AI, Data & Emerging Technology · LLM development
Large language model apps that hold up in production
We build applications on large language models, from retrieval-augmented search to fine-tuned classifiers and structured extraction, with the evaluation, monitoring and cost controls that turn a promising demo into dependable software.
- Retrieval-augmented generation
- Fine-tuning and adapters
- Structured output
Overview
Engineering choices behind a reliable LLM application
Large language model applications are built from a few moving parts: the model, the instructions, the data retrieved for each request, the code that validates output, and the tests that tell you whether a change helped or hurt. Each part can be swapped independently, and most quality gains come from retrieval and validation rather than from changing models. That keeps each improvement measurable.
Buyers face real trade-offs. Hosted frontier models are strongest out of the box but send data to a provider and charge per token. Open-weight models can run in your own cloud with more control but need GPU capacity and tuning. Smaller models are fast and inexpensive for narrow tasks. We benchmark candidates on your data before recommending one. The winner is often not the largest model.
Production readiness means typed outputs, retries, timeouts, tracing on every call and an evaluation suite that runs before each release. Prompts and settings live in version control like any other code, so every change can be reviewed, compared and rolled back. Without those basics, quality quietly drifts as models and data change underneath.
Who it’s for
Built for teams like yours
- 01
Product and platform teams
Engineering teams adding LLM features to an existing product who want a clean architecture, provider flexibility and tests, rather than prompt strings scattered through the codebase.
- 02
Data-sensitive organizations
Companies with strict data rules that need models running inside their own cloud account or network, with controls their security and legal advisers have signed off on.
- 03
Teams past the prototype
Groups that already have a promising proof of concept but are fighting inconsistent answers, rising costs or slow responses as real users arrive. We stabilize what exists rather than starting over.
Why it matters
The demo is the easy part
A prompt that works in a playground often fails on real documents, odd phrasing or a model update. Production LLM work is mostly engineering: choosing the right model for the task, designing retrieval that finds the right passage, forcing structured output, and measuring quality so changes are improvements rather than guesses.
We treat prompts, datasets and evaluations as code: versioned, tested and owned by you, so the system keeps working as models, prices and providers change underneath it over the coming years.
Every engagement includes
- Feasibility spikea quick test on your real data to confirm the approach before full build.
- Model comparisonaccuracy, latency and cost measured across candidate models for your task.
- Data preparationcleaning, labeling and splitting the examples used for retrieval, tuning and testing.
- Application buildAPIs, interfaces and integrations around the model, not just the prompt.
- Monitoring setuptracing, cost dashboards and alerts for errors, drift and unusual usage.
- Documentation & handoverprompts, datasets, evals and infrastructure handed over in your accounts.
Features
Core LLM engineering
- 01
Retrieval-augmented generation
Chunking, embeddings, hybrid search and re-ranking tuned so the model sees the right context.
- 02
Fine-tuning and adapters
Supervised fine-tuning or LoRA adapters when prompting alone cannot reach the accuracy or tone required.
- 03
Structured output
JSON schemas, function calling and validation so extracted fields drop cleanly into your systems.
- 04
Model-agnostic design
An abstraction layer that lets you switch between hosted APIs and open models without a rewrite.
- 05
Offline and online evals
Golden datasets, model-graded checks and production sampling that track quality over time.
- 06
Safety filters
Prompt-injection defenses, PII redaction and content filters placed before and after the model.
In practice
Problems LLMs handle well
Long-document question answering
Users ask questions across contracts, technical manuals or research files, and the system returns grounded answers with citations to specific pages, using retrieval tuned for long and structured documents. Answers say so when the documents do not cover a question.
Structured extraction at volume
Thousands of emails, notes or reports are converted into validated JSON records that match your schema, with confidence flags and automatic retries when output fails validation. Records that still fail are queued for a person to review.
Classification and routing
Free-text inputs such as feedback, claims or requests are labeled against your own taxonomy, with a smaller tuned model often handling the volume at lower cost than a large one.
Writing in your house style
A model adapted on your approved examples drafts descriptions, summaries or responses in your terminology and format, reducing how much editing each draft needs. Approved edits can be folded into later training rounds.
Process
How we work
- 1
Baseline benchmark
We assemble a test set from your real inputs, run several candidate models and prompt approaches against it, and record accuracy, latency and token cost for each one. This becomes the yardstick for every later change.
- 2
Retrieval design
Where grounding is needed, we test chunking strategies, embedding models, hybrid keyword search and reranking until the right passages reliably reach the model. Each change is scored on whether the right passages were actually retrieved for known questions.
- 3
Tuning decision
If prompting and retrieval plateau, we prepare training examples and fine-tune or train adapters, comparing the tuned model against the baseline on the same test set. Tuning is skipped when it does not clearly pay off.
- 4
Service hardening
The pipeline gets schema validation, fallbacks between providers, caching, rate limiting and tracing, packaged as an API your other systems can call. Timeouts and error handling are tested under realistic load before anything goes live.
- 5
Release checks
Every change to prompts, models or retrieval runs the evaluation suite in CI, and results are compared with the previous release before deployment is allowed. Regressions block the release until they are explained or fixed.
Deliverables
What you receive
- Model benchmark report on your data
- Retrieval pipeline and vector index
- Versioned prompt and configuration library
- Fine-tuned model or adapters, if warranted
- Production API with typed outputs
- CI evaluation suite and score history
- Tracing and cost monitoring setup
Tools & methods
Models & serving
- Claude
- GPT models
- Llama
- Mistral
- vLLM
- Amazon Bedrock
Libraries
- Python
- PyTorch
- Hugging Face Transformers
- LlamaIndex
- DSPy
Evaluation & ops
- Langfuse
- promptfoo
- Weights & Biases
- GitHub Actions
- Kubernetes
FAQ
Frequently asked questions
Anything else about LLM development? Ask us directly.
They solve different problems. Retrieval gives a model access to facts that change, like policies or product data, and lets it cite sources. Fine-tuning teaches style, format or a narrow skill such as classification. Many systems use both. We test on your data during the feasibility spike and recommend whichever gives the best accuracy for the cost.
Let’s work together
Have a project in mind?
Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.