Generative AI development services
Generative AI development services
Practical generative-AI features — chat, search, content and automation — built into real products, safely.
We build AI that earns its place in your product — measured on real outcomes, not demos.
Generative AI is only useful when it solves a real problem in your product. We build practical AI features — assistants, semantic search, content generation and workflow automation — and wire them into your app with the guardrails, evaluation and cost controls that keep them reliable in production.
What we build
- AI assistants & chatbots — grounded in your own data, not generic answers.
- RAG & semantic search — retrieval over your documents and knowledge base.
- Content generation — drafting, summarizing and transforming at scale.
- Workflow automation — agents and pipelines that handle repetitive work.
Built on your data, with guardrails
The difference between a demo and a dependable feature is the engineering around the model. We ground responses in your data with retrieval (RAG), add evaluation so you can measure quality, and put guardrails, rate limits and cost controls in place. We work with leading models — including Anthropic's Claude and OpenAI's — and choose per use case.
Safe, measurable, in production
We treat AI features like any other production system: monitored, evaluated against real examples, and designed with privacy and data handling in mind. You ship something you can trust in front of users — and a clear view of what it costs and how well it's working.
Who it's for
Teams adding AI to an existing product, and founders building something AI-native. We work as an extension of your team — small, senior and accountable — and we're candid about where AI helps and where it doesn't.
We've shipped AI-driven products including a faith-based AI chat app and vBites.ai, an AI nutrition product.
What's included
Capabilities- + AI assistants & chatbots
- + RAG & semantic search
- + LLM integration (Claude, OpenAI)
- + AI content generation
- + Agents & workflow automation
- + Evaluation, guardrails & safety
- + Data pipelines & fine-tuning
- + AI features in existing apps
How we work
Process- 01
Find the use case
We start from a real problem and a measurable outcome — not 'add AI' — and check whether generative AI is actually the right tool.
- 02
Prototype & evaluate
A working prototype on your data, with an evaluation set so we can measure quality before committing to a full build.
- 03
Build with guardrails
We engineer the feature for production: grounding, guardrails, rate limits, monitoring and cost controls.
- 04
Ship & improve
We launch behind sensible limits, watch real usage and quality, and keep tuning prompts, retrieval and models.
Frequently asked
What can generative AI actually do for my product?
Practical things: an assistant grounded in your data, semantic search, drafting and summarizing content, and automating repetitive workflows. We start from a real outcome, not the technology.
How long does an AI feature take to build?
A useful prototype is often 2–4 weeks; a production-ready feature with evaluation and guardrails typically a few weeks more, depending on scope.
Which models do you use?
We work with leading models including Anthropic's Claude and OpenAI's, and pick per use case based on quality, latency and cost — you're not locked to one.
How do you keep AI features accurate and safe?
We ground responses in your data (RAG), evaluate against real examples, and add guardrails, rate limits and monitoring — and design with privacy and data handling in mind.
Will it work with our existing systems and data?
Yes. Most of our AI work is adding features into existing products, integrating with your data sources, APIs and auth.
