Engagement studies.

A SaaS team needed an AI surface that felt as fluent as a senior career coach — without exposing user data to third-party model providers and without breaking under conversational load.

We designed a retrieval-first architecture with private model routing, conversational memory partitioned per user, and a streaming UI tuned for first-token latency. Channel adapters extended the experience into messaging surfaces the user already lived in.

A healthcare provider needed a unified portal across legacy clinical systems, with telehealth and an AI triage surface — under the constraints of regulated data and audit-grade trails.

We integrated against existing record systems through narrowly-scoped adapters, designed an AI triage flow with explicit guardrails and clinician-in-the-loop review, and built the application around an audit-first data model. Mobile and web shared a single design system.

A fintech needed risk decisions in the request path — fast enough to keep checkout alive, smart enough to catch coordinated abuse, explainable enough to defend post-incident.

We built a streaming feature pipeline with model serving on the request path, a slower offline pipeline for retraining, and a decision explanation layer surfaced to operators. Failure modes were designed first — every model decision had a deterministic fallback.

A retail group wanted a multi-vendor marketplace where the AI surface — search, recommendations, content — felt native instead of grafted on, and stayed fast as catalog complexity grew.

Headless commerce core, semantic search with a reranker tuned to the catalog, and an editorial layer where merchandisers could shape AI behaviour without code. Performance budgets were enforced at the edge.

A manufacturer needed early-warning signals for failures across heterogeneous assets, with a data layer messy enough that off-the-shelf tooling kept giving up halfway.

We modelled the data first — a unified asset graph fed by adapters per source — then placed predictive models on top. The interface deliberately avoided black-box scores, surfacing the contributing signals operators trusted.

A platform team needed multi-tenant isolation, configurable RBAC, and AI features that respected tenant boundaries — including model context, prompts, and retrieved data.

Tenant boundaries pushed all the way through the AI stack: per-tenant retrieval indexes, prompt and tool scoping, and an admin surface that made the boundaries visible. Production rollouts gated on per-tenant evals.