A 90-Day Playbook for Shipping Production AI Features

AISOVA Technologies published a guide for founders building AI. It details four common architectures, a 90-day rollout plan, and the hidden costs of evaluation and human review.

Shipping a production AI feature costs three to five times more in its first quarter than it does in a steady state. This claim comes from AISOVA Technologies, a consultancy that published a detailed guide to AI development. The budget inflation isn't from model inference. It comes from the unglamorous, often-underestimated work of evaluation infrastructure and human-in-the-loop review.

The guide provides a playbook for navigating this, based on a small set of repeatable architectures and a disciplined rollout process. It argues that most AI projects fail not because the model is wrong, but because the foundational engineering is incomplete.

The four production-ready architectures

The post identifies four patterns that it claims cover 90% of use cases. The advice is to select the simplest one that solves the business problem.

1. Prompted LLM with structured output: A single, constrained model call that returns a JSON schema. This is used for classification, extraction, and summarization tasks.
2. Retrieval-Augmented Generation (RAG): A vector store of proprietary documents is queried for relevant context, which is then fed to the model. This is the standard for question-answering over a knowledge base.
3. Tool-using agents: The model is given access to APIs, databases, or a browser to execute multi-step workflows like lead research or support ticket triage.
4. Fine-tuned small models: When API costs, latency, or privacy are primary constraints, a smaller open-weights model (3-8B parameters) can be trained on 5,000 to 50,000 high-quality examples to match larger model performance on a narrow task.

Budget for the hidden costs

Founders consistently under-budget two things: evaluation infrastructure and human review during rollout. AISOVA provides heuristics for estimating these costs. They recommend budgeting for at least 0.5 full-time equivalent employees for human review during the first 60 days of any new AI feature. Observability and tracing tools add another $200 to $2,000 per month.

Evaluation runs, where thousands of examples are re-graded after every prompt or model change, can often exceed the cost of production inference. This spending is what prevents regressions and ensures improvements are measurable, not just perceived.

Build the evaluation harness first

The guide’s most critical tactical advice is to build the evaluation system before building the feature itself. A robust harness prevents coin-flip development cycles where changes cannot be objectively measured. The components include a golden dataset of 200 to 2,000 real-world inputs with acceptable outputs, a suite of automated metrics, and a regression test that runs on every change. Without this, a team cannot reliably tell if a prompt tweak helped or hurt performance.

A 90-day shipping timeline

The playbook lays out a path from concept to internal beta.

• Days 1-15: The first two weeks are for workflow selection. Teams should audit five potential use cases and score them on human time consumed, tolerance for error, data availability, and the clarity of a success metric. The goal is to pick the highest-ratio candidate, not the most ambitious one.
• Days 16-45: The next month is dedicated to building the simplest possible version of the feature and its corresponding evaluation harness. The target is an internal beta, not a public release.

What We'd Change

The AISOVA playbook is a strong technical foundation. Its weakness is a lack of product and go-to-market context. The guide does not address how to communicate AI capabilities and limitations to users, a critical factor in adoption and trust. Setting user expectations for potential errors or variability is as important as the backend architecture.

The cost heuristic of "0.5 FTE for human review" is a useful starting point, but it lacks nuance. For high-stakes applications in fields like legal or medical tech, this figure is likely a significant underestimation. For low-risk internal tools, it may be an over-allocation. The cost of being wrong should dictate the investment in human oversight.

Finally, the source is a consultancy. The playbook is excellent content marketing that frames the problem in a way that highlights the need for expert guidance. Founders should recognize this. The advice is sound, but it is also a sales funnel for the firm's services.

Landing

The central lesson from the AISOVA guide is that production AI is a function of engineering discipline, not model access. The most successful teams are not necessarily those with the most advanced models. They are the ones who invest methodically in data quality, robust evaluation, and predictable rollout processes. The work that happens before and after the model call is what ultimately delivers business value.

The investor read

This playbook signals the maturation of the AI implementation market, shifting focus from speculative R&D to repeatable engineering. An investable AI-native company is no longer defined by access to a frontier model, but by its institutional capacity for rigorous evaluation, clear unit economics, and disciplined product rollout. Investors should probe for the existence and sophistication of a company's evaluation harness. The rise of consultancies like AISOVA also points to a growing 'picks and shovels' service economy, a secondary investment vector focused on providing the expertise required to de-risk AI implementation for incumbents and startups alike.

Pull quote: “Founders consistently under-budget two things: evaluation infrastructure and human review during rollout.”