The work that fills your team's day can run on its own. We build the AI systems that make that happen — wired directly into your infrastructure, not stitched on top of it. Built in, not bolted on.
Every engagement is different. But the through-line is always the same: AI that works in your systems, not just in demos.
Connect large language models to your stack through a proper gateway — model routing, fallbacks, rate limits, and cost controls built in. Versioned prompts and full observability, not raw API calls scattered through your code.
Systems that don’t just answer — they act. Single agents or coordinated multi-agent setups that plan, call tools, and complete real tasks in your environment, with guardrails and a full audit trail.
When off-the-shelf precision isn’t enough, we fine-tune on your data, your tone, your domain — then measure it. Evaluation and inference optimisation so the model stays accurate and affordable at scale.
Voice agents that handle real calls — inbound support, outbound outreach, booking, and surveys — wired into your telephony and CRM. They qualify, resolve, and log every call without a human in the loop.
Retrieval-augmented generation over your actual documents and data — answers that are accurate, cited, and hallucination-resistant. Semantic search underneath means the right result surfaces even when the wording doesn’t match.
An AI layer across your top of funnel — qualifying and enriching leads, booking meetings, updating the CRM, and drafting proposals. Your sales team spends its time on real opportunities, not admin or bad-fit leads.
Front-line support that resolves on its own across email and chat — answering from your knowledge base, handling the routine, and escalating cleanly to a human when it should. Faster replies, a shorter queue.
Turn unstructured input — PDFs, contracts, images, audio, emails, forms — into structured, queryable data. Extract, classify, and route it automatically, so information stops getting stuck in documents.
The production layer underneath the AI — GPU and self-hosted inference, model deployment, monitoring, and evaluation. On your cloud or ours: AWS Bedrock & SageMaker, GCP, or Azure, built to run reliably and stay observable.
We wire AI into the tools you already run — CRM, helpdesk, ERP, custom stacks — as pipelines that read, classify, route, and act. Not a standalone tool bolted on the side, but a genuine part of how your team works every day.
AI that works in your systems, not just in demos. Pick a query and watch it move through — retrieval, reasoning, and a cited answer in plain English.
Every AI system we build goes through the same rigorous process — because a pipeline that fails in production is worse than no pipeline at all.
We map what you’re solving, where your data lives, and which models fit. The tech decision comes after the problem is fully understood — not before.
We build production-grade code your existing tools can call — not a demo app. Proper error handling, auth, rate limits, and logging from the first commit.
We run it against your actual data and edge cases before any user touches it. Accuracy, latency, cost, hallucination rate — all measured and documented.
Deployment with observability built in. We watch it run, tune as it learns, and handle model updates so you don’t have to think about model versioning.
Production pipelines, not demos.
Short-term rental operator across Airbnb and Booking.com. Every new booking now triggers a personalised guest message — automatically, on every platform, without lifting a finger.
A Canadian media agency fielding a high volume of inbound interest. A voice AI now handles the entire inbound flow — qualifying, booking, and logging to the CRM without a human in the loop.
A creative and brand strategy agency running Google Ads at scale. An AI qualification layer filters every inbound lead against ICP criteria — only matched leads reach the sales team.
Every AI engagement produces documented, testable, maintainable work — not a prototype we hand off and walk away from.
You have CRM records, documents, support tickets, internal wikis — and people spending hours reading through them. AI can do that work faster and more consistently, with a full audit trail.
You want AI embedded in the product — not bolted on with a third-party widget. We build the inference layer properly from the start, so it scales with the product.
You need a technical partner who can deliver the back-end. We work NDA-available, white-label, and your clients never see us. The work ships under your name.
My guests on Airbnb and Booking.com get a response before I’ve even seen the notification. Every booking, every platform, without me lifting a finger.
LLM pipelines, retrieval-augmented generation (RAG), inference endpoint setup, knowledge bases, custom AI tooling, and workflow automation. We build practical integrations into existing products and processes — not demos or proofs of concept that never reach production.
Not necessarily. If you know you want to use AI but aren't sure how, we can help you identify what's actually useful versus what's noise. If you have a specific use case already, we'll tell you whether it's worth building and how we'd approach it.
Primarily existing models — OpenAI, Anthropic, Mistral, Llama, and others — via API or self-hosted inference. We build the pipelines, retrieval systems, and interfaces around them. Custom training is rarely necessary and rarely worth the cost unless you have a genuinely unique dataset and a clear reason to own the model.
In most cases, yes. We build APIs, connectors, and middleware that slot AI capabilities into your existing product or workflow. A full rebuild is only recommended when the existing system is the blocker — and we'll be direct with you about when that's the case.
Not necessarily. We build with maintainability in mind, document everything, and offer ongoing management retainers for teams that don't have AI engineering capacity. If you do have a technical team, we'll structure the handover so they can take it from there without a steep learning curve.
We scope data handling carefully at the start of every engagement. Where sensitive data is involved, we'll recommend self-hosted or private inference options and structure pipelines to avoid unnecessary exposure to third-party APIs. Data privacy is part of the architecture conversation from day one — not an afterthought.
It depends on the scope of the integration — the complexity of the pipeline, the models and infrastructure involved, and how much existing systems need to be adapted. A straightforward LLM integration into an existing product is a different brief from a multi-stage RAG pipeline with custom tooling. We scope every AI engagement individually and provide a fixed fee before work begins. There is no minimum project size — we scope every engagement on its own merits regardless of scale.
From architecture to deployment, we build and manage the full pipeline — not a prototype that works in demos but falls apart in the wild.
NDA-AVAILABLE · NO SALES TEAM · YOU SPEAK TO THE PROJECT LEAD
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