Five stages from idea to production, and a way out of every one
The Applicat Method is Applicat AI's five-stage delivery model for applied AI: Frame, Prove, Build, Deploy and Run. Each stage produces something you can look at, and each ends in a decision you make on the evidence. Any one of the five can be the last, which is the point of drawing it this way. Most enterprise AI does not fail loudly. It sits in a pilot nobody is willing to kill.
Five stages between an idea and a system in production.
Every stage moves the frontier models and your operation closer together, and ends in a decision made on evidence. Nothing counts as in production until the two genuinely intersect.
011 to 2 weeks
Frame
Where the models could meet the work
023 to 4 weeks
Prove
One case proved on your real data
034 to 10 weeks
Build
Integrations, guardrails, evaluation gates
042 to 4 weeks
Deploy
Rollout, training, adoption measured
05Ongoing
Run
Monitored, evaluated, kept current
Each circle is the same mark at a different stage. The outer circle is your organisation. The two lenses are the frontier models and your operation, and they are drawn closer at each stage because that is what the work actually does. The blue point is what is running in production, and it does not appear until the overlap is real.
Every stage ends at a gate, and the work can stop there.
Commitment climbs one step per stage, and the evidence under it accumulates at the same rate. That is what makes the next step defensible, and what makes stopping cheap. Select a stage to see what it produces and what it decides.
Fig. 02 The method, drawn against commitment
- Decision gate
- The engagement can stop here
Stage 01 1 to 2 weeks
Frame
We find the places where AI could move a number your leadership already watches, then rank them by what they are worth, how buildable they are and whether the data exists. You come out with a shortlist, a value case for each candidate, and an agreed description of what success would look like.
Outputs
- Your processes ranked by value and feasibility
- What data and integrations each one would need
- The number each candidate is meant to move
- Risk, residency and governance requirements
The decision at the end
Is any of this worth proving?
- Yes
- Take the top-ranked candidate into Prove.
- No
- Stop here. The ranked list and the value cases are yours, and your own team can act on them.
1 to 2 weeks committed
Stage 02 3 to 4 weeks
Prove
We build the top candidate for real: your own data, your own environment, the permissions that actually apply. It is then scored against cases your team picks, and measured for what it costs and how fast it answers at the volumes you expect. The stage ends in a recommendation, and no is a normal outcome.
Outputs
- A working system on real data and real integrations
- A test set drawn from your own cases, with baseline scores
- Cost and speed at the volumes you expect
- A written go or no-go, with the evidence
The decision at the end
Does it work on your data, at your cost and speed?
- Yes
- Harden the proof into a production system.
- No
- Stop on measured evidence, with the next candidate already ranked.
4 to 6 weeks committed
Stage 03 4 to 10 weeks
Build
The proof becomes something the business can depend on. It connects to the systems you already run, respects who is allowed to see what, keeps a record of every decision it makes, and refuses what it should refuse. Security review happens in this stage, before release. From here on, no change reaches a user until it has passed the tests, which is what an evaluation suite is for.
Outputs
- Production architecture and integrations
- Permissions, audit trail and limits on what it may do
- Every change re-tested automatically before release
- Security and compliance documentation
The decision at the end
Is it safe, governed, and able to pass its own tests?
- Yes
- Roll it out to the people who will use it.
- No
- Stop before release. Nothing goes in front of users until it passes.
8 to 16 weeks committed
Stage 04 2 to 4 weeks
Deploy
We roll out with the people who will actually use it, agree the points where a person has to decide, train the teams and count usage from the first week. A system nobody opens is worth nothing, so adoption is measured and reported like any other number in the business.
Outputs
- Rollout plan and human checkpoints
- Training and playbooks for the teams
- Adoption and value reporting
- A supported first period after go-live
The decision at the end
Are people using it, and is the number moving?
- Yes
- Move to managed operations.
- No
- Stop at the end of the supported period. Nobody should fund a system people do not open.
10 to 20 weeks committed
Stage 05 Ongoing
Run
Volumes shift, the data behind the system changes, and the models underneath it are replaced every few months, so a live system left alone quietly gets worse. We watch accuracy, safety and cost daily, re-test on your own cases whenever anything moves, prove a new model before it reaches your users, and write a report each month against the number agreed at the start.
Outputs
- Accuracy, safety and cost watched daily
- Model upgrades proven before they reach users
- Continuous testing and improvement
- A monthly report against the agreed number
The decision at the end
Is it still worth what it costs, this month?
- Yes
- Continue managed operations for another month.
- No
- Take the system in-house with its documentation and its tests. It keeps running.
Reviewed every month
The rules that hold when a programme is under pressure.
01
The proof runs where the real thing will run
Proofs are built on your real data, in your own environment, with the permissions that genuinely apply. There is then no gap between what was demonstrated and what can be deployed, and that gap is where most pilots quietly die.
02
Nothing ships until it passes its tests
Every system carries a set of tests written from your own cases. They decide the go-live, they decide whether a model upgrade happens, and they catch a regression before your users meet it. That set is the evaluation suite, and it stays with you.
03
People keep the decisions that carry consequences
Agents take the volume. We design the points where a person decides, and we keep those points fast, because a checkpoint that costs somebody a day is a checkpoint they learn to route around.
04
Your data never leaves your control
Systems run on your own cloud accounts, or in a dedicated environment you control. Your material is not used to train shared models, and residency is settled at the Frame stage, before any architecture is fixed.
05
When a better model arrives, you can take it
Model capability moves every few months. We keep the model behind an interface with its own tests, so swapping one for another becomes a test run and a decision made on the scores.
06
The people who scope it are the people who run it
No hand-off from a strategy team to a delivery team, and no gap between the advice and the consequence. The engineers in the first workshop are the ones you call at go-live.
FAQ
Questions about the method
See the method on your own use case.
An Applied AI Sprint covers Frame and Prove in four to six weeks, on your data, inside your perimeter.