[ prove it in weeks, not quarters ]

AI proof of concept services: AI POC and MVP development that de-risks your AI build

An AI proof of concept proves one use case works on your real data before you spend a full build budget finding out it does not. Describe the idea in plain language and Botgigs matches you to a vetted developer who scopes a tight, time-boxed POC or MVP, with a fixed success metric and an honest read on what production would take.

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[ short answer ]

AI POC development is a short, time-boxed build that proves whether an AI use case actually works on your data and delivers enough value to fund the full version, before you commit the full budget. A typical AI POC costs $10,000 to $150,000 and takes 4 to 12 weeks depending on scope, and almost all of that is engineering time rather than model usage, which usually stays under $1,000. It runs on a curated sample of your real data with a fixed success metric agreed up front. Expect production to cost roughly three to five times the POC, because that is where integration, security and messy live data get handled. Last updated July 2026.

01 / what you get

What a real AI POC delivers, not just a demo

A demo makes one happy path look good. A proof of concept tells you whether the idea survives your real data, your edge cases and a budget decision. These are the six things a well-scoped AI POC or MVP should hand back, so you leave with evidence rather than a nice screen recording.

[ use case ]

One sharply defined use case

A single question the POC exists to answer, not a grab bag of features. Narrow scope is what makes a POC fast, cheap and conclusive instead of a mini product that never ends.

[ real data ]

A run on your real data

A curated but genuine sample of your documents, tickets or records, so the result reflects your messiness, not a clean toy dataset that hides every problem waiting in production.

[ prototype ]

A working prototype to try

Something a stakeholder can actually use, not slides. A live prototype exposes the gap between what the model does in theory and what it does on the inputs your team sends it.

[ metric ]

A fixed success metric

The pass or fail bar agreed before the build starts: accuracy, deflection, hours saved or dollars. Without it, every POC ends in an argument about whether it worked.

[ blockers ]

The honest blockers

Data quality gaps, integration risks and compliance questions surfaced early, while they are cheap to fix, instead of discovered halfway through a production build.

[ estimate ]

A production estimate

A grounded read on what taking this to production would cost and how long it would take, so the go or no-go decision is made on numbers rather than optimism.

02 / the real risk

Most POCs die on the way to production. Design for that.

Roughly half of AI initiatives never reach production, and a large share of generative AI pilots are dropped after the proof of concept. The reason is almost never the model. It is that a POC runs on clean sample data with a dedicated team, and production has to handle live systems, messy inputs, security and drift. A POC that ignores that gap is why so many pilots stall. A good one plans for it from day one, which is the whole point of scoping it with someone who has crossed that line before.

~48%

of AI initiatives actually reach production, a transition that often spans months, not weeks.

3 to 5x

the POC cost is a realistic multiplier to reach a production-grade system on live data.

up to 30%

of generative AI projects get abandoned after the POC, usually over data quality or unclear value.

The takeaway is not to skip the POC. It is to run one that measures the right thing and names the production cost honestly, so you fund the builds that will survive and kill the ones that will not. If you want the full picture of where pilots stall and how to avoid it, read why most AI POCs stall before production, and measure the prototype the way you would evaluate any AI agent before you ship it.

03 / what it costs

POC cost and timeline by scope

The price of a proof of concept tracks how much of your world it has to touch. A self-contained prototype on sample data is cheap and fast; one that has to reach into several enterprise systems is neither. Typical 2026 US figures, not quotes.

POC scope	Typical US cost	Timeline
Simple chatbot / generative AI POC	$10,000 to $45,000	4 to 6 weeks
RAG / LLM application POC	$30,000 to $80,000	6 to 8 weeks
Enterprise-integrated agent POC	$75,000 to $150,000	8 to 12 weeks
POC to production (add-on)	3 to 5x the POC	Plus 3 to 6 months

The number that surprises people is how little of this is the model. Even at real evaluation volumes, API costs for a POC usually stay under $1,000; the budget is engineering time and data preparation. That is also why the honest production multiplier matters: a $40,000 POC that clears its bar can imply a $120,000 to $200,000 production build, and it is far better to know that before you start. If the POC proves out, the same developer can carry it into a custom AI solution or a fuller AI agent development engagement without a handoff.

