Seven capabilities, one production discipline
We build the full system — from inference architecture, permission-aware retrieval, domain adaptation, autonomous agents, data pipelines, and cryptographic governance to end-to-end custom web, commerce, and mobile applications.
AI System Architecture
Model selection, inference pipeline design, and production-grade deployment — built to your load profile.
Most enterprise AI projects fail not at the model, but at the system around it — latency budgets missed, cost overruns, no clear ownership between model and application. You need a system, not a demo.
RAG & Knowledge Systems
Retrieval-augmented generation pipelines that surface the right information at inference time, reliably.
Off-the-shelf RAG demos work on clean documents. Enterprise document sets are messy — mixed formats, competing revisions, permission boundaries — and generic pipelines hallucinate or leak.
Fine-tuning & Adaptation
Domain-specific model adaptation and evaluation frameworks grounded in your actual business metrics.
Generic models miss the vocabulary, tone, or reasoning your domain requires — and blindly fine-tuning without measurement burns budget without a signal on whether it actually helps.
Agentic Workflows
Multi-step AI agents with tool use, memory, and guardrails — designed for autonomous operation at scale.
Agent demos are impressive; agents in production are dangerous without guardrails. You need something that acts on your systems reliably and stops when it should.
Data Infrastructure
Pipelines, vector stores, and evaluation datasets that keep your models accurate as the business scales.
AI models are only as good as the data feeding them. Scattered sources, stale exports, and no evaluation dataset means model quality degrades silently and no one catches it.
AI Infrastructure & DevOps
The deployment, monitoring, and compliance layer that keeps your AI fast, available, and auditable under real load.
A model that works in a notebook and a model that survives production traffic and regulatory audits are different engineering problems. Without autoscaling, observability, and tamper-evident logging, an AI feature is one incident away from an outage.
Custom Web, Commerce & Mobile Platforms
End-to-end web platforms, commerce and funding portals, and cross-platform mobile & desktop apps — taken from blank-canvas idea to shipped software.
Ambitious organisations often have a clear operational or community vision — a member commerce network, a grant/loan portal, or a real-time mobile app — but struggle with fragmented agencies that hand off half-finished designs or brittle templates.
How we take AI systems from brief to production
Every enterprise or client build follows a bounded four-stage path — no open-ended retainers, and evaluation harnesses are built before we scale.
Discovery
We map your data, workflows, and constraints — and pinpoint where AI creates real ROI, not novelty. If the honest answer is that AI isn't the fix, we say so here.
Scope
A tight, costed plan: the proof we'll build first, the metric that defines success, and how it grows into production. No open-ended retainers.
Build
A working proof shipped fast, then hardened into a production-grade system — evaluation harness, monitoring, and rollback included, not bolted on later.
Handoff
Documentation, deployment, and knowledge transfer to your team — or an ongoing partnership if you'd rather we keep running it.
Questions teams ask before a first call
What stack do you work with?
We're stack-pragmatic. In practice that's TypeScript/Next.js and Python or Node services, PostgreSQL and vector stores (pgvector, Pinecone, Weaviate) for data, the major model providers (Anthropic, OpenAI, open-weight models) for the intelligence layer, and modern cloud infrastructure. We pick what fits your problem and your team, not what's trendy.
Who owns the IP?
You do. Work we build for you is yours — code, models, and data. We keep only our own pre-existing tools and generic building blocks. IP ownership is spelled out in the engagement agreement before any build starts.
How is our data handled?
Your data stays yours and is used only to deliver your project. We minimise what we touch, isolate client environments, and never train shared models on your private data. We'll align to your security and compliance requirements as part of scoping — our Security and Privacy pages describe the baseline.
What's the minimum engagement?
We start small on purpose. A Discovery plus a scoped proof-of-concept is the usual entry point — enough to prove value on a real problem before committing to a larger build. If a project isn't a fit, we tell you early.
How do we get started?
Two ways: generate a free AI Brief on this page to see how we'd approach your problem, or book a call directly. Either way, we come back with where we'd start and honest timeframes.
Answer four questions and our AI scopes it for you — grounded in what we actually build.