AI Frameworks Development Services
AI Frameworks and Platform Integration services configure LangChain, LlamaIndex, vector databases, and orchestration layers into the technical backbone moving prototypes to production. Framework choices made at demo stage rarely survive production load. Kodexo Labs is the integrator making AI frameworks production-grade, with observability wired in from sprint one.
TRUSTED BY ENTERPRISES
AI frameworks development at Kodexo Labs means LangChain, LangGraph, and CrewAI builds engineered for production traffic, not sandbox demos that stall the moment real users hit the endpoint. Most framework vendors ship thin wrappers that look clean in a Loom video and break the first time enterprise data complexity meets real concurrency. The Kodexo Labs team builds the orchestration, retrieval, and observability layers together, so the system holds under audit, integration, and load. For Extensiv, a $130M-funded Inc. 5000 logistics platform, the LangGraph build queries 207 database tables across 4 databases at 90%+ accuracy in plain English.
Our Core Capabilities:
Build LangChain pipelines that move data, tools, and reasoning into one production stack.
Orchestrate LangGraph multi-agent systems that handle stateful workflows across enterprise databases.
Deploy CrewAI role-based agent teams that coordinate research, drafting, and review tasks at scale.
Engineer RAG pipelines that retrieve answers from millions of records with audit-grade accuracy.
Fine-tune LLMs on proprietary data so the model speaks the language of the business.
Wire production monitoring and observability into every agent so failures surface before users see them.
IN THE NEWS
Proof That The Framework Work Ships And Stays Shipped
51 across 25+ industries
Top-Rated · Verified on Clutch
60+ engineers across 6 offices
94% across all engagements
Teacher AI · 50,000+ users · $5M+ revenue
2021 · Agile sprints · weekly demos
Pick The Framework. Kodexo Labs Builds The Production System
Eight framework practices, one delivery team, and deep experience shipping AI systems into production. Every implementation is engineered for observability, reliability, and scale from the first sprint onward.
LangChain Development Services
IFPG's franchise advisory chatbot improved from broken answers across 1,000+ listings to 85% accuracy after Kodexo Labs rebuilt its reasoning layer in LangChain. Operations teams now use production-ready pipelines that retrieve data, call tools, and return reliable answers at scale.
Chains coordinate search, calculations, and actions while preserving context across workflows.
Business users query internal systems naturally and receive traceable, auditable answers.
Framework Work That Survives The Production Line
The Kodexo Labs team ships frameworks built for traffic, audit, and the buyer's compliance officer, not the demo room.
Named Clients, Production Framework Outcomes
Teacher AI - Edtech Platform
Personalised tutoring had never scaled affordably. Kodexo Labs built Teacher AI to give every student a tutor in their native language, on demand. The in-house product now generates $5M+ in revenue.
50,000+
Users
30+
Countries
$5M+
Revenue
IFPG
IFPG's chatbot returned HTML-broken, inaccurate answers to every prospect, killing leads at first contact. Kodexo Labs rebuilt the reasoning layer with chain-of-thought prompting and eliminated all HTML errors.
85%
Accuracy Lift
100%
HTML Error Elimination
1000+
Franchise Listings
Therapy Talk
A mental-health platform launching into the EU needed GDPR architected from day one. Kodexo Labs built a privacy-first multi-agent framework routing inference through on-premise endpoints.
1923
Active Users
93%
Response Accuracy
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
Which Industries Run On AI Framework Builds From Kodexo Labs
A VP of engineering at a regulated mid-market company is weighing whether a generic LangChain demo can survive HIPAA, GDPR, or PCI-DSS production audit on launch day. The eight industry builds below each shipped under a different compliance regime, on a different framework stack, for a buyer who needed the agent to clear audit before sprint one closed.
- GDPR-Compliant Conversational AI FrameworkOn-Premise LLM Inference for PHILangSmith Audit Trail for Clinical ReviewHIPAA-Ready Multi-Agent Architecture
Compliance Starts Before Sprint One, Not After Launch
Framework-level data residency, access control, and audit trails are decided in the architecture phase. Production AI that meets HIPAA, GDPR, or SOC 2 starts at the framework choice.
AI framework builds that clear HIPAA, GDPR, and SOC 2 from sprint one.
A healthcare VP scoping a multi-agent platform for EU patients cannot ship a build that retrofits GDPR after the model goes live. Framework-level data residency, audit trails, and role-based access control are sprint-one decisions, not a post-launch compliance ticket.
Why engineering teams choose Kodexo Labs for AI framework builds.
A CTO comparing AI framework partners is weighing three risks: shallow framework knowledge, observability bolted on after launch, and vendor lock-in baked into the stack. The three differentiators below speak to each one.
Production-grade LangGraph, CrewAI, and AutoGen, not a GPT-4o wrapper.
