Your dedicated AI team.Goal-based.Outcome-driven.
A cross-functional team built around your goals, with defined resources and defined deliverables. It takes AI past the pilot and into your daily operations.
AI Pod · Example value report
Not client data
Example: cumulative value vs baseline
Example roadmap
- Invoice automationLive
- Support copilotLive
- Ticket triage agentShipping
- Forecast assistantNext
Example release cadence
Shipping on the agreed rhythm
Example status
- Build
- Run
- Measure
Status indicators shown for illustration
The challenge
Plenty of AI ideas. No dedicated team to deliver them.
Without a dedicated AI team, people are pulled away from other projects, priorities keep shifting, and no one owns AI from the first build to measurable business value.
Pilots that never scale
Proofs of concept show promise but stall before reaching day-to-day operations.
Skills spread across vendors
Data, ML, integration, and operations sit with different teams, leaving gaps in end-to-end ownership.
Shipped, then abandoned
Models drift and automations break when no one is responsible for maintaining and improving them.
Value nobody can prove
Leaders invest in AI without clear evidence of its business impact.
The approach
A dedicated AI team set up around your goals.
Here is what happens when you start with Mako. Everything is planned and signed off before the pod starts building.
Define the goals
Workshops with your business owners to agree the outcomes and capture today’s baseline.
- You get
- Goal sheet with baseline measures
Define the resources
We map the skills each goal needs and size the pod to the use case.
- You receive
- Recommended pod: roles and size
Define the deliverables
We agree what the pod ships, the release cadence and who operates it in production.
- You receive
- Pod charter for your sign-off
Put the plan to work
The pod builds and ships against the charter, and reviews results with you regularly.
- You receive
- Production releases and review reports
The difference
After the engagement, what is still working?
AI tool licence
Software nobody adopted
Project team
A handover document
AI Pod
AI running in production, still improving.
Why an AI Pod
Projects end at delivery. Pods are measured on what comes after.
| Aspect | Project team | AI Pod |
|---|---|---|
| Scope | A fixed project brief | Goals and outcomes, reprioritized by value |
| Team | Assembled per project | Dedicated, cross-functional, one engagement lead |
| Delivery | Big releases, long gaps | A steady production cadence |
| After go-live | Handed over | Operated and improved by the pod |
| Reporting | Milestones completed | Realized value, reported every quarter |
How we measure value
Agreed upfront. Reviewed every quarter.
Value isn’t based on Mako’s opinion. It’s measured against the goals, baselines, and success criteria you agree on from the start.
Establish the baseline
Before development begins, we document current performance using your existing data.
Define measures and targets
Together, we agree on what success looks like and how each goal will be measured.
Track real-world results
We use production data and verifiable reports to track progress against agreed targets.
Review and re-prioritize
Every quarter, we review results with your business stakeholders, identify what’s working and what isn’t, and adjust the roadmap accordingly.
Example value scorecard
Illustrative example
| Measure | Baseline | Target | This quarter |
|---|---|---|---|
| Invoice processing time | 3 days | 1 day | 1.5 days |
| Tickets auto-routed | 0% | 60% | 45% |
| Manual reconciliation hours | 120 / month | 40 / month | 70 / month |
Numbers are examples only. Your measures and targets are set together during planning.
The AI Pod model
Four principles behind every pod.
Outcome-based roadmap
One accountable roadmap tied to your business goals, with value tracked cumulatively over time.
Cumulative value
Build-and-run accountability
The pod operates what it ships, so reliability and results stay with the people who built it.
Cross-functional, one lead
Engineering, data, QA and operations skills under one engagement lead, your single point of contact.
Compounding assets
Reusable components and lessons carry from one workflow to the next, so each delivery gets faster.
- Workflow 1
- Workflow 2
- Workflow 3
Inside a pod
One pod. Every skill AI needs.
Pod size and roles depend on your use case. A single automation goal may need a small core team; a multi-workflow roadmap needs more skills. The roles below are typical, and we agree the exact mix with you during planning.
Your goals
At the centre of every pod
Engagement lead
Your single point of contact for priorities and progress.
AI / ML engineers
Build models, agents and LLM workflows.
Automation & integration
Connect AI to your systems, data and processes.
Data engineers
Prepare, govern and pipe the data AI depends on.
QA & AI evaluation
Test accuracy, safety and edge cases before and after release.
MLOps & cloud ops
Deploy, monitor and keep everything running in production.
FAQ
Questions, answered.
Still deciding? Start with one goal and we’ll map a pod around it.
A dedicated, cross-functional team built around your AI goals, with defined resources, defined deliverables and one engagement lead.
A goal sheet with baseline measures, a recommended pod shape (roles and size) and a pod charter covering deliverables, release cadence and production responsibilities, all for your sign-off before building starts.
They are written into the pod charter during planning and signed off by your business owners. Changes go through the same review, so priorities stay tied to value.
During the engagement, the pod operates what it ships. Monitoring, support hours, escalation and any handover to your team are agreed in the pod charter before go-live.
Against the goals, baselines and success criteria you agree from the start. We document the baseline before development begins, track results with production data and review them with your stakeholders every quarter.
An FDE is one engineer embedded in your business to solve a specific problem. An AI Pod is a team that runs an ongoing AI roadmap across several use cases.
Yes. As goals change, the pod’s roles and size can be adjusted at a quarterly review, with the change agreed in the charter.
Mako is ISO 27001 certified, and every pod works inside your security policies, access controls and data governance rules.
AI Pods
Give your AI roadmap a team that owns it. Build it. Run it. Prove it.
Your next step is a planning call. Bring one business goal, and we’ll walk through the pod shape, the first deliverables and how value would be measured.
No commitment. You leave with a clear outline of what a pod would look like.