01
Question framing
Define the technical and business uncertainty the POC must resolve.
AI proof of concept
AI proof of concept services that answer the question behind the idea.
Evidence-led prototypes that answer the question behind the AI idea.
We build the smallest credible demonstration of value, technical feasibility and risk - so the next investment decision is based on evidence.
Evidence over assumptionsKnown constraintsDecision-ready demoClear next phase
BYOND BOUNDRYS CONSULTINGApplied AI
Capability focusAI Proof of Concept
01Decision question05Test stages0Hidden assumptions
Why this matters
What a useful AI proof of concept should prove.
A POC should not be a polished demo with the hard parts hidden. It should test the data, workflow, model behaviour and acceptance criteria that determine whether a real pilot is worth funding.
What we build
What our AI PoC development services include.
We align experience, intelligence, data and operations around the job your team needs to complete.
02
Representative data slice
Use realistic examples without overbuilding the production data platform.
03
Working interaction
Create a small but credible workflow users can experience and review.
04
Evidence and recommendation
Document what worked, what failed and what production would require.
How we make it dependable
How we keep an AI PoC honest.
01Test the uncertainty that changes the investment decision.
02Use a realistic slice without pretending it is production.
03Show limitations as clearly as the happy path.
04End with a recommendation, not only a demo.
How it comes together
How we run an AI proof of concept.
The sequence keeps scope, evidence and ownership clear from the first conversation to the next release.
- 01Question
Frame the business and technical uncertainty.
- 02Slice
Select data and the smallest credible path.
- 03Build
Create a working interaction stakeholders can test.
- 04Decide
Turn evidence into a funded next step or a stop.
Delivery workflowDefine the decision → Select evidence and data → Build the critical path → Test with stakeholders → Recommend pilot, revise or stop
What you receive
What you receive at the end of the PoC.
Every engagement is shaped around practical outputs that help your team make a decision, start a build or operate the next version.
01POC brief and acceptance criteria
02Working prototype
03Representative test set
04Findings and limitations
05Pilot recommendation and backlog
Technology layer
Technology we use to build AI PoCs.
We choose tools for fit, control and maintainability - not because a logo is fashionable.
Python
FastAPI
React
Azure OpenAI (GPT-4o)
Google Gemini Flash
PandasPydantic
Docker
FAISSFAQ
AI proof of concept: common questions
What is an AI proof of concept?
An AI proof of concept (PoC) is the smallest credible build that tests whether an AI idea works on your real data and workflow. It answers a specific business or technical question, such as extraction accuracy, answer quality or feasibility, before you invest in a pilot or production system.
How is an AI PoC different from a demo or a pilot?
A demo shows the happy path. A PoC tests the hard parts: representative data, edge cases, model behaviour and agreed acceptance criteria. A pilot then puts a validated solution in front of real users. Our PoCs end with a clear recommendation to pilot, revise or stop.
Which companies offer AI proof-of-concept services?
AI PoC services are offered by specialist AI consultancies, cloud-provider partners and larger systems integrators. Look for a partner that agrees acceptance criteria before building, tests on representative data and documents limitations openly. Byond Boundrys is an OpenAI Select Partner and ISO 9001:2015 certified, and builds PoCs for RAG, document intelligence, agents and custom AI applications.
What do you need from us to start a PoC?
A clearly stated business question, a representative slice of data or documents, access to the people who own the workflow, and agreement on what success looks like. We help frame the question and acceptance criteria if they are not yet defined.
What do we receive at the end?
A PoC brief with acceptance criteria, a working prototype stakeholders can test, a representative test set, documented findings and limitations, and a pilot recommendation with a prioritised backlog.
What kinds of AI PoCs do you build?
Common examples include new AI workflow validation, document extraction feasibility, RAG answer-quality tests and executive demonstrations backed by evidence, using tools such as Python, FastAPI, React, Azure OpenAI, Google Gemini and FAISS.
Ready for the next step?
Bring us the workflow behind the requirement.
We can help shape the first release, architecture and evidence needed to move forward.