OpenAI Integration
OpenAI API integration — GPT, embeddings, the eval suite the product needs.
AI Products
OpenAI & LLM IntegrationWire the Model Into the Product.
OpenAI and LLM integration for products that need a language model inside — the model selection, the API integration, the prompt and context engineering, the eval suite, the cost controls and the swap path, shipped as production code with the engineering rigour the product needs.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
OpenAI Integration
Anthropic Integration
Google Gemini Integration
Open Source LLM Integration
Multi-Model Routing
Model Swap Path
Overview
LLM integration is engineering, not a prompt in a script
LLM integration is the work of putting a language model inside the product — the model selection, the API integration, the prompt and context engineering, the eval suite, the cost controls and the swap path. The prompt is one component, the integration is the product.
We build LLM integrations with the model selection, the API integration, the prompt and context engineering, the eval suite, the cost controls and the swap path as part of the architecture from sprint one. The output is an integration the product can rely on, not a prompt in a script that breaks on the second call.
This is the wrong engagement if the use case does not need a language model, or if a SaaS AI product already fits the use case. We will say so on the call.
- Engineering, Not Prompt — The model, the API, the prompt and the eval engineered, not improvised.
- Model-Agnostic — A model swap path, not a single-vendor lock-in. OpenAI, Anthropic, open source, your own.
- Cost-Defensible — Token budgets, caching, batching and the cost review that keeps the bill honest.
- Evaluated, Not Vibes — An evaluation suite that runs on every change, with the regressions caught early.
What we deliver
Everything included in our openai & llm integration
Anthropic Integration
Anthropic Claude integration — the model, the prompt and the eval the product needs.
Google Gemini Integration
Google Gemini integration — the model, the context and the eval the product needs.
Open Source LLM Integration
Open source LLM integration — Llama, Mistral, Qwen, the deployment the product needs.
Multi-Model Routing
Multi-model routing, the cost optimisation and the per-feature model the product needs.
Model Swap Path
A documented model swap path, so the next model is a config change, not a rewrite.
Our process
A proven process for successful delivery
01
Discover
We agree the use case, the model, the data and the architecture on paper.
02
Plan & Design
We design the integration, the prompt, the context and the eval suite.
03
Develop
We build the integration, the prompt and the eval in sprints.
04
Deploy
We ship to production with the eval, the cost controls and the observability live.
05
Optimize & Grow
We read the data, the cost and the evaluations, and ship the next iteration.
Technology
Built with a stack that stays maintainable
Providers
- OpenAI
- Anthropic
- Google Gemini
- OpenRouter
Open Source
- Llama
- Mistral
- Qwen
- DeepSeek
Orchestration
- LangChain
- LlamaIndex
- Custom pipelines
Observability
- LangSmith
- Helicone
- OpenTelemetry
What you can expect
- Typical Integration Build
- 4-8 wksTypical Integration Build
- API Latency Target
- <500msAPI Latency Target
- Evaluations on Every Change
- 100%Evaluations on Every Change
- Agnostic Architecture
- ModelAgnostic Architecture
Industries we serve
Delivered for teams in your sector
It depends on the use case, the data, the latency and the cost. We pick against the requirements, not the hype. The architecture is model-agnostic, so the next model swap is a configuration change, not a rewrite. For sensitive data, the model can run on your VPC or on your hardware.
AI development builds the AI. LLM integration puts the model inside the product the business already has. The integration is the work, the model is one component. The right answer depends on whether the AI is a new product or a feature of the system the business already runs.
Token budgets, caching, batching, model routing, and a cost review that keeps the bill honest. The cost is a non-functional requirement, not an afterthought. We design against the cost from the first sprint, and the dashboards show the per-request cost against the budget.
Yes, when the use case fits. Open source models (Llama, Mistral, Qwen) are a good fit for the use cases that do not need the frontier capability, and they can run on your infrastructure, with the deployment model the data posture requires. We will say so on the call.
Related services
More in AI Products
Business software
ERP, CRM, HRMS & custom software we build
Ready to start your openai & llm integration project?Let’s scope it together.
Tell us the outcome you need. We’ll come back with an approach, a timeline and a written estimate.