AI Proof of Concept Development Services
Most AI ideas fail quietly, after months of budget and work. Kodexo Labs catches that early: we test your idea on your own data first and tell you plainly if it's worth building. It's the same approach behind 51 AI products now live and working for real businesses.
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TRUSTED BY ENTERPRISES
Kodexo Labs AI Proof of Concept service is part of our Agentic AI development services. This is where we find out whether an AI idea is worth building, before you spend real money on it. Whether you're picturing an AI agent that makes decisions on its own, or one that reads and organizes information for you, we test it with the same rigor as software already running in production. It's the same process behind 51 AI-powered products Kodexo Labs has shipped across 25+ industries.
Our Core Capabilities
We verify your data and use case are ready before any development begins.
We build an early working version using RAG (retrieval-augmented generation), so your software can search your own records and return accurate answers, not guesses.
We test the specific AI capability your use case needs, whether that's computer vision, language understanding, or prediction.
We build a prototype that extracts the exact information you need from your existing documents.
Every engagement ends with a written go or no-go decision, backed by measured results.
You get a working demo and real test data every week, not a single polished reveal at the end.
IN THE NEWS
AI-powered products across 25+ industries
Top-Rated AI Development Company
Client retention rate across the portfolio
recognized clients
Types of AI Proof of Concept We Build
AI ideas carry different risks depending on what they're built to do. A tool that answers questions from your own records fails differently than one that reads contracts or predicts demand. Each proof of concept below is built to test the specific risk your use case carries, before you commit a production budget.
Feasibility & Discovery Scoping
Every AI idea rests on an assumption: that your data can actually support it. We test that first, before any development starts. It's the same discipline behind 51 AI products we've shipped to production.
We agree on the goals, the data checks, and the tests before any work begins, not after.
If your data isn't ready, we tell you clearly before you commit any development time or budget.
Don't Guess. Test It First.
You have an AI idea, but you don't know yet if it will actually work for your business. We test it on your real information and give you a clear yes or no, before you spend money building it.
Real Proof of Concept Results Before the Build
AI Lab Report Automation
Staff typed lab results into patient plans by hand, six steps per report, with no way to catch errors. Kodexo Labs built a system that reads lab reports and drafts signature-ready plans, so doctors only review and sign.
98%
Test Names Auto-Matched
16/16
Report Layouts Recognized
0
Wrong Values Passed Through
Fairness Factor
Employees preparing for a performance review had no single place to plan, predict the outcome, or practice the conversation, and manual preparation took about four hours per review. We built an AI coach that guides preparation, scores the likelihood of a raise or promotion, and lets users rehearse. Its predictions land 80% of the time.
80%
Prediction Accuracy
30%
Negotiation Confidence Increase
35%
Performance Improvement
Nutrition Buddy
Planning meals around a person's calorie and nutrient goals is slow and easy to get wrong by hand. We built a proof of concept that works out daily calorie needs, builds meal plans from verified nutrition data, and warns users when a goal is unrealistic or unsafe, such as losing 20 kg in one month.
Weekly
Nutrition Data Update Checks
1,200 kcal
Daily Minimum Before a Plan Is Flagged
5 kg
Maximum Realistic Monthly Weight Change
What Clients Say About The Team
Fast-growing organisations do not applaud a consulting partner for polished slide presentations; they praise it for showing up when something actually breaks. The notes below come from founders who watched Kodexo Labs work the problem in real time.
Kodexo Labs has met all expectations; the team delivers on time and manages the project seamlessly. They respond promptly to needs and communicate effectively through virtual meetings, Google Chat, and WhatsApp. Overall, they're highly passionate about the project and excel in customer service.
Christopher Brigham
MD President, Brigham and Associates, Inc.
WATCH VIDEO
- HIPAA validated at prototypeSignal-monitoring feasibility testVital Connect detection proofDocumentation accuracy check
Where AI Proof of Concept Work Proves Its Value
The risk a proof of concept must retire changes by industry. A HIPAA gap is not a latency gap, and a lease clause is not a warehouse query. We scope each one against the risk that matters.
Scoping an AI Proof of Concept?
