DEEPGRAIN · AN OPERATING CONSULTANCY BUILT FOR THE AI ERA.
Read first. Then the numbers move.
A small set of organisations who chose to map the work at click level before they bought a tool. Each engagement built to compound long after we'd left.
Finance and People Ops, before and after we read the grain.
⚖ Two months on the champions had shipped five more agents we never scoped. That is the test.
DEEPGRAIN · AN OPERATING CONSULTANCY BUILT FOR THE AI ERA.
Same shape. Different grain.
Every engagement starts with a real workflow and ends with a number the client owns. Here are three more, in the same before-and-after shape.
From training programme to production tools.
A doing problem, not a training problem.
A new operating model, not a new tool.
In the client's words
The full readouts below carry the testimonials, the engagement shape, and the outcomes the team carried after we left.
Defence Technology · Series B · ~200 employees · 12 week engagement
83 hours reclaimed. Every week.
A rapidly scaling defence technology company had hit the classic post Series B bottleneck. Manual processes couldn't keep pace with growth. Finance was buried in AP and AR admin. People Ops was fielding the same policy questions every day.
The effort was there. What had never been done was a proper read of how the work actually happened, so nothing built on top of it ever quite fit.
Finance time reduction
People Ops queries eliminated
AI confidence per participant
“The coaching approach meant we built exactly what we needed, using the tools we already had. Two months on, our champions are still building and extending automations independently.”
Chief People Officer
0 critical issues in 2 months post engagement
5 new agents built independently by champions
6 team members onboarded via the workspace alone
Financial Data and Analytics · ~600 employees · Multi cohort programme
15 people. 5 squads. 5 deployed solutions.
A sophisticated financial data business needed its People function to operate differently. Not just to use AI, but to build with it. The capability was there. The work was to move the whole team up the maturity curve together, building real tools rather than running generic training.
Five cross functional workstreams. Each squad owned a specific problem and shipped a live solution. Discovery to delivery in seven weeks.
Participants across 2 timezones
Production tools deployed
Weeks from discovery to live solutions
AI confidence survey: pre and post delta across all participants
Custom AI Playbook delivered for permanent internal use
Champions continuing to build independently post programme
Transit Technology · PE backed · 260 employees
A doing problem. Not a training problem.
Engineering had strong AI adoption. Mandated, tracked, effective. The rest of the business had licences and a champions programme that produced no outputs. The gap was time, focus, and someone to build alongside until the tools were live and the skills sat in house.
40 people across two timezone windows. One named production tool. Two internal builders who can now maintain and extend everything without external support.
People enabled in wave one
Internal builders trained
First year licence cost eliminated
Climate Consultancy · Growth stage · ~60 employees
The right systems for the next phase.
A specialist climate consultancy scaling through significant growth needed its Associate Lifecycle redesigned from scratch. Onboarding through to offboarding, with AI woven through every stage.
A new operating model rather than a new tool. Built to compound as the organisation continued to grow.
Outcomes & proof
What you can expect to take away.
- How do you measure whether the work actually compounded?
- We define two or three operating metrics with you at the start of an engagement — usually a mix of throughput (hours reclaimed, cycle time on a named workflow) and durability (number of internal champions still extending the system 60 and 120 days after we leave). We come back at the 90-day mark to read those numbers with you, on the record. If they didn't move, we say so.
- What does 'lasting change' look like in practice?
- Concretely: a People Ops team handling 70% of inbound queries through systems they own and modify themselves; a finance close that runs without the consultant who designed it; a hiring loop that survives the departure of the head of talent. The test is whether the work keeps running, and keeps improving, without us in the room.
- How are case studies selected, and what gets anonymised?
- We only publish case studies the client has reviewed and approved line by line. Where the work touches sensitive sectors (defence, regulated finance, early-stage climate) we anonymise the company name and any identifying detail of the engagement. The patterns and numbers are real; the brand is held back when the client prefers it that way.
- What are the confidentiality boundaries during and after the work?
- Everything we see inside an organisation is covered by an NDA from the first conversation. We don't share client names without written permission, we don't reuse client artefacts, and we don't take on directly competing engagements inside a 12-month window without disclosing both sides. Patterns we observe inform our writing; specifics never do.
- Will you put us in touch with a reference client?
- Yes. After a first conversation, if the fit looks right, we'll introduce you to one or two operators we've worked with (typically a Chief People Officer or COO) who can speak directly to how the engagement ran and what was left behind. We ask permission before each introduction; we never broker a reference cold.
Stay close
Field notes from the work.
A short dispatch, a few times a quarter. Patterns we keep seeing in how organisations actually run.
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Want this kind of before and after on your own function?
Thirty minutes. One workflow. We map it together.
The first move
Want this kind of before and after on your own function?
Thirty minutes. One workflow. We map it together.