AI integrations that just fit.
We wire AI into the products and business systems you already run. Voice, vision, retrieval, copilots, real-world devices, and the intelligent glue between your tools. No rewrites, no greenfield detours.
Talk to usEverything an integration needs to actually land.
Bolting a model onto an existing system is the easy 20%. The rest is choosing the right one, evaluating it against your real data, rolling it out without breaking trust, and keeping it healthy as the model landscape shifts. Here's what we deliver.
AI inside what you already run
Not a sidecar service or a standalone tool. The integration lives inside your existing product or system, sharing the same auth, database, design language, and ops. The new behaviour shows up where users or your team already work.
Model and API selection, justified
We pick the model and provider that fit the job (Claude, GPT, Gemini, open-source, hosted or self-served) and write down why. So when costs shift or a better option lands, you can swap with confidence instead of starting over.
Evals tuned to your domain
An evaluation harness built on your real data and your real edge cases, not a generic benchmark. So you can measure whether a prompt change, model swap, or retrieval tweak actually makes things better for your users.
Observability and cost tracking
Per-feature dashboards for latency, token spend, error rates, and quality signals. Wired into the tools you already use so the AI feature doesn't become the one part of the product nobody can see.
Gradual rollout with a kill switch
Feature flags, percentage rollouts, allow-lists, and a one-click off switch. The integration ships behind controls so you can expand confidently and pull it back instantly if something goes sideways.
Ongoing tuning post-launch
Models change, prompts drift, your data evolves. We stay involved after launch, reviewing prompts, re-running evals, and swapping models when a better fit appears, so the integration stays sharp instead of slowly rotting.
How we work
6 week product cycles that always launch.
Build your vision with our 6-week product cycles. A small senior team, AI-amplified end-to-end, geared up to launch your idea in six weeks.
Why 6 Weeks? It's the Goldilocks Zone - Striking the perfect balance between allowing enough time to build something meaningful, while being short enough to keep risks low!
Whether its an MVP, prototype, or feature in a existing product, our 6 week cycles make sure you have something tangible at the end of the project.
Sounds cool! Tell me more01: Discovery
Refine your ideas and plan what will be launched in 6 weeks.
02: Kick-off
We get cracking. Design, code, and AI work happen in parallel from day one.
03: Check-in
On week 3 get ready for an exciting demo of progress.
04: Build & Iterate
Continue work and integrate feedback from the check-in.
05: Pre-launch
A check-in before launch to tie up loose ends and get ready.
06: Launch
The big day is here, you idea is launched to the whole world.
What we usually build.
- 01
Copilots inside your existing product
AI assistants that live inside the product your users already log into. Context-aware of the screen they're on, the data they can see, and the actions they're allowed to take. Native, not bolted on.
- 02
Image intelligence and classification
Pull useful information out of the images, scans, photos, and screenshots your business already collects. Classify, tag, extract text, detect anomalies, route to the right place. Built into your existing storage and workflow.
- 03
Voice and text-to-speech
Add natural voice generation, real-time transcription, or automated phone calls to your stack. Connected to your CRM, calendar, or ticketing so the audio side talks to the rest of your systems.
- 04
Cameras and real-world device integration
Connect AI to the physical world. Object detection on a camera feed, monitoring on a production line, smart capture from a scanner, building access driven by a video stream. The model becomes part of how the physical thing works.
- 05
Stitching business systems together
Add intelligence to the data flowing between your tools. Bridge formats, infer missing fields, deduplicate records, route messages where rules-based logic couldn't. The work that used to need a human to read first now reads itself.
- +
Got something different?
Tell us about your use case — we'll come back with a straight answer about whether it's something we can help build.
Real integrations, in production.
We integrate AI into our own products as well as client codebases, and every lesson from our own stack feeds back into the work we do for you.
Chat Thing
Chat Thing is our first in house product that allows users to create magic AI chatbots
Building HeyYou a production AI security platform in 8 weeks
HeyYou had a working AI prototype. Pixelhop built the launchable product: a multi-tenant platform with deep Axis camera integration, all live in 8 weeks.
Orbit: AI grant scraping for Grants Online
An AI grant scraping pipeline that turned a year of manual review into minutes per grant.
Things people ask.
How long does an AI integration take?
It depends entirely on what you're integrating and how deep it goes into your existing systems. We start with a scoping conversation to map the feature, the data it touches, and the integration points. From there we come back with an estimate based on the actual complexity, not a generic timeline.
Which stacks and frameworks do you work with?
We pick the stack that fits the specific use case, not the framework we like best this quarter. The starting point is what you already use and what your team will maintain after we hand off. From there we lean on the tooling, libraries, and patterns we've battle-tested on our own products and previous client projects, so you're not the first project to ship a given approach.
How do you handle data privacy and model providers?
We scope exactly what data leaves your systems and which provider sees it. Options range from zero-retention API agreements with providers like Anthropic and OpenAI, through to self-hosted open models when data can't leave your environment. We document the data flow before any integration ships.
How do you evaluate whether the integration is actually good?
Where it makes sense, we build an evaluation harness on your real data: input examples, expected behaviour, and edge cases. Changes to prompts, models, or retrieval get measured against the work rather than vibes. Some integrations are simple enough to test by hand and don't need the overhead, so we work out the right approach with you up front.
What about maintenance once the model landscape shifts?
Models change every few months. We offer ongoing engagements that cover prompt tuning, eval refreshes, model swaps when a better fit appears, and cost optimisation as pricing moves. The integration stays current instead of becoming the part of the codebase nobody wants to touch.
Got something that could use a brain transplant?
Tell us what you're trying to ship. We'll come back with a straight answer.
Start a conversation