Hire Offshore Developers
Hire dedicated offshore developers who integrate into your sprint cadence, ship production-ready code from week one, and remain on your team long-term, reporting into your standups, using your established toolchain, and committing to your roadmap quarter after quarter.
Our Machine Learning Development Services, Built for Production
Reinforcement Learning
Machine Learning Model Development Services
We build ml model development services the way production engineers build infrastructure. Every engagement starts with a feasibility audit, a data readiness check, and a clear architecture before a single line of training code is written. Our machine learning application development services cover the full lifecycle, from feature engineering and model selection to neural network development, training, and validation
We build neural network systems on TensorFlow and PyTorch for problems classical ML cannot solve. CNNs power our vision builds, RNNs and transformers run our NLP and signal pipelines, and every architecture is sized to the production budget you actually have. CNN, RNN, and transformer architectures for vision, language, and signal work GPU-accelerated training and inference on CUDA, with ONNX portability
Reinforcement learning earns its place when decisions stack and outcomes arrive late. We design the reward function, build the simulation environment, and train the agent on policy-gradient or value-based methods, then validate against real operational data before any live rollout. Reward design and simulation environments for robotics, recommendations, and scheduling Policy-gradient, Q-learning, and actor-critic training loops with offline validation
Chatbots that compound, not collapse, under real load.
The difference between a demo bot and a production system shows up at month three. Let us scope yours before that line.
- HIPAA Patient Intake BotsClinical Triage AssistantsMedical Records RetrievalProvider Scheduling Agents
Industry-fit chatbot builds, not generic deployments.
Eight regulated industries, eight production stacks, eight different compliance footprints. Healthcare runs under signed BAAs and HIPAA controls. FinTech runs against SOC 2 and PCI scope. EdTech runs against FERPA and GDPR. Each industry below is anchored to a chatbot we already shipped to production and still operate today.
Build a HIPAA-compliant, agentic, or omnichannel chatbot. Let's scope it.
A full compliance stack, not a checkbox afterthought.
Self-hosted deployment, on-premise options, HIPAA, SOC 2, GDPR, CCPA, and PIPEDA coverage are not add-ons. They are architectural decisions made on day one. Diesel Laptops runs inside its own AWS VPC. SmartMedHx is HIPAA-compliant with patent-pending AI and a signed BAA across 42 healthcare providers. If data sovereignty matters, it is already in the architecture.
Why production-grade chatbot teams choose Kodexo Labs.
Most AI work never makes it past the prototype. The pilot gets approved, then dies between proof of concept and production. Kodexo Labs ships systems that survive contact with real data, real compliance requirements, and real users at scale. 51 products shipped, 25+ industries, 94% client retention, $1M+ in documented annual client savings.
Compliance-First Architecture
Compliance is the architecture, not the wrapper. HIPAA, SOC 2 Type II, GDPR, CCPA, and PIPEDA all sit inside the same stack. BAAs are signed before code is written. We enforce prompt injection mitigation at every model boundary, with AWS VPC self-hosting available from day one.
LangGraph + CrewAI + FAISS Stack
Stateful orchestration with LangGraph, role-based agents through CrewAI, and FAISS for vector retrieval ship together as one production pattern. Few teams operate this stack in production. We ship agentic ai chatbot development on it as standard, with PIPEDA-ready clustering for Canadian deployments.
Discovery Sprint Methodology
A two-week scoped discovery sprint runs before any production build. Enterprise chatbot integrations are mapped in Phase 0 against your CRM, ERP, and identity layer. Compliance is scoped in the same sprint, not bolted on at QA. That is how an ai chatbot development company protects timelines once builds begin.
PhD-Level AI Team
Syed Umaid Ahmed, PhD Scholar at FAST-NUCES and Microsoft Certified AI, leads the technical bench. The same depth applies across our enterprise ai chatbot development services practice, from architecture review through model selection.
Track Record
Over $1M in documented annual client savings. 94% client retention. Vital Connect catches conditions three times earlier and reaches a diagnosis 40% faster on signal-monitoring infrastructure we built and now operate.
The exact technologies behind every AI chatbot development build.
Every stack decision below is chosen for compliance posture, retrieval accuracy, or production reliability — never trend.
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Eight-phase delivery roadmap. Discovery to LLMOps.
Process governs every chatbot engagement we run, from first scoping call through post-launch model monitoring.
Discovery Sprint (2 weeks)
Scope definition, integration mapping, and compliance requirement assessment covering BAA, PHI, GDPR, and PIPEDA. The deliverable is a Discovery Sprint document that defines architecture, timeline, and budget before any code is written.
Architecture Design
LLM selection across GPT-4o, Anthropic Claude, LLaMA 3, and Mistral against the use case. Vector store selection across FAISS, Pinecone, Weaviate, and Qdrant. RAG pipeline shape, context window strategy, and fallback paths are locked here.
Conversational AI Development
Conversational ai development covers intent design, entity mapping, and dialogue-flow architecture. NLP model configuration tunes recognition accuracy against real client utterances, not generic benchmarks, so production behaviour matches expectations from day one.
Agent Orchestration
For agentic scope, LangGraph or CrewAI handle multi-step tool-call orchestration, reasoning loops, and memory. Agentic ai chatbot builds at this stage power SmartMedHx provider-intake automation and IFPG franchise-lead reasoning across 1,000+ listings.
Integration
CRM and ERP integration runs through a webhook and API layer over Salesforce, HubSpot, Zendesk, and custom enterprise systems. WhatsApp Business API ships where the audience lives on messaging rather than web chat surfaces.
Related insights on AI chatbot development.
Field notes from the Kodexo Labs research desk on enterprise chatbot architecture.
AI chatbot development — the questions teams ask before they build.
Timeline and scope drive cost. Basic customer service bots scope in 3 to 6 weeks. Enterprise builds with agentic orchestration and compliance run 8 to 16 weeks. The 2-week Discovery Sprint scopes timeline and budget before any commitment, so AI chatbot development pricing is fixed against a real architecture, not a guess.
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