Introduction deck

Bookbag · AI engineering consultancy

We set up AI engineering inside your company.

We assess how your teams build software, set up AI-assisted workflows in your own tools, train your engineers, put the controls in place, and build alongside your team until it runs without us.

In one slide

What we sell, and what we don't.

We sell: Consulting and hands-on engineering. We assess, set up, train, govern, build and run AI-assisted software engineering inside your company.
We don't sell: Software licences, coding agents or outsourced developers. Your people and your tools stay yours, and you never have to buy software from us.

Start with you

Where is your organization on this line?

01
Licences
Coding assistants bought for every engineer.
02
Experiments
Teams try agents their own way. Results are anecdotes.
The gap starts here
03
Operating model
Roles, review, workflows and budgets designed for AI-written code.
04
Governed delivery
Agents work the real backlog under policy and approval.
05
Measured value
Outcomes reported against a baseline the board accepts.

Between stages 2 and 3 is the gap we work on: tools without an operating model.

The questions we hear

Four questions engineering leaders are being asked.

Is it working?

Usage is easy to count. Lead time, quality and cost per change are not.

Is it safe?

Who approved what the agents changed, and where is the record?

What does it cost?

Licences and tokens reach the invoice before outcomes reach anywhere.

Will it scale?

Pilots run on enthusiasm. Every team needs roles, review and budgets.

The public evidence

Adoption is nearly universal. Control and cost are not settled.

90%

of the technology professionals DORA surveyed use AI at work. The same study still finds a negative relationship between AI adoption and software delivery stability.

Google Cloud, Announcing the 2025 DORA Report, 23 September 2025

65%

of engineering teams using agentic coding will treat the IDE as optional by 2027, Gartner predicts, moving control, governance and validation to automated platforms.

Gartner, press release on enterprise AI coding agents, 20 May 2026

2028

is the year Gartner predicts AI coding costs will overtake the average developer's salary, as token consumption grows and vendors move to consumption pricing.

Gartner, press release on AI coding costs, 24 June 2026

What developers report

Individuals are faster. The organization has not caught up.

87%

of developers say AI helps them ship work faster. Only 28% say their day-to-day work has noticeably less friction than before AI.

Atlassian, State of Developer Experience Report, 2026 edition

49%

of developers spend four or more hours a week reviewing AI output, correcting prompts and similar work.

Atlassian, State of Developer Experience Report, 2026 edition

17%

of developers, and 23% of engineering leaders, say their organization has a clearly communicated, actively implemented AI strategy across the software lifecycle.

Atlassian, State of Developer Experience Report, 2026 edition

Here's the difference

Tool vendors sell licences. Outsourcers sell people. We change how your own engineering organization works.

Your people

Your tools and repositories

Your numbers, measured

What we do · 40 services · part 1 of 2

Every service we offer.

AI readiness assessment

AI engineering maturity review
Tool and licence audit
AI spend review
Delivery baseline
Risk and security review
Tool and vendor selection
Roadmap and investment case

AI workflow setup

Coding assistant rollout
Agent workflows
Codebase context
AI code review
Test generation
CI/CD integration
Ticket and backlog integration

Engineering team enablement

Hands-on team workshops
Engineer training
Reviewer training
Engineering leadership training
Playbooks and standards
AI champions programme
Coaching and office hours

What we do · 40 services · part 2 of 2

Every service we offer, continued.

Governance & quality

AI usage policy
Agent access and permissions
Approval rules
Spending controls
Secure setup
Quality gates and evaluations
Audit evidence

AI-native software delivery

New features and applications
Legacy modernization
Integrations and internal tools
Test coverage uplift
Maintenance backlog
Prototypes and proofs of concept

Managed AI engineering

Workflow operations
Tool and model updates
Usage, cost and outcome reporting
New team onboarding
Ongoing team support
Quarterly leadership review

How it works

Start small, prove it, then scale.

14 weeks

Readiness assessment

For a leadership team deciding where to start.

AI readiness assessment

212 weeks

AI Engineering Transformation

With two to four delivery teams, on their real backlog.

AI workflow setupEngineering team enablementGovernance & quality

3Ongoing

Scale and operate

For organizations taking the model to more teams.

Managed AI engineeringAI-native software delivery

Step 1 · 4 weeks

Readiness assessment

For a leadership team deciding where to start.

What you have at the end

A baseline of AI use, spend and delivery performance today
Risk, security and governance gaps, ranked
A sequenced roadmap and an investment case

What we need from you

An executive sponsor who owns the outcome
Time with engineering leads and a few engineers
Read access to delivery data: repositories, tickets, CI
Today's AI tools, licences and spend

Step 2 · 12 weeks

AI Engineering Transformation

With two to four delivery teams, on their real backlog.

Weeks 1–2
Baseline and design
Agree the measures, the teams and the controls.
Weeks 3–8
Build and run
Governed AI workflows on live work, reviewed weekly.
Weeks 9–12
Measure and hand over
Evidence against the baseline, and a plan to scale.

What you have at the end

An operating model your teams run without us
Governed AI workflows in production on real work
Policies, approvals and budgets in force
An evidence pack measured against week one

How we work

What you can hold us to.

One specialism.: Software engineering is the only function we work on. We do not sell general AI strategy, data platforms or chatbots.
No software to buy.: We work with the repositories, coding assistants and models your teams already use. You never have to buy software from us.
Your backlog, not a demo.: Work starts on real items in your repositories within the first fortnight, under your review rules.
A baseline before anything changes.: We agree the measures in week one and report against them, including the ones that do not move.
Controls before scale.: Policies, approvals and budgets are in force before another team joins.
Senior people on the work.: The people who scope an engagement are the people who deliver it.

Honest check

Is this a fit?

A fit

AI coding tools are in use, and you can't yet show what changed
An executive owns the outcome
You can give us two to four teams and real work

Not a fit

You want to buy a coding tool
You want to outsource development
You want a strategy deck with no delivery

Not sure yet? Start with a briefing for your leadership team.

Not ready to start?

Take part in our study instead.

Study · first edition

The State of AI Engineering in Enterprise Software Teams

How engineering organizations are adopting AI-assisted development: what they use, how they govern it, what it costs, and what they can show for it. We are inviting CTOs, CIOs and heads of engineering to take part.

A short, structured interview
Findings shared with participants before publication
Your position against an anonymized peer group
Nothing attributed to you without written consent

Next step

Start with the readiness assessment.

A short scoping call with your sponsor, then a written scope and fee. Four weeks after kickoff you have a baseline, the gaps ranked and a roadmap your leadership can act on.

Request a scoping call

4 weeks · scope and fee agreed in writing · hello@bookbag.ai