AI Proof of Concept Development Services
Committing a production budget to an unproven AI idea is how good teams lose an entire quarter. An AI proof of concept from Kodexo Labs tests the approach against real client data first. It returns a clear, documented go or no-go production decision.
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TRUSTED BY ENTERPRISES
Part of our Agentic AI Development Services practice, this is where we prove an idea is worth building. We scope agentic, generative, and applied AI use cases with the discipline that production demands.
Our Core Capabilities:
Feasibility and data-readiness scoping before a single production line ships
Agentic and RAG prototypes that let software reason over your own data
Applied AI prototypes for computer vision, language, and prediction tasks
Documentation and data-extraction proof of concepts on real client files
Production-readiness evaluation with a written go or no-go production call
Sprint-based delivery with weekly demos and fully instrumented test results
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AI-powered products across 25+ industries
Top-Rated AI Development Company
Client retention rate across the portfolio
In 2021, Agile sprints with weekly client demos
AI Proof of Concept Development Capabilities
Every engagement answers one question: will this AI approach actually hold up in production? We scope five kinds of proof of concept, each built to test a different risk before the client commits any real budget.
Feasibility & Discovery Scoping
Before any code, we pin down what success must look like and whether the client's real data and systems can actually support it.
We set the goals, the data check, and the tests up front, not later.
If the data is not ready, we tell you before you spend a sprint.
Most AI Proof of Concepts Never Reach Production
Too many pilots die as demos that never survive real data. We scope yours against the production question, then bring the use case.
Validation Before the Build
Diesel Laptops
Fleet technicians were losing hours to manual searches across 160,000+ repair records, and that proprietary data could never leave their own infrastructure. Before building, we validated a self-hosted retrieval approach inside an AWS VPC. Lookup time dropped 85%, data residency held, and this Inc. 5000 company kept full custody of all its repair data.
160,000+
Repair Records
85%
Faster Lookup
AWS
Self-Hosted Infrastructure
SmartMedHx
Clinical teams lost nearly an hour daily per provider to note-taking. We built a HIPAA-compliant RAG chatbot using LangGraph, interviewing patients, generating charts, and securing PHI within AWS VPC.
85%
Search Time Reduction
160,000+
Repair Records Indexed
12 Weeks
Build to Production
Extensiv (Inc. 5000)
Extensiv teams waited days for engineer-driven data answers, slowing decisions. We built a LangGraph agent that interprets questions, queries operational databases, and delivers grounded insights in plain English.
90%+
SQL Accuracy
207
Tables
04
Databases
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,
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.
Why Teams Choose Kodexo Labs to Run Their AI Proof of Concept Work
Plenty of vendors can spin up a demo. Fewer can tell you, with test results, whether it survives production. Here is what separates a Kodexo Labs proof of concept from a slide deck that stalls.
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.
Every tool listed is in active production on a Kodexo Labs.
Every framework, runtime, and cloud service named here is running on a live client product right now. No theoretical stack, no resume keywords, no tools added for marketing weight.
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 (Days 1-3)
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 (Days 4-5)
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 (Weeks 2-3)
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 (Week 4)
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 (Week 5+)
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
Agentic AI Applications, Benefits and Challenges in Healthcare
August 2025 · By Aruba Yousuf
A comprehensive guide to agentic AI applications in healthcare for 2025, covering benefits, challenges, technical infrastructure, leading platforms, and implementation best practices.
7 Promising Agentic AI Use Cases with Real-World Business Examples for 2025
August 2025 · By Mohammad Ahmed Rajput
Explore 7 promising agentic AI use cases for 2025, including autonomous customer support, supply chain optimization, and personalized retail experiences, with real-world examples demonstrating 20-60% efficiency gains and ROI within 6-18 months across healthcare, sales, retail, and more.
Agentic AI vs. Generative AI: Key Differences and How to Choose the Right One in 2025
July 2025 · By Mohammad Ahmed Rajput
Explore the key differences between Agentic AI and Generative AI in 2025, focusing on autonomy, decision-making, and content creation. This guide covers their characteristics, use cases, and decision frameworks for businesses aiming to optimize operations or creative workflows.
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.