AI & Data
LLM Systems Built to Last
We engineer the system around the language model: retrieval, tools, evaluation and observability, so LLM applications behave predictably in production.
Overview
LLM Development
LLM development is an engineering problem more than a prompting problem. We design retrieval pipelines that find the right context, tool-calling layers that let models take bounded actions, and orchestration that handles retries, timeouts and failures. Choices about chunking, embeddings and model routing are made on measured results, not habit. The goal is a system that is predictable, observable and affordable to run. We document every design choice for your team. That includes how prompts, models and data sources were chosen and tested.
We help you decide when a hosted model is enough, when a smaller open model makes sense for cost or privacy, and when fine-tuning is actually justified. Every LLM system we build includes tracing, evaluation datasets and cost monitoring, so you can see what it did and why. That visibility makes ongoing improvement practical.
Context
Problems we solve
- 01
Poor retrieval quality
Answers are only as good as the context retrieved. We tune chunking, embeddings and ranking against real questions.
- 02
Fragile prompts
Small model updates can break prompt behavior. We version prompts and run regression tests on every change.
- 03
Unsafe tool use
Models that can act need strict limits. We scope permissions, validate inputs and require approval for risky steps.
- 04
Fine-tune or not
Fine-tuning is often unnecessary and costly. We test simpler options first and recommend it only when evidence supports it.
Capabilities
What we deliver
- 01
Retrieval-augmented generation
We build RAG pipelines with document ingestion, hybrid search, reranking and source citation.
- 02
Agents and tool calling
We build workflows where models call your APIs within defined boundaries and audit trails.
- 03
Model selection and routing
We compare hosted and open models, and route tasks to the cheapest model that meets quality needs.
- 04
Fine-tuning and adaptation
Where justified, we prepare datasets and fine-tune models, then compare results against a baseline.
- 05
LLM evaluation
We build test sets and scoring methods, including human review, to track quality over time.
- 06
Observability and cost control
We add tracing, token accounting, caching and rate limits so behavior and spend are visible.
Where it fits
- Internal knowledge assistants with citations
- Contract and document analysis tools
- Agents that update CRM or ticket records
- Structured data extraction from text
- Self-hosted models for private data
Technologies we use
- OpenAI API
- Anthropic Claude
- Llama
- LangChain
- LlamaIndex
- Pinecone
- pgvector
- vLLM
Approach
How we deliver
- 1
Discover
We learn your goals, users, constraints and existing systems before proposing anything.
- 2
Plan
Scope, priorities, architecture and milestones are agreed so everyone knows what ships when.
- 3
Design
User flows, interfaces and technical design are shaped and reviewed with you early.
- 4
Build
Work is delivered in short iterations with regular demos, so progress is always visible.
- 5
Test
Functional, integration and performance checks run throughout, not only at the end.
- 6
Launch & support
We deploy, monitor and keep improving the product after it goes live.
FAQs
Common questions
Have a question that is not covered here? Get in touch.
Should we fine-tune a model or use RAG?
RAG is usually the first choice when the goal is answering from your own documents, because it is easier to update and audit. Fine-tuning helps with style, format or specialized tasks, and we test it against a baseline before recommending it.
Can an LLM run entirely on our own infrastructure?
Yes, using open-weight models hosted in your cloud or data center. The trade-offs are hardware cost, operational effort and sometimes lower quality than the largest hosted models, which we measure for your task.
How do you test an LLM application?
We build a set of representative questions and expected behaviors, score outputs automatically where possible and review samples by hand. We rerun these tests whenever the prompt, model or data changes.
How is this different from generative AI development?
Generative AI work centers on user-facing features and content. LLM development covers the underlying architecture, such as retrieval, agents, model choice and evaluation, that makes those features dependable.
Explore
Keep exploring
Let's build what's next
Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.