An AI subscription is not an AI strategy
A chat assistant on every desk lifts individual productivity, but it doesn't move your cost base or your cycle times.
The question is structural: which work is still manual, where do decisions stall, and what should AI take over entirely?
What AI-native actually takes
Automate the work
We rebuild your costliest workflows around AI. Agents read documents, pull data, decide within limits, update systems, and escalate when a person is needed. The result is measured in cycle time, cost per transaction, and hours returned.
Enable your teams
We train by department and role, using your real work, so finance, sales, support and engineering each learn where AI fits theirs. Capability spreads across the company instead of sitting with a few enthusiasts.
Scale it safely
We put in place permissions, evaluations, monitoring, human review, data controls and governance. Some workflows can act alone, others should recommend, request approval, or escalate, and we draw that line for each one.
Every department, one workflow at a time
We start where the work is repetitive, measurable, and expensive, then extend the same foundations across the organisation.
Finance
Invoices, reconciliation, approvals, collections, reporting. Your team works exceptions instead of rekeying data.
Operations
Information moves between tools, processes stay monitored, exceptions surface before anyone has to ask.
Sales
Account research, CRM updates, meeting prep, follow-ups. Reps get their calendar back.
Customer support
Requests understood, context retrieved, replies drafted, approved actions taken, hard cases routed. Resolution time drops, not just first response.
HR and internal teams
Policy questions answered, requests processed, documents prepared, onboarding coordinated.
Engineering
Codebase exploration, features, tests, debugging, refactoring, review. Repeatable agent workflows, not occasional assistance.
Train every team to work with agents
Agents are not only for developers. The same tools that navigate a codebase can clean data, reconcile systems and generate reports. What changes is the team, not the technology.
Engineering
Your developers already use Codex and Claude Code, at different levels and with no shared rules. We make it consistent: how tasks get scoped, where repo context lives, what agents may touch, how generated code gets reviewed. Then the harder workflows most teams haven't reached, like large refactors and test generation at scale.
Data, finance and operations
Cleaning data, reconciling systems, building reports. We train analysts and ops teams to run this work through agents themselves instead of joining an engineering queue.
Support, HR and wider teams
For teams that never touch code, we train on the document and workflow agents that fit their work, and on the judgement that matters everywhere: what to delegate, what to verify, when to involve a person.
Your internal AI playbook
The rules, written down: what agents may access, what needs human review, how sensitive data is handled, when autonomy is appropriate. Engineering gets the deeper version covering repo context, code review and production permissions.
How we work
Five steps from mapping the opportunity to making each workflow cheaper than the last.
1. Map the opportunity
We look at how work actually happens: repetitive tasks, bottlenecks, manual handoffs, knowledge stuck in one person's head. You get a roadmap ranked by impact, risk and effort.
2. Redesign the workflow
AI on a broken process gives you a faster broken process. We decide what stays human, what AI assists and what runs alone, then define the context, actions, approvals and failure paths.
3. Build and integrate
We build the agents, integrations, context systems, evaluations and infrastructure needed to run in production, inside the tools your teams already use.
4. Train your teams
A workflow nobody trusts goes unused. We train the people around it by department and role, and for engineers that means Codex and Claude Code on your own codebase.
5. Measure and scale
We instrument quality, adoption, automation rate, intervention, cycle time and cost. What works becomes the template for the next workflow, so each one costs less than the last.
Works with what you already run
We integrate with CRM, ERP, databases, documents, email, internal apps, legacy software, cloud infrastructure and developer tooling, and add new infrastructure only where it earns its place.
Frequently asked questions
What becoming AI-native involves, and how the work is scoped and measured.