AI agents that ship.
Custom AI agents that actually understand your domain: trained on your data, wired into your systems, and built to do real work. Not chat-shaped novelties.
Talk to usEverything an agent needs to actually work.
A real agent isn't a model with a prompt. It's a stack: tools it can call, content it can trust, evaluations that catch regressions, and a team that maintains it. Here's what we deliver.
A production-ready agent
Not a prototype. A fully built agent deployed to your stack with monitoring, evaluations, and a clear hand-off. Ready to handle real traffic from day one.
Tools that match your workflows
Agents only work when they can actually do things. We connect them to your APIs, data sources, and SaaS tools (CRM, ticketing, billing, internal services) so they take action, not just answer questions.
A controllable knowledge layer
Retrieval-augmented generation tuned to your content, your tone, and your edge cases. We set up the ingestion pipeline so your agent stays current as your docs and data change.
Evaluation and guardrails
Automated evaluations so you know whether changes make the agent better or worse. Hard guardrails on the things that matter: escalation rules, refusal cases, audit trails.
A dashboard that tells the truth
Conversations, tool calls, costs, latency, success metrics. So you can see what your agent is doing in production and where it needs improvement.
A team that stays around
Agents aren't ship-it-and-forget. We support, iterate, and tune post-launch. Pricing that reflects an ongoing relationship, not a one-shot project.
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
Customer support agents
Cut first-response time to seconds and only escalate the conversations that actually need a human. Trained on your help docs, ticket history, and product behaviour so answers stay accurate and on-brand.
- 02
Internal knowledge agents
Your team's instant answer to 'where's the doc on…?'. Knows everything you've ever written down across Notion, Confluence, Drive, and Slack, and links back to the source so nobody has to take its word for it.
- 03
Slack, Discord & Telegram agents
Agents that live where your customers, community, or team already do. Same brain, surfaced inside the chat platform. No new tab to learn, no extra login to remember.
- 04
Voice agents
Real-time voice agents for inbound support, outbound calls, and discovery. Fluent enough that callers don't realise they're talking to one. Connected to your CRM, calendar, and ticketing.
- 05
Embedded product copilots
The AI feature that's already on your roadmap, designed and shipped inside your product instead of bolted onto the side. We handle prompt design, retrieval, evaluation, and the UX so it actually feels native.
- +
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 agents, in production.
We build our own AI agents alongside client engagements. Every lesson here gets folded 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 it take to build an AI agent?
A meaningful prototype runs in 1–2 weeks. A production-ready agent typically takes 4–8 weeks from kickoff, depending on how many tools it needs to call and how strict the evaluation bar is.
Which models and platforms do you use?
We're model-agnostic and work with Claude, GPT, Gemini, and open-source models depending on the job. Same for orchestration: Vercel AI SDK, custom stacks, the Anthropic Agent SDK, MCP servers, whatever fits. The point is the outcome, not the framework.
What's the difference between an agent and a chatbot?
A chatbot replies. An agent acts. Agents have tools they can call (your APIs, your databases, third-party services) so they can do real work. Book a meeting, refund an order, escalate to a human, update a record. We build the second kind.
Where can I deploy my agent?
Anywhere your users already are. We've shipped agents into web chat widgets, Slack, Discord, Telegram, WhatsApp, real-time voice calls, and embedded inside products as native features. The brain stays the same; we wire it up to whichever surfaces fit your workflow.
Do you help write the prompts?
Yes. Prompt design is core to what we do, not a one-off you hand over. We write the initial prompts, set up an evaluation harness so prompt changes can be measured against real conversations, and tune them as the agent meets real users. Most engagements include a regular prompt review post-launch.
What's context engineering and why does it matter?
Context engineering is the work of getting the right information in front of the model at the right moment. Your docs, your tool descriptions, the user's recent activity, the screen they're looking at. It's usually 80% of the difference between an agent that feels useful and one that feels generic. We design retrieval pipelines, tool schemas, and prompt structures so the relevant context is always there without overwhelming the model.
Got a job for a robot?
Tell us what you're trying to ship. We'll come back with a straight answer.
Start a conversation