LangGraph, CrewAI, AutoGen, and MCP run in active production builds at Kodexo Labs, not in a sandbox demo. Each framework is selected for the client's specific use case before sprint one opens, so the stack matches the problem instead of the marketing deck.
The build ships with observability wired in, not bolted on after a production incident.
AgentOps and LangSmith are part of the sprint-one architecture, not post-launch additions. Production drift, agent hallucination, and latency spikes are caught in staging by the monitoring layer, not by a client email at 11pm on a Friday.
The model layer is not locked to one provider.
GPT-4o, Claude 3.5 Sonnet, Llama 3, and Mistral run on a single orchestration layer at Kodexo Labs. Model swaps do not require a framework rebuild, because provider lock-in is an architecture failure, not a vendor constraint to design around.
Our Agentic AI Systems Are Shipping Daily with Inc. 5000 Clients, Major Logistics Operators, and Patent-Pending Healthcare Providers Today
Three clients, three industries, three agentic systems running today: Diesel Laptops across fleet diagnostics, Extensiv across logistics data, SmartMedHx across healthcare documentation.
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.
Day-one production stack for every placement
A VP of Engineering needs the production tool stack, not a marketing taxonomy.
Where AI Framework Builds Fail, and How Kodexo Labs Engineers Around It
A VP of Engineering at a regulated mid-market platform has watched two prior AI pilots stall in production and needs the next build to ship with failure modes designed out from sprint one. The three cards below name the risks that kill framework projects most often, and the architecture shipped against each one.
Framework lock-in strands your AI investment when a vendor shifts pricing or strategy.
Dynasty Pulse's data pipeline cut processing time from 15 minutes to 30 seconds, a 98% reduction, built on portable open-source components the client owns outright, not a black-box vendor stack. No single-vendor framework, no proprietary runtime, no walled-garden tooling that locks rebuild cost in if pricing shifts.
A framework with no observability layer is a production incident waiting to surface.
Every Kodexo Labs build ships with AgentOps and LangSmith wired in from sprint one. Extensiv runs 50+ agents under real-time tracing across 207 tables and 4 databases. The AWS VPC self-hosted option keeps client data inside client infrastructure.
Compliance gaps in the framework layer become regulatory exposure at scale.
Zendrop's commerce automation was architected with data isolation and audit logging baked into the framework layer before the first transaction ran. The same build delivered 45% faster time-to-market, 50% lower launch costs, and a 25% conversion lift, because compliance plumbing did not slow the velocity work.
Five phases, each producing something you verify
Every Kodexo Labs custom software build runs through the same five phases, each ending with a working deliverable you verify.
Discovery Sprint
The discovery sprint defines what gets delivered, why it gets built in that order, and what production success looks like for the organisation. User-story mapping and acceptance criteria are locked during this phase, so every subsequent sprint carries a measurable, agreed output the team can verify.
Architecture and UI/UX Design
Architecture decisions covering the data model, API boundaries, microservices versus monolith, and cloud hosting choice are finalised before any UI/UX work begins. The design system is then built in Figma and handed to Storybook, with the architecture and data model fully documented before coding starts.
Sprint Development
Development runs in two-week sprints, using Node.js or Python on the backend and React or Next.js on the frontend, with Docker containers and GitHub Actions CI/CD wired in from sprint one, replacing monthly status calls with weekly working demos of tested code in the production branch.
QA and Security Hardening
Quality assurance runs in parallel with active development, never as a final gate bolted on at the end. Security hardening applies OWASP Top 10 controls, penetration testing patterns, and HIPAA and GDPR validation where required, integrated into the development lifecycle from commit one.
Deploy and Iterate
Deployment uses Kubernetes on AWS or GCP, Cloudflare for CDN and DDoS protection, and Sentry for real-time error monitoring from minute one of production. Post-launch iteration then continues on the same sprint cadence, never as a separate retainer renegotiation, with a deployment plan agreed first.
Insights From The Kodexo Labs Team
Top 15 Artificial Intelligence Applications List 2026
June 2026 · By Mohammad Ahmed Rajput
A guide to the top 15 AI applications of 2026, covering AI industrial applications and the best open-source artificial intelligence tools across industries.
AI in Adaptive Learning: Benefits, Challenges, and Best Practices for 2024
October 2024 · By Mohammad Ahmed Rajput
A practical guide to AI in adaptive learning, covering benefits, challenges, platforms, ROI, and best practices for personalized education in 2024.
What is Neural Network – The Future of AI in Businesses Defined
January 2024 · By Mohammad Ahmed Rajput
Neural networks are a core component of AI and deep learning, enabling machines to process data, recognize patterns and make decisions.
Frequently Asked Questions
LangGraph is a stateful multi-agent workflow framework with loops and branching. Kodexo Labs built Extensiv's operations agent on it: 50+ agents query 207 tables across 4 databases at 90%+ accuracy, turning a 14-hour ops question into a 22-minute answer.