Kodexo Labs

Profile set up by Edverise AI

Set Up By Edverise AI

Edverise AI built this profile by analyzing thousands of data points across the web. If you own this business, become an Edverise partner to manage and update your profile.

Become an Edverise Partner

Overview

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

50 - 249

Employees

2021

Year Founded

ABOUT KODEXO LABS

Your AI-Native Development Partner

Your Partner in AI-Native Development. Kodexo Labs specializes in creating web applications, mobile applications, and advanced AI systems that are deployed to production and remain operational. We have successfully launched 51 products and hold a rating of 4.9/5 on Clutch based on 13 verified reviews. Our solutions serve over 50,000 users in more than 30 countries, with a client retention rate of 94%. Our offerings include: • AI agents and autonomous systems that streamline complex multi-step workflows • RAG-powered search and knowledge systems designed for handling large volumes of structured data • Generative AI solutions, including LLM fine-tuning, voice AI, and intelligent tutoring • Comprehensive web and mobile applications developed with AI considerations integrated at all levels • Tailored software solutions for sectors such as Healthcare, EdTech, Fintech, E-Commerce, and Automotive. Demonstrated results include: • 85% faster search capabilities for Diesel Laptops (Inc. 5000) using a self-hosted AI solution across over 160,000 repair records • 90%+ accuracy in SQL for Extensiv (Inc. 5000) involving 207 tables and 4 databases • Over $1.5 million in first-year revenue from Teacher AI, catering to more than 50,000 users in 30+ countries. We don’t just integrate AI into existing systems; we incorporate it from the ground up, from architecture through to production. With a team of more than 60 engineers and PhD-level AI expertise, we are based in Austin (HQ) and have additional locations in NYC, SF, Chicago, London, and Karachi. Established in 2021.

Tech Stack

PythonTypescriptReduxJavaScriptKotlinDjangoNodeJSReactNextJSReact NativeAngularFlutter+2 more

Languages Spoken

English

HIGHLIGHTS

$10,000+

Min Project Size

$50 - $99/hr

Avg Hourly Rate

50 - 249

Employees

2021

Year Founded

Services

Focus

Industries

Clients

Services Provided

33% Custom Web Application Development

18% Mobile App Development (iOS & Android)

12% AI Model Fine-Tuning & Training

5% Custom AI Chatbot Development

15% Generative AI

2% AI Strategy & Roadmap Consulting

15% LLM Integration & Prompt Engineering

About the Team

OUR STORY

Our Journey Began in 2021 with engineers who create and deliver, rather than consultants who merely offer advice and depart. We kicked off our journey on Upwork, demonstrating our capabilities alongside the top teams globally. This experience solidified our fundamental belief: AI falters not during prototyping, but in the transition from demonstration to production. Our mission is to bridge that divide. We have successfully launched 51 products, earned a 4.9/5 rating on Clutch, and grown to over 60 engineers in 6 locations, all under expert AI leadership with PhDs. Our client retention rate stands at 94%. We don't simply integrate AI; we develop with it.

Portfolio

Natural Language Query for Warehouse Data

Extensiv

An AI-driven natural language interface allows warehouse operators to access over 130 database tables using plain English through Text-to-SQL and knowledge retrieval. This feature is part of Extensiv, a leading cloud-based warehouse management system (WMS) utilized by more than 1,500 third-party logistics (3PL) providers. **Challenges and Solutions:** Previously, 207 tables across four SQL Server databases needed SQL expertise for data access, leading to coordination problems within a fragmented multi-agent system, with query latencies of 5-8 seconds and around 75% accuracy. Now, users can make queries in plain English via a unified LangGraph orchestrator, supported by an eight-stage validation pipeline, achieving over 90% accuracy and reducing query latency to 3-5 seconds, allowing non-technical users to access data directly. **Key Outcomes:** Management of 207 tables across four databases has resulted in more than 90% accuracy in SQL queries (up from approximately 75%), and over 85% accuracy in retrieval-augmented generation (RAG), which is an improvement from 70%. The average query latency has decreased from 5-8 seconds to 3-5 seconds, with the 95th percentile latency being below 5 seconds. A projected reduction of 30-40% in support tickets is expected, and hybrid search methods outperform vector-only searches by over 10%. There is no risk of cross-tenant data leakage, with a mean time to detect (MTTD) incidents under 5 minutes and a mean time to resolve (MTTR) under 30 minutes. The solution has received a perfect 5-star client rating (5.0/5.0 across all categories). **Technology Stack:** The solution is built using Python, LangGraph, LangChain, Claude Sonnet 4 (the primary language model), OpenAI GPT-4 (as a fallback), Qdrant (vector database for SQL examples and document embeddings), PostgreSQL (for schema registry and metadata storage), Redis (for caching), FastAPI (as the API gateway), Microsoft SQL Server (for client databases), OpenAI text-embedding-3-large, NeMo Guardrails (for security), and monitoring tools like Prometheus and Grafana, along with OpenTelemetry for distributed tracing.

AI Chatbot for Franchise Broker Network

IFPG

Franchise chatbot optimization has led to the complete removal of HTML errors and an 85% enhancement in accuracy. Challenge & Solution: Addressing broken HTML and incorrect matching resulted in no errors and an 85% increase in accuracy. Key Outcomes: Complete elimination of HTML errors, an 85% boost in accuracy, a 70% decrease in ambiguity, and over 1,000 franchise listings processed. Technology Used: PHP, JSON Preprocessing, Chain of Thought Prompting, Few-Shot Learning.

AI Diagnostic Search for Fleet Repair

Diesel Laptops

An AI-driven diagnostic search engine has decreased the time needed for fleet repair searches by 85% across over 160,000 repair records. The challenge involved hours of manual searching, which was transformed into a sub-second AI-based search process. Key achievements include an 85% reduction in search time, indexing a database with 160,000 rows, deploying on AWS VPC, and achieving sub-second query responses. The technology stack used comprises Python, FastAPI, AWS VPC, a RAG Pipeline, and a Vector Database.

16 more projects