Nandu

Change how your team builds.We stay until it holds.

Buying tools changes little. We look at how your team works today, run the transition to building with agents, and stay involved until the new practice survives contact with a real deadline.

Our approach

The hard work starts after the prototype.

Nearly every engineering team has run an AI pilot by now. Far fewer ship differently than they did a year ago. The reason is rarely talent and rarely the tools. It is that the pilot never changed the daily practice, so the gains stayed with individual developers and never became the team's.

We work on that practice: how work gets planned, how it gets reviewed, what counts as done, and what the agents are allowed to assume. The infrastructure goes underneath so the whole thing still stands when a deadline hits.

We run our own company this way. What we transfer is what we use.

How an engagement runs

1

Assessment

How does the team build today? Where is AI already in use, where does it quietly fail, where would it genuinely pay off? We look at the real codebase and the real workflow, not a questionnaire.

2

Design

Together we agree on the target practice: conventions, review gates, what the agents get to know, and which parts of the infrastructure your team needs first.

3

Transition

Workshops first, then real work in the real codebase with us alongside. The first projects run under the new practice while the habits form.

4

Accompaniment

A practice shows its worth when a deadline hits. We stay through that phase, adjust what doesn't fit your team, and hand over once it holds without us.

Workshops

Training for engineering teams

No introduction to what an LLM is. These are working sessions on the questions that decide whether agentic development adds up or drifts.

Spec-driven development with agents, and why the plan has to outlive the session
Reviewing work when a model wrote it, and why self-reported success gets expensive
Structuring a codebase so several people and many agents can work in it at once
The quiet ways agentic development drifts, and the checks that catch it early
Scaling effort with risk instead of with size
Building the knowledge layer that lets the next session start ahead of this one

Formats

Half-day intro

One working session with the team: the practice, the failure modes, and a hands-on pass through your own codebase.

Multi-day deep dive

The full practice, taught and applied: planning, review, testing discipline, and the infrastructure underneath.

Full engagement

From assessment to accompaniment, scoped to your team and codebase. Most teams end up here.

What you're left with

A working practice the team actually follows, visible in the repository rather than in a document
The infrastructure licensed, set up, and running in your environment
Engineers who can tell solid agent output from plausible output, and can prove the difference
Knowledge that builds up across sessions instead of evaporating with each one
A codebase that many agents and several people can work on without it losing shape

Start with the assessment

We will look at how your team builds today and tell you honestly where agents pay off and where they don't.