AI MVP Development for Series A CTOs: How SpeedMVPs Helps

Yes. We work within your repository, raise pull requests, follow your branch naming conventions, and wait for review before merge. We can adapt to whatever review process you have in place, whether that is a two-approval policy, a specific CI check requirement, or a particular code style enforced by your linting configuration. If your review cycles have a typical turnaround time that will affect the overall delivery timeline, we account for that in the project schedule upfront rather than treating it as a delay.

We define the evaluation approach as part of scoping, before we write any code. This includes specifying what good output looks like, what failure modes are unacceptable, and what quantitative threshold on the evaluation set is required before the feature is considered production-ready. We build a simple evaluation harness as part of the delivery so your team can run the same assessments when you update the model or change the prompt. This prevents the subjective arguments about whether the AI is good enough that often delay late-stage delivery.

Yes. SOC 2 readiness work is a separate engagement that we scope independently from your product feature work. We identify the specific control gaps, produce the required documentation, implement the access control and audit logging changes, and prepare the evidence collection process. This typically runs in parallel with your normal engineering activity without requiring your core team's involvement beyond an initial architecture review. We hand over the completed evidence pack and any implemented controls with full documentation for your internal team or your auditor.

Most of our engagements with Series A CTOs run for two to four weeks per module or capability. We scope each engagement independently so you are committing to a specific, bounded piece of work with a fixed price rather than an open-ended retainer. If you have a larger programme of work, we can sequence it as a series of fixed-scope engagements, each with its own delivery and handover, which gives you the flexibility to pause, reprioritise, or extend without being locked into a long-term contract.

We start with a full infrastructure review: current topology, spend by service, utilisation patterns, and configuration. We identify the highest-impact opportunities first, which typically include reserved capacity commitments for predictable workloads, rightsizing overprovisioned compute, eliminating unused storage and data transfer, and reviewing AI inference costs specifically if you are running models at scale. We produce a prioritised recommendation with estimated savings per item, then implement the changes with your approval and monitor the spend impact over the following fortnight.