AI MVPs and LLM features that hold up in production. Evaluation sets, guardrails and human review built in. Fixed price from $8,000, delivered in 30 days or less.
Most AI products look good in a demo and fall over when real users send real inputs: a scanned PDF at an angle, a question the documents do not answer, a customer who writes three languages in one message. minidev.pro builds AI features that are measured against your real examples before launch, say "I don't know" when they cannot verify an answer, and hand uncertain cases to a person instead of guessing.
You get a working product, not a proof of concept: sign-up, your core AI workflow, a database, payments or a waitlist, an admin view and production hosting, built by the senior developers you talk to on the first call.
LLM features inside real products, aimed at one narrow job each. Narrow jobs can be tested, priced and trusted; "an AI that does everything" cannot.
Retrieval over your documents, help center or records, with answers that cite the passages they came from and decline when the sources do not cover the question.
Pull structured fields and tables out of PDFs, forms, invoices and reports into your database, with each value checked and anything unverifiable flagged rather than filled in.
Summaries of calls, tickets, case notes or long reports in a fixed format your team agrees on, linked back to the source so anyone can check them.
Tag, prioritize and route incoming emails, tickets, leads or applications, with a confidence threshold below which a person decides.
Multi-step tasks with a small set of tools and clear limits, such as drafting a reply from the CRM and the knowledge base for a human to approve. No open-ended autonomy over money or customer accounts.
When the AI is the product itself, we build the whole thing around it: onboarding, billing, usage limits, admin and the review tools your team needs to run it.
This is the part generic AI development companies leave out of the proposal, and the part that decides whether the feature survives contact with users.
Real examples from your business with the correct answers, built in week 1. Every prompt change and model swap is re-run against it, so "it seems better" becomes a number.
Structured outputs validated against a schema, rules the answer must satisfy, limits on what the model can see and do, and refusal when the sources do not support an answer.
Low-confidence or high-stakes results land in a queue for a person to approve, correct or reject. Corrections feed back into the evaluation set.
Per-user and per-account usage limits, caching of repeated context, the smallest model that passes the evaluations, and alerts before a bill surprises you.
Every request, the context it was given, the output and the cost, stored so you can debug a bad answer, audit a decision and see what users actually ask.
If the provider is down, slow or unsure, the product still works: a manual route, a queued retry or a plain "we'll get back to you", never a blank screen.
For a UK geoenvironmental practice: extracts contamination results from four UK lab PDF formats where commercial table parsers failed. pdf.js column clustering, a dual-parser cross-check, 148 of 148 gold cells correct, and it refuses to guess a value it cannot verify. The same fail-closed principle runs through every AI feature we build.
An AI-powered consultation product in health and wellness. We built the MVP, and the client came back for ongoing product work.
Extraction and evidence software for regulated files, where a wrong number is worse than no number. Parsers fail closed instead of guessing, and we extract only what is on the page.
Two costs: a fixed build price agreed before we start, and model usage you pay the provider directly as the product is used.
| Scope | What it looks like | Build price |
|---|---|---|
| AI product MVP | The AI is the product: the $5,000 core MVP (sign-up, one core workflow, database, Stripe or waitlist, admin, landing page, deployment) plus the AI work with its evaluation set, guardrails, review queue and logging | From $8,000 |
| MVP with AI features | A normal app with one or two AI features on top, such as summaries, smart search or auto-tagging | From $7,500 |
| AI in an existing product | A defined feature added to your codebase, or ongoing AI work on the SaaS Growth Partner retainer | Scoped on a call, or from $5,000/month |
| Model usage | Per-token charges from OpenAI, Anthropic or another provider, billed to your own account | Pay as you go |
Other add-ons, such as subscription billing, file processing or compliance features, are priced per feature. Payment is 50% upfront and 50% on delivery. Price your exact scope with the MVP cost calculator →
Model providers charge per token, roughly three-quarters of an English word. You pay for the input tokens you send (instructions, retrieved documents, the user's message) and, at a higher rate, for the output tokens the model writes back. Two current list prices, as of October 2026:
| Model | Input, per million tokens | Output, per million tokens |
|---|---|---|
| Anthropic Claude Sonnet 5.5 | $2.00 | $10.00 |
| OpenAI gpt-5.4-mini | $0.75 | $4.50 |
An illustrative month for a modest product: 10,000 requests, each sending about 3,000 input tokens (instructions plus retrieved context) and getting about 500 output tokens back. That is 30 million input tokens and 5 million output tokens.
