails, no observability, naive cost controls. We refactor those out, add an evaluation harness so regressions are visible, instrument it for production traffic patterns, and lock down the prompt and tool-use surfaces so the feature behaves predictably at scale. See our vibe code rescue service for the broader pattern.
• How do you evaluate whether AI is the right solution vs. a deterministic approach?

  We ask three questions before recommending AI: does the task tolerate non-determinism, is the input space too varied for rules, and is the cost per call defensible at the volume you expect? If the answer to any of those is no, we will propose a deterministic alternative — a state machine, a search index, a templated workflow — even when AI would be more interesting to build. We have killed our own AI proposals in scoping calls when a 50-line rule-based solution would have shipped the same outcome.