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Website Development

AI & Emerging Tech

RAG, agents, voice, and other AI inside your product

Overview

Why this matters

AI is now table stakes inside many web products, and the bar for shipping it well is high. We design and build AI features inside websites, applications, and SaaS products. Our work covers retrieval-augmented generation over your content and data, conversational interfaces that understand domain context, voice and multimodal experiences, agentic workflows that act on behalf of users, and the evaluation harnesses that prove these features behave. We build with the providers and models that fit each use case, including OpenAI, Anthropic, Google, and open models hosted on your own infrastructure where data residency matters. Every AI feature we ship is paired with safety, evaluation, and observability work because shipping AI without guardrails is how companies get into trouble. We work with product teams adding AI to an existing product, founders building AI-native products, and operations teams automating internal workflows.

Why us

Key benefits

Use case first, model second

We start with the user problem and revenue or cost lever, then pick the model, provider, and architecture to match.

Retrieval done properly

Chunking, embeddings, hybrid search, and evaluation built so retrieval-based answers stay grounded and current.

Evaluation and safety as work, not slides

We build evaluation harnesses, red-team prompts, and monitoring so quality and safety are measured, not assumed.

Cost and latency under control

We design for predictable token cost, latency budgets, and caching so AI features stay viable as usage scales.

How we work

Our approach

01

Use case & data

We define the user problem, the data sources involved, and what good behavior looks like before any model selection.

02

Prototype & evaluate

We build the smallest working prototype, then evaluate quality, cost, and latency against agreed thresholds.

03

Productionize

We build the production feature with safety, observability, and the user experience and integrations it needs.

04

Operate & improve

We monitor behavior in the wild, retrain or tune as data shifts, and feed real usage back into evaluation.

FAQ

Frequently asked questions

Should we use OpenAI, Anthropic, or an open model?
All three have good options. We pick based on use case, latency targets, cost budget, data residency, and any compliance requirements. For many regulated use cases we recommend open models hosted on your own infrastructure or in a VPC.
How do you stop AI features from making things up?
We use retrieval over trusted sources, structured prompts, and explicit refusal patterns, then we measure with an evaluation set rather than relying on intuition. No system is perfect, but we make ungrounded answers rare and easy to flag.
Can you build agentic workflows that take actions?
Yes. We build agentic features that can call APIs, write to systems of record, and orchestrate multi-step work. We are careful about scope, authorization, audit logging, and human-in-the-loop checkpoints for any action with meaningful business consequence.
How do you handle data privacy?
We design for least-privilege access to data, segregation between tenants, and clear handling of PII. For sensitive data we recommend self-hosted or VPC-deployed models, plus enterprise agreements with providers that ensure data is not used for training.

Want help with AI & Emerging Tech?

We will scope the right path for your goals.