Product
AI Chatbots & Assistants
Domain-specific chatbots powered by GPT-4 with RAG retrieval from your knowledge base — accurate, on-brand, and hallucination-controlled.
OpenAI / Claude Integration — Production LLM Applications That Actually Work
We integrate OpenAI and GPT-4 into production applications — building RAG chatbots, AI assistants, function calling workflows, and streaming interfaces that solve real business problems at scale.
20+ OpenAI integrations in production | GPT-4, Function Calling, RAG, Streaming
- Projects delivered
- 0+
- Clients worldwide
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- Client satisfaction
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Projects delivered
Clients worldwide
Client satisfaction
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WHY CODEFLAMME
- Production-grade architecture from day one
- TypeScript, testing, and accessibility built in
- Dedicated engineers — no freelancers or hand-offs
- NDA protected | reply within 24 hours
RESPONSE TIME
< 24h
We reply to every inquiry within one business day with a structured plan.
What We Build
What We Build With OpenAI / Claude Integration
Production applications across product types — scoped to your users, stack, and growth stage.
6 product types — compare what we ship with OpenAI / Claude in production.
- Platform
RAG (Retrieval-Augmented Generation)
GPT-4 connected to your documents, database, or knowledge base — answers grounded in your actual data, not hallucinated.
- SaaS
Document Intelligence
GPT-4-powered document analysis, summarisation, data extraction, and classification for high-volume document processing.
- Commerce
Function Calling & Tool Use
GPT-4 function calling workflows connecting the LLM to your APIs, databases, and business logic for agentic task completion.
- Internal
AI-Powered Search
Semantic search replacing keyword search — users find relevant results using natural language queries against your content.
- Design
Content Generation Workflows
Automated content generation pipelines — product descriptions, report drafts, email responses — with human review workflows.
WHY CODEFLAMME
Not Another Offshore Vendor
We know the hesitation. Here's exactly how we're different.
You Talk to the People Building It
No account managers relaying messages. You're in direct contact with the founder and senior engineers on your project — every sprint, every decision.
No Bench Rotation
The team that scopes your project is the team that ships it. We don't swap engineers mid-project to free them up for someone else.
NDA Before We Talk Details
Your idea and IP are protected from the first real conversation — not after contracts are signed.
Full IP, Zero Strings
Every line of code, every design file, transfers to you on delivery. No licensing, no retained rights, no surprises.
Our Capabilities
Our OpenAI / Claude Integration Development Services
Core delivery areas for OpenAI / Claude — architecture, implementation, and production hardening.
- 01
OpenAI API Architecture
Proper API client setup, token counting, rate limit handling, retry logic, and cost monitoring for production OpenAI usage.
- 02
System Prompt Engineering
Structured system prompts with persona definition, context injection, format constraints, and safety guardrails for reliable outputs.
- 03
RAG Pipeline Design
Chunking strategy, embedding model selection, vector store integration, retrieval logic, and context window management for RAG systems.
- 04
Streaming Responses
Server-sent events and WebSocket streaming for real-time token-by-token response delivery in chat interfaces.
- 05
Function Calling Implementation
Tool definition, parallel function calling, result injection, and multi-step agentic workflows with proper error handling.
- 06
Evaluation & Testing
LLM output evaluation frameworks, golden dataset testing, hallucination detection, and quality monitoring in production.
When to Choose
When OpenAI / Claude Integration Is the Right Choice
Decision scenarios where OpenAI / Claude Integration is the strongest fit — and why it earns the recommendation.
SCENARIO 01
Primary use case
You need an AI chatbot trained on your content
GPT-4 with RAG is the current best approach — accurate responses from your specific knowledge base without fine-tuning costs.
02
Scenario
You need document intelligence at scale
GPT-4's context window and understanding make it far superior to regex or classical NLP for extracting structured data from unstructured documents.
03
Scenario
You want to automate a text-based workflow
If humans are currently reading, categorising, or responding to text at scale, GPT-4 can automate 80%+ of that work with properly designed prompts.
04
Scenario
You need AI features fast
OpenAI's API allows AI features to be shipped in weeks rather than the months required to train custom models.
In Practice
How We Use OpenAI & Claude on Real Builds
LLM API integrations we ship, when a managed model is enough vs a full orchestration layer, and backend pairings.
Most OpenAI and Claude work we deliver is product-facing: support chat over a private knowledge base, document extraction into structured fields, and draft-generation inside existing SaaS workflows. We wrap providers behind our own API layer for keys, rate limits, logging, and evals — not browser-side calls.
A direct API integration is enough for single-step prompts. LangChain or LangGraph enters when retrieval, tools, or multi-step agents need structure. Python/Django or FastAPI backends are the usual host; Node appears when the product API is already JavaScript.
Concrete deliveries include ticket-triage assistants that draft replies from past cases, invoice and ID parsers that write into CRM fields, and in-app summarisers for long threads or contracts. Prompts and model choice live in config so product code stays stable when providers change. React dashboards or mobile clients call our backend only; streaming uses SSE or WebSockets already in the stack. Cost and latency budgets are set per feature, not as one global model default. We also keep a thin eval harness — golden prompts and expected shapes — so regressions surface before a model or prompt swap reaches production.
- 01
LangChain
When RAG pipelines or tool-using agents grow past a few prompts, we move orchestration into LangChain/LangGraph with tracing.
- 02
Python / Django
LLM endpoints, queues, and document pipelines commonly live in Django or FastAPI services next to the product database.
- 03
AI Development
Provider integrations are scoped inside our AI delivery practice — evaluation, cost controls, and production monitoring included.
Complementary Stack
The Stack We Use Alongside OpenAI / Claude Integration
The tools we pair with OpenAI / Claude Integration in production — organised by layer, not hype.
LAYERS
09
TOOLS
47
01
STACK_LAYER
OpenAI
02
STACK_LAYER
Anthropic / Claude
03
STACK_LAYER
xAI / Grok
04
STACK_LAYER
Google / Gemini
05
STACK_LAYER
Frameworks
09
STACK_LAYER
Monitoring
FAQ
Frequently Asked Questions
Can't find what you need? Talk directly with our team.
Book a Discovery CallThrough RAG, strong system prompts with explicit constraints, and output validation. No LLM is 100% hallucination-free — we design systems that detect and flag low-confidence responses.
Ready to build something powerful?
Tell us what you are building. We will respond within 24 hours with a clear, honest assessment — no pressure, no sales pitch.
NDA protected · Reply within 24 hours · No commitment required