04 / why botgigs

A POC scoped to answer, not to impress

01

Time-boxed and fixed-scope

A POC with a hard deadline and one question to answer, so it cannot quietly grow into a six-month project. You get a clear go or no-go, not an open-ended retainer.

02

Built by someone who ships production

The developer scoping your POC has taken AI systems live before, so the prototype is designed with the production gap in mind. Hire the generative AI developers who have crossed that line.

03

Measured against a real bar

A success metric agreed up front and tested on your data, so the result is evidence you can take to a budget owner, not a subjective demo everyone remembers differently.

04

A straight path to the full build

If the POC proves out, the same vetted developer can carry it forward into an AI development engagement, so nothing is thrown away and no context is lost.

05 / how it works

From an idea to proof, in a few weeks

step_01

Define the question

The one use case the POC exists to prove, the data it would run on, and the success metric that decides go or no-go. The AI turns that into scope and the specialist skills to screen for.

step_02

Get scope and matches

A vetted developer who has shipped this kind of system, with a time-boxed plan, a fixed cost band, and a production estimate in the same breath so there is no surprise later.

step_03

Build, test, decide

A working prototype on your real data, measured against the agreed bar. You leave with evidence and a costed production path, then fund the full build only if it clears.

06 / questions

AI POC development questions, answered

What is an AI proof of concept?

An AI proof of concept is a small, time-boxed build that answers one question: can this AI use case actually work on your data and deliver enough value to justify a full build. It runs on a curated sample of your real data, proves or disproves feasibility in a few weeks, and produces evidence you can take to a budget decision. It is deliberately not production software, so it skips the scaling, security and integration work a live system needs.

How long does an AI POC take?

Most AI proofs of concept take 4 to 12 weeks depending on scope. A simple chatbot or generative AI POC often finishes in 4 to 6 weeks, a retrieval or RAG application lands around 6 to 8 weeks, and an enterprise-integrated agent POC that touches several systems runs 8 to 12 weeks. The single biggest driver of the timeline is how ready your data is, not the model.

How much does an AI POC cost?

An AI POC typically costs $10,000 to $150,000 in 2026. A simple chatbot or generative AI POC runs $10,000 to $45,000, a RAG or LLM application POC $30,000 to $80,000, and an enterprise-integrated agent POC $75,000 to $150,000. Almost all of that is engineering time: even at real evaluation volumes, model API costs for a POC are usually under $1,000. Turning the POC into production commonly costs three to five times more.

What is the difference between a POC and an MVP?

A POC answers can it work, while an MVP is the smallest version real users can actually use. A proof of concept proves feasibility on sample data in a controlled setting and is often thrown away. An MVP is a lean but genuine product, wired into live systems with basic guardrails and monitoring, that ships to a small group of users to test value in the real world. Many teams run the POC first, then build the MVP only if the POC clears its success bar.

Why do AI POCs fail to reach production?

Because a POC runs on clean sample data with a dedicated team in a controlled environment, and production does not. Roughly half of AI initiatives never reach production, and a large share of generative AI pilots are abandoned after the POC. The gap is the unglamorous engineering a POC deliberately skips: live-system integration, messy real-world data, security and compliance, monitoring for drift, and sustained accuracy over time. The fix is to design the POC with production in mind and set a clear go or no-go bar before you start.

What should an AI POC include?

A useful AI POC includes a single, sharply defined use case, a sample of your real data, a working prototype that a stakeholder can try, and a fixed success metric agreed up front. It should also surface the honest blockers: data quality gaps, integration risks and the rough cost of taking it to production. A POC that only produces a slick demo, with no measurement and no production estimate, has told you almost nothing worth funding.

Should I build a POC or go straight to production?

Build the POC first unless the use case is genuinely proven and low-risk. A proof of concept costs a fraction of a production build and can save you from funding a system that was never going to work on your data. Skip it only when the pattern is well established, the data is clean and integrated already, and the downside of a false start is small. For anything novel, regulated or data-heavy, the POC is the cheapest insurance you will buy.