Six practices,
one engineering
team.
Each pairs deep technical work with an operator's eye for adoption. We can take one slice or the whole chain — strategy, build, wiring, and the field operations behind the data.
AI Software
Development
AI put to work — inside real products.
We build the integrations, agents and workflows that turn adoption into real productivity. Not chatbot demos — production systems your users keep using.
- >LLM-powered features — generation, extraction, classification
- >Autonomous and semi-autonomous agents with tool use
- >RAG systems grounded in your private knowledge
- >Computer vision pipelines for images, video and documents
- >Evals before features — we measure what "good" means
- >Thinnest useful slice first; weeks, not quarters
- >Model-agnostic architecture; swap providers without rewrites
- >Guardrails, cost controls and observability from day one
Web &
Mobile Apps
Production-grade products, built to scale.
Clean architecture, fast performance and interfaces people actually want to use. From first-release MVPs to mature platforms — same engineering discipline at every scale.
- >Web apps — React, Next.js, TypeScript end to end
- >Native-feel mobile apps for iOS and Android
- >APIs, backends and auth built to scale
- >Design systems that keep UI consistent as teams grow
- >One senior full-stack team — no handoffs between disciplines
- >Ship to production in week one; iterate in the open
- >Performance and accessibility as defaults
- >Tests, CI, monitoring — the boring stuff done right
AI Automation
& Integrations
We wire your systems together — so your team doesn't have to.
Automating workflows, connecting platforms and eliminating the manual overhead that slows a team down. The unglamorous pipes that turn a stack of tools into a system that runs itself.
- >Workflow automation across your existing SaaS stack
- >Custom API integrations — CRM, billing, support, internal tools
- >Data pipelines that keep every system in sync
- >Agents that handle tickets, triage and structured ops tasks
- >Map the real workflow before writing any code
- >Prefer boring, reliable tools over clever fragile ones
- >Observability baked in — you see exactly what ran and why
- >Clean handover with docs; no vendor lock-in
Consulting
& Strategy
Where AI fits. What to try first. How to actually adopt it.
Less "AI strategy deck", more a shortlist of the three things to ship this quarter and a plan for each. Engineers in the room, not just strategists.
- >AI adoption audits — where it'll work, where it won't
- >Architecture reviews for AI-heavy systems
- >Technology roadmaps tied to real business outcomes
- >Hands-on workshops with engineering and product teams
- >Opinions, not optionality — we tell you what we'd do
- >Short engagements, two to six weeks, concrete outputs
- >Happy to hand off to your team or stay to build
Crowd Sourcing
& Data Collection
From field capture to clean labels.
We design and run large-scale data collection programmes so you get the on-the-ground, labelled data your AI and GIS systems need. Real operations, not just software.
- >Field data collection programmes with distributed teams
- >Crowd-sourced submission apps with QA built in
- >Labelling and annotation ops for ML training sets
- >Verification and dispute resolution pipelines
- >Design the tool around how the work actually happens
- >Multi-stage QA — volume and quality both measured
- >Incentive design that gets clean data, not gamed data
- >Full audit trails from submission to dataset
GIS &
Location Data
We did the ground work — literally.
Deploying field teams to physically collect location data, map coverage and verify on-the-ground information at scale — then turning street-level capture into structured, ingestion-ready datasets.
- >Field data collection with mobile-first capture tools
- >Business listing verification and POI enrichment
- >Ground teams deployed for coverage at scale
- >Structured geospatial datasets, ready for ingestion
- >Custom collection apps matched to the brief
- >Geo-fenced QA — you see where every datapoint came from
- >Multi-region field ops, centrally coordinated
- >Clean, standardised output in your preferred schema
Not sure which practice you need? Send the problem — we'll tell you where it fits.