AI features & agents: AI features that survive real users.

Getting a model to impress in a demo takes an afternoon. Making it do the right thing for thousands of users, with their data and a bill you can predict, takes product engineering.

AI features get the same treatment as everything else we build. We start from the user’s job and design around what the model does well. Evals, guardrails, and fallbacks keep it trustworthy in production.

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Teams we’ve built for

  • Fasset
  • Zantrik
  • Ride On
  • Cookups
  • Curo
  • Access Doctor
  • Dianahost
  • Indosat
  • opsable.io
  • Marketever
  • Vendidit
  • Fmail
  • FreshWay
  • Rashifol
  • Khwab
Sound familiar?

The demo was magic. Production is another story.

  • Your AI feature works beautifully (about 80% of the time).
  • Nobody can say whether the last prompt change made things better or worse.
  • The model bill is growing faster than revenue.
  • Users don’t trust the answers, so they don’t use the feature.
  • Your knowledge is scattered across docs, databases, and tickets. The model can’t see any of it.
Capabilities

What we can help with

Pick what you need. Most projects use several of these, all delivered by the same team.

How it works

From first call to handover.

  1. Step 01

    Find the job

    Before anyone touches a model, we pin down the user problem and how we’ll measure success.

  2. Step 02

    Prove it

    A quick proof of concept on real data, with an honest read on quality, cost, and latency.

  3. Step 03

    Build it properly

    The production feature and the UX around it, with the evals and fallbacks that keep it honest.

  4. Step 04

    Measure & tune

    Tracing and eval runs your team can keep using to improve quality and cost after launch.

What you walk away with
  • A written feasibility read-out: what’s viable, what isn’t, and why
  • A production AI feature integrated into your product
  • An evaluation suite you can re-run on every prompt or model change
  • Cost, latency, and quality monitoring
  • Documentation of prompts, tools, and data flows

Tools we reach for

  • OpenAI
  • Anthropic
  • AI SDK
  • PostgreSQL + pgvector
  • Next.js
  • Cloudflare Workers
Who it’s for
  • SaaS teams with an existing product and a clear AI use case
  • Founders building a product around AI from scratch
  • Teams whose AI prototype stalled on quality, cost, or trust
Talk to us
Related work

Where we’ve done this before.

All work
FAQ

Good questions, straight answers.

Something we missed? Ask us directly. A person will answer, not a bot.

  1. 01Which models do you work with?

    Mostly OpenAI and Anthropic, plus open-weight models where privacy or cost calls for them. We design so you can switch providers without rewriting the product.

  2. 02Is our data used to train models?

    We use API tiers that don’t train on your data, and your keys and data stay in your own accounts. When data can’t leave your infrastructure, we can run open-weight models inside it.

  3. 03How do you know whether the AI is any good?

    Evals: a set of real examples and expected behaviours we run on every change. It turns “feels better” into a number your team can track.

  4. 04What about hallucinations?

    We reduce them by grounding answers in your data, constraining outputs, and validating what comes back. The interface also shows sources and lets people correct mistakes.

  5. 05Can you add AI to our existing product?

    Yes, that’s most of our AI work. We integrate with your stack as it is. You won’t be asked to rebuild around a new framework.

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