Generative AI applications that are grounded, tested and affordable to run.
We build on large language models (LLMs) for drafting, summarization, extraction and reasoning tasks — choosing the right model, grounding it in your sources, and measuring quality and cost before anything reaches users.
- Use-case and output specification
- Model selection report
- Grounding and prompt layer
- Evaluation harness
- Production service
Where this helps
What we deliver
How it works
- 01
Define the task
We collect real examples of the inputs and the outputs your experts consider good, and agree how quality will be judged.
- 02
Compare approaches
Several models and prompting strategies are tested against the examples. Where retrieval or fine-tuning is needed, we say so with evidence.
- 03
Build the application
We implement the prompt, grounding and output handling inside a service that fits your architecture and security requirements.
- 04
Evaluate and red-team
The system is tested on edge cases, adversarial inputs and known failure modes until it meets the agreed bar.
- 05
Launch and tune
Release to a limited group, collect feedback and review logs, then adjust prompts, routing and caching to improve quality and cost.
Design decisions we make with you
Model and hosting
Hosted API, cloud provider endpoint or open-weight model in your infrastructure — chosen per task based on quality, data sensitivity and cost.
Prompting, retrieval or fine-tuning
Most tasks are solved with good prompting plus retrieval. We only recommend fine-tuning when tests show it is needed. See [model adaptation](/ai-services/model-adaptation).
Structured versus free output
Where output feeds another system, we constrain it to a schema and validate it, rather than parsing free text.
Cost controls
Model routing, prompt caching, output length limits and batching keep per-request cost predictable and visible.
Ownership and portability
You own the prompts, test sets and code. An abstraction over model providers keeps switching practical.
Applications
Related capabilities
- Enterprise Knowledge AssistantAnswer staff questions from your own policies, procedures and records, with sources shown and access rules respected.
- Document IntelligenceClassify, extract, check and route incoming documents, with people reviewing the exceptions.
- Sales & Marketing AssistantsResearch accounts, draft tailored outreach and review content against brand rules, with reps and marketers approving everything that goes out.
- RAG & Enterprise Knowledge SystemsAnswers drawn from your own documents and data, with sources shown, permissions respected and content kept current.
- Model Adaptation & Fine-tuningDecide when prompting, retrieval, fine-tuning or a custom model is justified — and do it with evaluation data that proves the choice.
- AI App DevelopmentComplete AI products — interface, model layer, application code and integrations — designed for the people who will use them every day.
- MLOps & LLMOpsEvaluation, release, monitoring and cost control for predictive models and LLM applications once they are in production.
Questions buyers ask
We ground answers in your source material, require citations where possible, constrain output formats, and test for unsupported claims. For high-stakes output, a person reviews before anything is sent or saved.
It depends on the task, your data sensitivity and budget. We test candidates on your own examples and recommend one, with a cheaper or more private alternative where it makes sense.
Often, yes — through private cloud endpoints or open-weight models in your environment. The tradeoffs are covered on our Enterprise & Private AI page.
Models and usage change over time. We monitor quality and cost, rerun evaluations when providers update models, and can hand operations to your team or run them under an agreed support scope.
Discuss this capability with an engineer.
Tell us about the workflow or product. We reply with questions, a suggested first step and who would work on it.