Generative AI & LLM Development
Build enterprise applications on large language models: copilots, knowledge assistants, and content tools that work with your data and fit how your teams operate.
Getting from an impressive demo to a dependable product.
A language model can produce a good answer in minutes. Making it accurate, safe, and useful every day takes engineering.
Answers that can’t be trusted
Generic models don’t know your business and can sound confident when they’re wrong.
Sensitive data concerns
Teams need clarity on what information reaches which model, and who can see the results.
Unpredictable costs and speed
Model choice, prompt design, and architecture all affect response time and running cost.
No way to measure quality
Without evaluation, it’s hard to know whether a change made the system better or worse.
The full stack of a production LLM application.
Use-case & model selection
Match each task to the right model, balancing quality, latency, cost, and data requirements.
Retrieval-augmented generation
Ground answers in your documents and systems, with references users can check.
Copilots & assistants
Design conversational and in-app experiences that help people complete real tasks.
Prompt & output engineering
Structure prompts, tools, and outputs so results are consistent and usable by other systems.
Evaluation & quality testing
Build test sets and automated checks to measure accuracy and catch regressions.
Security & access control
Respect user permissions, protect sensitive data, and log usage for review.
From idea to production LLM application.
Define
Agree the task, users, data sources, and how quality will be measured.
Learn more about DefinePrototype
Build a working version on representative data and evaluate the results.
Learn more about PrototypeProductionize
Add integrations, permissions, monitoring, and a user experience people adopt.
Learn more about ProductionizeImprove
Track quality and cost, update models, and refine based on real usage.
Learn more about Improve
Common starting points.
Questions about generative AI development?
Which language model will you use?
It depends on the task, data sensitivity, performance needs, and budget. We compare options during the proof of concept and design so models can be changed later.
How do you reduce incorrect answers?
By grounding responses in approved sources, constraining outputs, showing references, and testing against evaluation sets before and after launch.
Will our data be used to train public models?
We agree data handling requirements with you up front and choose providers and configurations that meet them.
Do we need to fine-tune a model?
Often not. Most business applications are well served by retrieval and good engineering. Fine-tuning is considered when testing shows a clear benefit.