Custom CRMs. Replaced bloated, seat-priced CRMs with lean systems shaped around how the team actually sells and follows up.
Agentic engineering you can audit.
Built on principles you can trust.
Isolated agent
Every task starts in a sealed box.
Each task gets a fresh, disposable environment: its own copy of the code and the app, no credentials, no secrets, an allowlisted path out. The agent proposes; it never merges, deploys or approves anything.
Layered checks
Verified independently, in layers.
Checks rerun in a clean environment; scans detect test-gaming, secrets and risky dependencies; a fresh-context reviewer's findings are confirmed by a second model; an acceptance agent uses the running app like a person would.
How we buildRisk-based sign-off
Trust follows risk, not habit.
Routine changes that pass every layer merge on their own. Anything touching security, money, data, infrastructure or the agents themselves always reaches a human, with intent, evidence and gaps laid out.
Request an assessmentClaude now writes about 80% of the code merged at Anthropic, behind layered automated checks. That's the model we build to.
Humans sample by risk and approve critical code · Anthropic, “How Anthropic secures its AI-native software development lifecycle,” July 2026
Why the system matters
AI writes more of the code every quarter. The checks decide whether that's safe.
Merged code
~80%
of merged code at Anthropic is now written by Claude, behind layered automated checks
Anthropic, July 2026Security flaws
45%
of AI code-generation tests introduced security flaws
Veracode GenAI Code Security ReportShadow software
60%
of builders made software outside IT oversight
Retool, Build vs. Buy 2026Change risk
~70%
of outages are caused by changes to a live system
Google SREAgentic engineering
for
AI-built internal tools
Request an assessmentLegacy payroll migration. Adapted and migrated aging internal payroll software onto modern, maintainable infrastructure without disrupting pay runs.
AI built into everyday workflows. Built AI into the tools teams already use: intake that sorts itself, drafted follow-ups, documents read automatically, reports that summarise themselves.
Network infrastructure. Years of designing and running business networks: the reliability discipline every build inherits.
Representative engagements.
Client names stay private. The work speaks for itself.
What we do
Four ways we take cost and risk out of how you work.
Internal tool takeover & security review
Your teams are shipping AI-built tools. Keep them: we inventory them, audit them, harden them, document them and maintain them.
Learn moreAgentic engineering & AI integration
Custom systems that replace bloated seat-priced software, with AI models built in where they pay off, delivered by agents inside a harness that enforces your security rules.
Learn moreNetwork infrastructure
Architecture, segmentation, connectivity and monitoring, from the team that started out keeping networks up.
Learn moreAI advisory
Where AI actually moves cost and margin across your teams, which tools to standardise on, the governance around them, and a rollout people adopt.
Learn moreAI integration
Models in production, under the same controls as everything else.
We integrate AI services into the systems we build and into the ones you already run: retrieval over your own documents, extraction and classification, drafting, and agents that act only through narrowly scoped tools.
An AI feature is still software. It gets an owner, a test suite, a security review and a record of every change, like the rest of the system.
- Model-agnostic: Claude, GPT, Gemini or open-weight models, within your approved-vendor list.
- Runs on accounts your organisation owns, with data-retention terms agreed before anything is sent.
- Evaluated before launch and monitored after, with prompts and outputs logged.
- Tested for prompt injection and data leakage as part of security review.
- High-risk outputs go to a person before they take effect.
The economics
Agentic engineering changes what software costs.
Agents do the implementation work inside a system that verifies every change, so a small team delivers what used to take a large one. That's a fraction of the cost and a fraction of the time of a traditional development partner, with more verification, not less.
Traditional development partner
Large teams, long timelines.
- Estimates driven by headcount and staffing ramps
- Months between kickoff and first release
- Review capacity limits how fast changes ship
- Documentation and audit trails as an afterthought
Tethered
A fraction of the cost. A fraction of the time.
- Agents implement; engineers set intent and judge risk
- A fixed quote after scoping, not open-ended time and materials
- Layered, independent verification on every change
- AI features shipped with evaluations, logging and human sign-off
- Every change attributable, reversible and documented as it ships
How we build
A loop built so mistakes never reach production unchecked.
The model isn't the product. The system around it is: isolation, enforced policy, layered independent verification and risk-based human sign-off.
What every engagement includes
You own it. You can audit it. You can run it without us.
Every project, without exception.
01
Private by default
Every project lives in a private repository your organisation owns. We don't train models on your code, and we tell you exactly which AI providers process it and on what terms.
02
Documented in a private DeepWiki
Architecture diagrams, module-by-module explanations and a Q&A assistant over the actual code, delivered privately to your organisation.
03
Auditable end to end
Every change traces to its task, its run, the model and the approver, and can be reverted in one action.
04
Deployed where you need it
Your cloud, your data centre, or managed by us. Single sign-on, least-privilege access and logs that integrate with your monitoring.
Resources
How we work,
and where to start.
How we buildTen principles, enforced by the harness, not hoped for in a prompt.
Each grounded in published work from the teams operating agents at the largest scale today.
TakeoverThe tools your teams built work. That's not the same as safe.NetworkWhere we started. Still where everything runs.AssessmentSoftware spend & risk assessmentAI integrationModels in production, under the same controls as everything else.Past workLegacy payroll migration, without disrupting pay runsPast workCustom CRMs shaped around how teams actually sellFAQQuestions security teams ask.Start with an assessment.
We'll map your software spend, your internal-tool risk and where AI integration would pay off, and tell you what's worth doing first.