AI transformation for engineering teams · six weeksYour team, shipping with agents.
Measured by Friday.

Not a transformation programme. One repository, a baseline taken in week one, our delivery pipeline installed inside your conventions, your team running it, and the same numbers measured after.

Book a consult See our work

engineers on the agentic path at a practice we built it for

5 → 1

developers needed for two Android apps, same timeline

6 weeks

one repo, a baseline, the pipeline, the numbers after

Claude Code

MCP servers, guardrails, generated tests, living docs

Six weeks in your repo · scroll

Click a week to jump

Six weeks: the pipeline goes into your repo, your team runs it, and it is measured against your own week 0. The figures shown are an example, not a result.

What you get.

The pipeline on your repo: planning agent, story population, autonomous implementation inside your guardrails, automated review, generated tests, living documentation, human-in-the-loop escalation

A baseline before anything changes: lead time, pull-request cycle time, review turnaround, incidents, test coverage on new code

Your conventions as the guardrails: linting, CI, branch rules, review policy, enforced on every change

Your engineers running it from week three, with us watching; a recorded walkthrough and a week of questions answered

The same numbers after, in a review you sign; then widen it or stop

Engagement: Fixed scope, six weeks · NET 14 · Ask about the six weeks →

Good for

— A team of five to forty engineers with a roadmap longer than the year

— A CTO who has been asked about AI by the board and wants a number, not a slide

— A codebase with CI and review already in place, however imperfect

We will say no when

— The brief is to replace engineers rather than to ship more with the ones you have

— There is no CI and nobody wants to write the first test

— Nobody on your side can own the decision to widen it or stop

The pipeline

Six stages. A person signs every increment.

The same pipeline that runs every Hopsig build, installed on yours.

01PlanThe build is planned with a solutions-architect agent before code is written. The plan is a document you can object to.

02StoriesEpics and stories populated and tracked automatically, in your Jira or ours.

03BuildStories implemented autonomously inside your guardrails, every change checked against them.

04ReviewAutomated review and generated tests before a senior looks. A senior always looks.

05DocumentDocumentation written as the code changes, never a week behind.

06EscalateWhen a decision needs a person, it is pushed to a phone with the context, and the pipeline waits.

How the six weeks run.

Fixed scope, fixed price

Week 0

Read the repo, take the baseline

Conventions, CI, review policy, and the five numbers as they are today. Written down before anything changes.

Week 1

Pipeline on one service

Planning, stories, implementation inside your guardrails; the first agent-authored pull request reviewed by your senior.

Week 3

Your team runs it

We watch, answer and adjust the guardrails. Escalations go to your phone, not ours.

Week 6

The same numbers, after

Lead time, cycle time, review turnaround, incidents, coverage. In a review you sign. Widen it or stop.

Then

Widen, or not

The next repo at a fixed price, or a handover with the pipeline staying as it is.

What we measure.

Before, in week one; after, in week six

Lead time

Idea to production, per story.

PR cycle time

Opened to merged.

Review turnaround

Opened to first senior review.

Incidents

Caused by changes shipped through the pipeline.

Coverage on new code

Tests generated and kept.

From the record.

All case notes →

01 · AI · delivery

Data & AI

Agentic software-delivery framework on Claude Code

Problem — An engineering practice wanted to deliver faster without losing review, tests and documentation along the way.

What we did — An end-to-end pipeline on Claude Code: project planning, automated epic and story population with progress tracking, autonomous story implementation, a dedicated solutions-architect agent, guardrail enforcement, automated code review and test generation, living documentation, and human-in-the-loop escalation pushed to mobile when a decision needs a person.

Measured — Rolled out to 10 engineers and now the practice’s default path for new work. Two Android apps scoped at five developers were shipped by one, on the original timeline.

Claude CodeMCP serversAgent orchestrationGuardrailsAutomated reviewJira

Client withheld · details on the callAsk about this →

02 · Enterprise · automation

Data & AI

Autonomous development pipeline for Dynamics 365

Problem — Customising a CRM platform end to end without a team of specialists.

What we did — Provisioned a full Dataverse environment and Azure AD app registration, then authored the project blueprint covering three MCP servers and plugin lifecycle management, letting an agent drive customisation end to end.

Measured — Agent-driven customisation running end to end.

Dynamics 365DataverseAzure ADMCPClaude Code

Client withheld · details on the callAsk about this →

03 · AI systems

Data & AI

Multi-agent orchestration framework

Problem — Several independent AI agents had to work one objective together, reliably, not just in a demo.

What we did — A Streamlit proof of concept first, to validate the idea in days. Then production: FastAPI backend, React front end, LangChain to define agent roles and collaboration flows, OpenAI and Groq models behind them.

Measured — Coordinated workflow simulation among autonomous agents, reliably repeatable — [ADD RUNS / SUCCESS RATE].

FastAPIReactLangChainOpenAIGroqStreamlit

Client withheld · details on the callAsk about this →

04 · AI · small models

Data & AI

Small language models where an LLM was too expensive

Problem — An accuracy target that a large model met at a cost the business could not.

What we did — Shipped a product on small language models as a cost-effective alternative, weighing accuracy against inference cost.

Measured — Accuracy target met at a fraction of the inference cost — [ADD COST / ACCURACY].

SLMsInference costEvaluation

Client withheld · details on the callAsk about this →

Questions we get about this.

No. It changes what they spend the day on. The practice we built it for kept its ten engineers and shipped more; the two-apps-by-one-developer result freed four people for other work, it did not remove them.

Claude Code with MCP servers for the pipeline, your existing CI and review tooling as the guardrails. Model choice is whatever passes your eval at a cost you accept; the pipeline does not care about the logo.

It runs in your repo and your accounts. Model calls go through a provider with no training on your data, or through your own cloud account if you prefer; we write down which, in the SOW.

Then you stop at week six, the pipeline stays if you want it, and you have a measured answer instead of an opinion. That outcome is in the SOW too.

AI transformation · the call

Bring one repo. We will show you the Friday after.

Bring the problem, not a brief. If it is outside what we do well, we will say which of our contacts to call instead.

Book a consult See our work

Or email info@hopsig.com — reply within one working day.

AI transformation for engineering teams · six weeksYour team, shipping with agents.Measured by Friday.

What you get.

Six stages. A person signs every increment.

How the six weeks run.

Read the repo, take the baseline

Pipeline on one service

Your team runs it

The same numbers, after

Widen, or not

What we measure.

From the record.

Agentic software-delivery framework on Claude Code

Autonomous development pipeline for Dynamics 365

Multi-agent orchestration framework

Small language models where an LLM was too expensive

Questions we get about this.

Does this replace engineers?+

Which models and tools?+

Does our code leave the building?+

What if the numbers do not move?+

Bring one repo. We will show you the Friday after.

What you get.

Six stages. A person signs every increment.

How the six weeks run.

Read the repo, take the baseline

Pipeline on one service

Your team runs it

The same numbers, after

Widen, or not

What we measure.

From the record.

Agentic software-delivery framework on Claude Code

Autonomous development pipeline for Dynamics 365

Multi-agent orchestration framework

Small language models where an LLM was too expensive

Questions we get about this.

Does this replace engineers?+

Which models and tools?+

Does our code leave the building?+

What if the numbers do not move?+

AI transformation for engineering teams · six weeksYour team, shipping with agents.
Measured by Friday.