LLM App Architecture
Model selection, prompt design, context window, retrieval and the architecture on paper.
AI Development
LLM Application DevelopmentApps Built on Top of Language Models.
LLM application development for products that need language models at the core — the model selection, the prompt and context engineering, the retrieval, the evaluation suite, the guardrails and the cost controls, shipped as a production app with the engineering rigour the use case needs.
10+Years Experience
100+Projects Delivered
50+Expert Developers
20+Industries Served
LLM App Architecture
RAG & Knowledge AI
Fine-Tuning & Adapters
Agentic Workflows
Evaluation & Guardrails
Production & Operate
Overview
An LLM app is an app that calls a model, with the right guardrails
An LLM app is an application that uses a large language model as one component. The model is not the app. The app is the data pipeline, the retrieval, the prompt and context engineering, the evaluation, the guardrails, the UX and the operations. The model is the engine, not the vehicle.
We build LLM apps with the model selection, the prompt and context engineering, the retrieval, the evaluation suite, the guardrails and the cost controls as part of the architecture from sprint one. The output is a product that ships to production, not a prompt that works once and then breaks.
This is the wrong engagement if the use case does not need a language model. A traditional ML model, a rules engine or a SaaS product is often the right answer for that. We will say so on the call.
- Production-Grade — Built to ship, with the evaluations, the guardrails and the observability in place.
- 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 llm application development
RAG & Knowledge AI
Retrieval-augmented generation against the data the business owns, with the eval suite.
Fine-Tuning & Adapters
Fine-tuning or LoRA adapters against the data, with the eval that proves it worked.
Agentic Workflows
LLM agents with tool use, memory and the orchestration the use case needs.
Evaluation & Guardrails
An evaluation suite, the safety filters, the brand controls and the moderation.
Production & Operate
The production deploy, the observability, the cost review and the model swap path.
Our process
A proven process for successful delivery
01
Discover
We agree the use case, the data, the model and the architecture on paper.
02
Plan & Design
We design the system, the prompts, the context and the evaluation suite.
03
Develop
We build in two-week sprints with a working slice every Friday.
04
Deploy
We ship to production with the evaluations, the guardrails 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
Models
- OpenAI
- Anthropic
- Google Gemini
- Open source LLMs
Orchestration
- LangChain
- LlamaIndex
- Haystack
Vector & Data
- Pinecone
- Weaviate
- pgvector
- PostgreSQL
Observability
- LangSmith
- Helicone
- OpenTelemetry
What you can expect
- To First Production Release
- 6-12 wksTo First Production Release
- Target Uptime
- 99.9%Target Uptime
- 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.
Most production systems need RAG. Fine-tuning is for the format, the style or the domain vocabulary — not for the facts. The decision is made on the eval suite, not a guess. Most projects end up with RAG and a small amount of prompt work; a minority need both; very few need fine-tuning alone.
An evaluation suite is part of the architecture from sprint one. The suite includes a held-out test set, a rubric for the qualitative checks, and a CI gate that catches regressions before deploy. Every prompt change, every model swap, every fine-tune is run against the suite, and the result is in the deploy log.
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.
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