Tell us the use case and the data you have, and we will tell you what a PoC would need to prove.
Where Compliance Gets Tested During the Proof of Concept
A proof of concept is where compliance should surface, not the production build. We scope every prototype against the regulatory framework the finished system will face. A gap shows up in week one instead of week thirty, when fixing it costs real money.
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
PCI-DSS
PIPEDA
Zero-Trust Architecture
Data Residency Controls
SOC TYPE 2
ISO 27001
HIPAA
GDPR
CCPA
COPPA
PCI-DSS
PIPEDA
Zero-Trust Architecture
Data Residency Controls
Why Teams Choose Kodexo Labs for AI Proof of Concept Work
Many vendors can build a quick demo. Far fewer can show you, with real test results, whether an AI idea will work once customers start using it. Kodexo Labs has 51 AI products in production, and these four strengths shape how we test yours.
Compliance Proven Week One
Regulated work fails late when compliance is a phase-two problem. We test it first. For Therapy Talk, we validated GDPR architecture at the proof of concept stage, before the platform reached 1,923 users. The compliance gap surfaced in week one, not at launch, when a fix costs most, with 93% response accuracy holding steady throughout.
Wrong Approaches Killed Cheaply
Committing to the wrong architecture early gets expensive once a team has already built around it. We test candidate vector stores, FAISS, Pinecone, and Qdrant, against your real latency and strict data-residency rules, on our production LangGraph stack. That same disciplined testing runs behind every recommendation.
Validated Before You Commit
An idea that fails in production costs more than an honest no. For Angel Therapy, we validated an AI documentation approach against real sessions before any full build. That approach went on to cut note-writing time by more than 90%. That is what a proof of concept is for: evidence, not optimism. We prove first.
Speed That Compounds
Every delay costs budget. The faster you know an approach works, the faster it earns its production budget. For Dynasty Pulse, we validated a real-time data pipeline that cut latency from 15 minutes to 30 seconds, a 98% reduction. We scope your proof of concept to reach exactly that kind of clarity just as quickly.
Thinking About Scoping an AI Proof of Concept Right Now?
You have a use case and some data. You need to know if it is real before funding a build. Tell us both, and we will scope what a proof of concept must prove.
Recognised By The Platforms That Vet AI Companies
Kodexo Labs is reviewed where technical buyers do their diligence: Clutch and Upwork. Every badge below links to the live profile.
Overcoming AI Proof of Concept Challenges
Most proof of concept work fails for reasons that have nothing to do with the model. It fails on data, on scope, and on the quiet gap between a demo and production. These are the three failure modes we prevent.
The Tech Stack Behind POC That Ships
These are the tools we actually use in production healthcare builds. Not a marketing capability list, just the stack our engineers reach for on day one of a new project.
How We Run Your Proof of Concept
Five phases take a use case from an open question to a documented production decision, with visible progress every single week.
Feasibility and Scoping
Before any code, we lock the success criteria the proof of concept must clear: an accuracy threshold, a latency target, and an integration test. Then we check data readiness against your real live systems.
Architecture and Selection
Next we lock the architecture family, agentic, applied, or a hybrid, choosing an approach such as LangGraph for stateful designs. We present the proposed design and scope for your sign-off, and confirm the compliance requirements production will face.
Working Prototype Build
We build the working prototype against your real data, with weekly demos so progress is visible every cycle, not just at the end. It is instrumented from day one, so the results are measured.
Validation and Decision
We test the prototype against the Phase 1 criteria for accuracy, latency, and integration feasibility. Then we make the explicit go or no-go call, backed by results, and document what passed and what did not.
Handoff to Production
If the answer is go, we hand the engineering team a validated architecture and a production roadmap. The handoff documents what was tested, what passed, and what a full production build will require next.
Insights From The Kodexo Labs Team
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Frequently Asked Questions About AI Proof of Concept Development
Kodexo Labs starts every AI proof of concept by defining success first. That means an accuracy threshold, a latency target, and proof the approach integrates with real systems. Only then does the team select an architecture and build against real data. That criteria-first discipline, not a polished demo, is what makes a proof of concept trustworthy enough to guide a production decision.