Axelerant is a Registered partner in the Claude Partner Network, and an AI-native company that runs on Claude. Everyone has the model now. What matters is what you build on top, and whether it maps to a business goal.
Axelerant is a Registered partner in Anthropic's Claude Partner Network. But access to Claude is not the differentiator. Everyone has the model. What matters is what you build on top, and whether it maps to a business goal.
We build in two layers. Adoption by standard: every project carries a Claude context file, skills are committed to the repo, and a human reviews every merge. And an agentic layer on top, the foundation of how we operate and deliver, including Bott, where an engagement, its codebase, and its institutional memory meet.
We ran it on ourselves first. That is the offer.
Registered partner, Claude Partner Network. Claude Code on every engineering project.
“The pilots impressed everyone and changed nothing.”
Demos that map to no actual use case, run by enthusiasts, measured by applause, retired by the next quarter's priorities.
“Everyone is using AI, and nobody governs it.”
Adoption happened in the shadows: no standards, no review gates, no answer when security or legal asks what the AI touched.
“The output is fast, and nobody trusts it.”
AI-generated work shipping without a review discipline, until the first bad merge or wrong number makes the whole program guilty by association.
“The tools arrived before the use cases.”
Licenses bought at the top, mapped to nothing at the bottom, renewal approaching with no evidence either way.
We treat AI adoption as an engineering discipline: context before prompts, use cases before tools, and human judgment at every consequential point. The model is the floor. The value is the layer you build on it.
From AI-assisted engineering delivery to agentic systems, API integrations, estate-scale analysis, adoption standards, and model-aware architecture. The layer you build on the model, not the model itself.
Claude Code across the development lifecycle, implementation, review, testing, debugging, documentation, with human review before every merge.
Claude Code · Review gates · Human-in-the-loop merges
Purpose-built agents with real jobs: engagement memory, delivery automation, knowledge systems, each mapped to a business goal before a line of it was built.
Bott · Foyer · Memra · Fynn · Quire · Forta
Custom tools and automations on the API: batch processing, custom agents, and integrations into the systems teams already live in, like Jira and Slack.
Anthropic API · Batch processing · Jira · Slack integrations
Workflows where Claude carries the organizing load, tens of thousands of data points structured in hours instead of days, while the specialist keeps every judgment call.
Estate-scale audits · Structured extraction · Specialist review
The governance layer most AI programs skip: context-file standards, review gates, role-level usage guidelines, and the playbook that turns enthusiasm into organizational capability.
Context files · Review gates · Role-level guidelines
Choosing where an LLM genuinely belongs in a system, and where it does not, so AI lands as infrastructure with a job, not a feature looking for one.
System design · LLM placement · Guardrails
Named engagements, real systems in production, framed at exactly what we did.
Six interconnected systems, Quire, Foyer, Bott, Forta, Memra, and Fynn, are the foundation of how we operate and deliver: some in production, others still earning their place. Memra is the shared knowledge layer. Bott is where the engagement, the codebase, and institutional memory meet. Fynn carries the people side. None of them started as a technology decision. Each started as a business goal.
On an estate-scale technical SEO audit for a large membership organization, four domains, 85,000+ broken links, 14+ regional subfolders, Claude carried the organizing load. Days of manual structuring collapsed into hours, and the judgment never left the specialist.
Our Drupal delivery teams run a one-command Claude workflow: name the ticket, and the pipeline moves from specification through build, validation, and tests to a reviewed pull request, with a human gate before anything merges.
Bring us the tools you bought, the experiments that stalled, or the governance question nobody owns, we will tell you honestly what we would keep, kill, and build.
