AI features your users would actually miss.

Yes — we work inside your current codebase and ship behind feature flags, so new AI functionality reaches a small slice of users first. Your core product and its uptime are never put at risk for the sake of a new feature.

Almost never. Most AI features — search, recommendations, summarization, chat — bolt onto a modern stack as a new service or API call. We'll flag the rare case where your architecture genuinely blocks integration before any work starts.

We're provider-agnostic — OpenAI, Anthropic, Google Gemini, and open-source models via providers like Together or self-hosted inference, depending on cost, latency, and data requirements. We recommend a stack rather than default to one.

During the audit phase we benchmark candidate models against your actual data on accuracy, latency, and cost per request, then recommend the option that fits your product's margins — not just the most capable model available.

It depends on the feature — some integrations need no historical data at all, others benefit from your existing product usage or content data. We audit what you have first and only ask you to collect more if a feature genuinely requires it.

Common, and rarely a blocker. Data pipeline work — cleaning, structuring, and connecting your data to the model — is part of the engagement, scoped during the audit so there are no surprises mid-build.

Engagements scope to the specific feature after the audit — a single well-defined feature integration is a very different project from a multi-feature AI layer. You get a fixed-scope quote before any build work starts, plus a separate estimate of ongoing model API costs.

A single, well-scoped feature typically ships in 3–6 weeks from audit to staged rollout. Multi-feature integrations or ones needing meaningful data pipeline work run longer — you'll get a real timeline once we've scoped it.

We define success metrics before we build — adoption rate, task completion, output quality score, or a downstream business metric like conversion or retention — and track them through the staged rollout, not just at launch.

We build to your existing code standards and hand over documented, versioned prompts and integration code — not a black box. Most clients' teams take over day-to-day maintenance within weeks of launch.

Your call. Some clients hand off fully after launch; others keep us on for ongoing tuning, cost monitoring, and model upgrades under a lighter monthly retainer. We scope whichever fits before rollout.

We use providers' enterprise/API tiers with no-training-on-your-data agreements where available, minimize what's sent to third-party models, and can route sensitive workloads to self-hosted or private-deployment models when your compliance needs require it.

Every integration ships with guardrails — confidence thresholds, fallback flows, and human-in-the-loop review where the stakes are high — plus an eval process to catch quality regressions before users do.

You get a full team — engineering, UX, and data — that has shipped AI features across multiple companies, without the hiring timeline or the risk of a single freelancer's bandwidth. We integrate with your existing team rather than replacing it.

Book a scoping call — we'll review your product and data, identify the highest-impact AI feature to start with, and send a fixed-scope proposal within 24 hours.