Our AI delivery approach: seven steps from opportunity to operation
Most technology projects fail at a hand-off: lab to engineering, build to operations, vendor to client. Our AI delivery approach is built around those hand-offs. Seven steps connect discovery, measured experimentation, system engineering and long-term improvement, and the same steps apply to a document pipeline, a data platform or a quantum feasibility study.
Seven steps
Durations vary with scope; the order does not.
- 01
Discover
We start with the business goal and the people who own it. Workshops establish the number that should move, the baseline it moves from, the risks that worry the sponsor and the constraints nobody has written down. We walk the current process rather than reading about it, and record where decisions are made today and by whom. Discovery is short and it is the step most projects skip. What you get: a discovery note with objectives, baseline, constraints and the stakeholder map.
- 02
Assess
Then we test whether the goal is achievable with what you have. Data readiness is checked against real samples, not a schema. Architecture, security and integration points are reviewed. For optimization and quantum questions we build the classical baseline here, because a new method is only worth pursuing if it beats something. Financial assumptions get the same scrutiny as technical ones. What you get: a feasibility report with a go, no-go or reshape recommendation.
- 03
Design
With feasibility established, we define the target. Process flows show how work will move once the system exists. The architecture names components, hosting regions and the data residency rules that apply in India, UAE, Saudi Arabia or United States. Roles and approval gates are set, acceptance criteria are written so they can be tested, and delivery milestones are agreed with your team's calendar rather than ours. What you get: an architecture and design pack with acceptance criteria and a milestone plan.
- 04
Prototype
We then test the assumptions that matter most, in a controlled build on representative data. The scope is deliberately narrow: one document type, one camera, one forecast horizon. Success criteria from the design step are measured, and results are reported with their limits. A prototype that fails is a success of this step, because it failed before you funded a production build. What you get: a working prototype, a results report against the criteria and a decision on scaling.
- 05
Engineer
Production code follows, written to be maintained for years. Reviewed code, automated tests, integrations with your ERP, CRM or OT systems, validation controls and operational documentation are built together, not added at the end. Security requirements from the design step become tested controls. Our own products, where used, are configured and extended here. What you get: a tested, documented system ready for staged rollout.
- 06
Deploy
Rollout happens in stages: one site, one entity, one user group at a time. Behavior is validated against the acceptance criteria in the live environment, and monitoring is switched on before the wider release. Ownership and recovery processes are established with your team so that an incident at 2 a.m. has a named person and a runbook. We stay close until the system has run through a normal operating cycle. What you get: a live system, monitoring, runbooks and a handover record.
- 07
Improve
Finally, the system has to keep earning its place. Reliability, output quality, cost and adoption are reviewed on a schedule we agree. Models are retrained when drift appears, not on a calendar. Improvements are prioritized from evidence: what users do, what the logs show and what the discovery number is doing now. This step has no end date, and it is where most value is realized. What you get: a review cadence, a measured improvement backlog and a system that is still fit for purpose next year.
Why does the order matter?
Because every step removes a kind of risk the next one cannot. Assessment before design stops you architecting for data you do not have. Prototyping before engineering stops you productionizing a model that only worked on the demo set. We compress steps when the problem is well understood, but we do not skip them.
One written deliverable per step
Every step closes with a document your team can act on without us in the room, from the discovery note to the improvement backlog.
The same approach, different subjects
For a document processing solution, the prototype step is a few hundred of your invoices. For a quantum feasibility study, the assess step builds the classical baseline and the prototype is a simulation benchmarked against it. For a cloud platform, the deploy step includes load testing before the first production release. Read how we collaborate for the engagement models that wrap these steps.
Frequently asked questions
How long does the whole seven-step path take?
Discovery and assessment together usually take 2 to 6 weeks. A prototype runs 6 to 10 weeks. Engineering and deployment depend on scope, typically one to three quarters for an enterprise system. Improvement continues for the life of the system. A dated plan comes at the end of the design step and is revised in writing if the evidence changes.
Can we start partway through, for example with a prototype already built?
Yes, and it is common. We run a compressed assessment on what exists, confirm the data, architecture and success criteria, then continue from the right step. A prototype built elsewhere is treated as evidence, not a specification; if it does not meet production standards we say what it would take.
What do you need from our side at each step?
A sponsor who owns the number, access to the people who run the process, data samples under NDA, and a decision-maker for hosting and security questions. During engineering and deployment we need a counterpart in IT for integrations and access. The time commitment is heaviest in discovery and deployment and light in between.
What happens if the assessment says the idea will not work?
You get a report that says so, with the reasons and, where there is one, the reshaped version that would. For quantum questions this is the normal outcome today: most problems are better served by classical or quantum-inspired methods, and we document the comparison. A no-go report is cheaper than a stalled pilot, and we treat delivering one as a job done well.
Discuss your roadmap
Tell us where you are on the path, from an idea to a system that needs improving, and we will propose the next step and what it would take.