03 · Technology
AI features that survive contact with production.
Wiring a model to a prompt and getting an impressive demo is easy. Making that feature reliable, affordable and safe with real users, data and edge cases is engineering, and that gap is where AI projects tend to stall. We build AI features into real products: retrieval-augmented assistants, semantic search, agentic workflows and in-product LLM features, with evals, guardrails and cost controls designed in from the start. If a feature should not use a model, we will tell you that too.
What you walk away with
- A straight answer on whether a model belongs in the feature at all.
- Retrieval grounded in your own documents and data, so answers are accurate and attributable.
- Evals that show whether a change made the feature better or worse.
- Guardrails against the failure modes that would embarrass you in front of users.
- Cost controls, from model routing and caching to hard budget limits, so a popular feature doesn't become a runaway bill.
What it covers
- Retrieval-augmented assistants (RAG)
- Assistants grounded in your own documents and data, so answers are accurate and attributable instead of confidently wrong.
- Semantic and hybrid search
- Search that understands meaning as well as keywords, wired into your product with the relevance tuning that makes it useful.
- Agentic workflows
- Multi-step automations where a model plans and acts within hard guardrails, for workflows that benefit from autonomy.
- LLM features in the product
- Summarization, drafting, classification and extraction built into the screens your users already use, engineered like any other web-app feature.
- Evals and observability
- Test sets, scoring and monitoring, so you can prove a change helped and catch regressions before users do.
- Guardrails and cost control
- Input and output safety, rate and budget limits, caching and model routing, so the feature stays safe and the bill stays sane.
Is it the right fit?
Right for you if
- A real task where retrieval, classification, generation or an agent measurably improves the outcome.
- Existing software or web applications that need AI features wired to their own data.
- Teams that want evals, guardrails and cost limits treated as part of the feature.
Probably not if
- AI added because the board asked for AI rather than because it helps anyone.
- Problems that are better solved with conventional software, better search or a clearer interface.
- A chatbot on the homepage for its own sake.
How the work runs
Every engagement moves through the same five stages, with one team throughout.
Understand
A first conversation and a look at what already exists: your position, your customers’ path, your traffic and your technology.
Define
Positioning, scope and architecture decided deliberately and written down, so everyone knows what is being built and why.
Design
Brand, content and interface designed together, against those decisions rather than the fashion of the month.
Build
Built with speed, accessibility and security as requirements, and reviewed on a staging site before anything goes live.
Improve
Launch is the start. We measure what matters, fix what drifts and report in plain language.
Further reading
- AI in Web Development: A Reality CheckWhat AI genuinely speeds up in web development, what it can't do, and the questions that separate an honest 'AI-powered' pitch from a label.
- How to Rank in Google's AI Overviews (2026)Google says its AI features rest on its core ranking systems. What gets content cited in AI Overviews and AI Mode, and what you can safely ignore.
- Will AI Agents Replace Digital Agencies?AI agents are getting good at execution, but execution was never the hard part. What they replace, what they can't, and who still needs an agency.
AI integration: common questions
Do we need our own model, or do you use existing ones?
Almost always, the right answer is to build on existing frontier or open models rather than train your own; the cost and complexity of a custom model is rarely justified. We focus on the engineering around the model: retrieval, evals, guardrails and integration. That is where the real value and difficulty live.
How do you keep AI costs under control?
Through model routing (using the smallest capable model per task), caching, prompt and context discipline, and hard budget limits, all designed into the feature from the start.
What if AI isn't actually the right solution?
Then we will tell you, before you spend on it. Many problems pitched as AI problems are better solved with conventional software, better search or a clearer interface. We would rather lose an AI line item than build you a feature that does not earn its keep.
Can you add AI to our existing product?
Yes. We integrate AI features into existing software and web applications, wiring them to your real data and your real interface, with the evals and guardrails that make them safe to ship.
How do we start?
With the task, not the technology. Tell us what you're building, what the feature should do for your users and what data it would draw on, and we will tell you plainly whether a model belongs in it.