AI-native web apps
React and TypeScript front ends with streaming responses, optimistic UI, citations and graceful degradation when a model is down.
All services
Build & Engineering
AI App Development
AI-native products your users trust on day one.
- 6–10 wks
- Demo to production
- <800ms
- Typical first-token latency
- 99.9%
- Availability target
The problem we solve
A demo is a weekend. A product is authentication, permissions, latency budgets, evaluation, cost control, accessibility and support. We build AI applications the same way we would build a payments system — because that is the bar enterprise users hold them to.
What the work includes
Mobile & cross-platform
iOS and Android delivery with offline-first sync, on-device inference where privacy demands it, and push-driven agent updates.
Copilots inside existing products
Embedded assistants scoped to real permissions, wired to your domain APIs rather than a generic chat box.
Enterprise integration
SSO/SAML/OIDC, SCIM provisioning, audit logging, tenancy isolation and role-aware retrieval.
Evaluation & guardrails
Golden datasets, regression suites in CI, refusal handling, PII redaction and prompt-injection defence at the boundary.
Cost & performance engineering
Model routing, caching, batching and token telemetry so unit economics improve as usage grows.
How the engagement runs
A sequence you can plan a quarter around.
- 01
Shape
Product definition, user journeys, model and architecture decisions, latency and cost budgets.
- 02
Build
Two-week increments with a working deploy at the end of each, evaluations gating every merge.
- 03
Harden
Load testing, red teaming, accessibility audit, security review and rollout plan.
- 04
Operate
Managed support under SLA or a structured handover to your engineering team.
Common questions
Before you commit.
- Who owns the code?
- You do. It ships into your repositories and cloud accounts with full documentation.
- Can you work with our in-house engineers?
- Yes — blended teams are our default, and the handover starts on day one, not at the end.
- Which models do you use?
- Whichever passes evaluation for your workload. We build for model portability so a swap is a config change, not a rewrite.
Related practices
AI Agents & Process Automation
Agentic workflows wired into ERP, CRM, ticketing and document systems, with human checkpoints where risk demands them.
ViewGenerative AI & RAG Engineering
Retrieval architecture, fine-tuning, evaluation harnesses and inference cost tuning built for production traffic, not demos.
ViewComputer Vision & Multimodal
Inspection, safety monitoring, document vision and multimodal assistants deployed at the edge or in the cloud.
ViewTalk through ai app development.
A 45-minute briefing with the people who would run the work — scope, timeline and a straight answer on whether it is the right next step.