| Model | Input cost | Output cost | Month total |
|---|---|---|---|
| Claude Sonnet 5.5 | 30M × $2/M = $60 | 5M × $10/M = $50 | About $110 |
| gpt-5.4-mini | 30M × $0.75/M = $22.50 | 5M × $4.50/M = $22.50 | About $45 |
These figures are illustrative, not a quote. Prompt caching and batch processing can cut them; agents that loop through several calls per task, long documents and retries push them up. We estimate usage for your product during scoping and set limits so a spike cannot run away with your budget. For a fuller breakdown of build and running costs, read how much an AI MVP costs.
We collect 30 to 100 real examples of the task from you, agree the correct output for each, and turn them into the evaluation set. Then a signed Must / Later / Never scope, core screen designs and a deployed skeleton app.
The AI workflow end to end on staging: retrieval or extraction, prompts, schemas and guardrails, scored against the evaluation set. Model choice is settled on the scores, not on hype.
Human review queue, logging, usage limits and the fallback path, plus payments, email, admin and the landing page.
A final evaluation run, testing with awkward real inputs, production launch and a recorded handover, including how to re-run the evaluations yourself. 30 days of post-launch fixes start.
The delivery date is in the contract. If we miss it without a scope change on your side, you get a full refund of fees paid plus $2,000.
We will tell you on the first call if a feature should not be AI. It saves you money and it is usually safer.
If the logic can be written as rules (tax bands, eligibility criteria, routing by postcode, a fixed form layout), deterministic code is cheaper per request, faster, testable line by line and never invents an answer.
Credit, hiring, medical, legal and safeguarding decisions need a qualified person to sign off. AI can draft, sort and summarize for that person; it should not be the one deciding.
If a single error has a serious cost and there is no human check, use rules, or design the AI step so a person approves every output.
If you cannot show us real inputs and what a good output looks like, there is nothing to evaluate against. Start with a manual process in the MVP and add AI once you have the data.
You are a startup or small team with a specific job for AI inside a product, real examples of that job, and you want it live with users within a month at a fixed price.
You need a model trained from scratch, research into new model architectures, on-premise GPU infrastructure, or a regulatory certification process before anyone can use the product. A specialist ML research team or a larger consultancy is the better choice.
At minidev.pro, an MVP where AI is the core of the product starts at $8,000 fixed: the $5,000 web MVP plus $3,000 for the AI work. Adding AI features to an otherwise normal app is $2,500 on top of the MVP. Model usage is separate and paid to the provider on your own account; in our illustrative example of 10,000 requests a month it comes to roughly $45 to $110, but it depends entirely on volume and model choice.
30 days or less for a focused AI MVP, with the delivery date written into the contract. The AI add-on adds about three build days, which the agreed date already accounts for. Week 1 includes building the evaluation set from your real examples, so we know what good looks like before the AI is built.
The cheapest model that passes your evaluation set. We usually start with a mid-tier model from OpenAI or Anthropic, test a smaller one against the same examples, and keep the model behind one interface so it can be swapped later. If a feature needs a larger model to pass, we use it and show you the cost difference.
You cannot make a language model incapable of being wrong, so we design the product around that. Answers are grounded in your own documents and cite them, outputs are checked against rules and schemas, low-confidence results go to a human review queue, and when the system cannot verify an answer it says so instead of guessing. An evaluation set built from real examples is re-run before every release.
You do. Once the final payment is made, all custom code, prompts, evaluation sets and documentation are yours. The model provider account is set up in your name, so your data, usage and bills stay with you, and we configure the provider's API rather than consumer chat apps.
Yes. We start by reading the codebase and collecting real examples of the task, then add the feature behind a flag with logging and a fallback path. A single well-defined feature can be scoped as a fixed-price project; ongoing AI work fits our SaaS Growth Partner retainer, from $5,000 a month.
Model prices checked as of October 2026. Providers change prices often, so check the current pages before budgeting.
Bring ten real examples to the call. Fixed from $8,000, live in 30 days or less.
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